From 8d063c8691808d152c7bb3d1881ab3e3817f0646 Mon Sep 17 00:00:00 2001 From: Michael Benavidez Date: Fri, 26 Jun 2026 15:16:07 -0500 Subject: [PATCH] feature: merge to single-page doc and add llms.txt --- .../MI3XX/_static/css/custom.css | 44 +- reference-architecture/MI3XX/conf.py | 10 +- reference-architecture/MI3XX/index.rst | 643 +++++++++++++++++- .../MI3XX/legal-information.rst | 40 -- reference-architecture/MI3XX/llms.txt | 19 + reference-architecture/MI3XX/overview.rst | 599 ---------------- .../MI3XX/sphinx/_toc.yml.in | 6 +- 7 files changed, 684 insertions(+), 677 deletions(-) delete mode 100644 reference-architecture/MI3XX/legal-information.rst create mode 100644 reference-architecture/MI3XX/llms.txt delete mode 100644 reference-architecture/MI3XX/overview.rst diff --git a/reference-architecture/MI3XX/_static/css/custom.css b/reference-architecture/MI3XX/_static/css/custom.css index e7d3b4a..bcbccbd 100644 --- a/reference-architecture/MI3XX/_static/css/custom.css +++ b/reference-architecture/MI3XX/_static/css/custom.css @@ -1,31 +1,7 @@ -/* Network troubleshooting page */ - -/* Custom width for rccl errors table */ -table.rccl-errors { - table-layout: fixed !important; - width: 100% !important; -} -table.rccl-errors th:nth-child(1), -table.rccl-errors td:nth-child(1) { - width: 40% !important; -} -table.rccl-errors th:nth-child(2), -table.rccl-errors td:nth-child(2) { - width: 60% !important; -} - -/* Custom width for rdma errors table */ -table.rdma-errors { - table-layout: fixed !important; - width: 100% !important; -} -table.rdma-errors th:nth-child(1), -table.rdma-errors td:nth-child(1) { - width: 40% !important; -} -table.rdma-errors th:nth-child(2), -table.rdma-errors td:nth-child(2) { - width: 60% !important; +/* Hide the primary sidebar toggle (carat) only when the primary sidebar is + empty. */ +.bd-container__inner:has(.bd-sidebar-primary.hide-on-wide) .sidebar-toggle.primary-toggle { + display: none; } /* Remove white background from topology diagrams in dark mode */ @@ -43,3 +19,15 @@ html:not([data-theme=dark]) .bd-content img { background-color: #777 !important; } +/* Shrink the Legal information section heading */ + #legal-information > h2 { + font-size: 1rem; + } + + /* Render the Legal information body text smaller (see .. container:: legal-text + in index.rst) */ +.legal-text { + font-size: 0.65rem; + line-height: 1.4; +} + diff --git a/reference-architecture/MI3XX/conf.py b/reference-architecture/MI3XX/conf.py index 2bf69d3..a69cb94 100644 --- a/reference-architecture/MI3XX/conf.py +++ b/reference-architecture/MI3XX/conf.py @@ -8,7 +8,6 @@ if os.environ.get("READTHEDOCS", "") == "True": html_context["READTHEDOCS"] = True -project = "AMD Instinct Hub" html_title = "AMD Instinct MI3XX Reference Design" author = "Advanced Micro Devices, Inc." copyright = "Copyright (c) 2026 Advanced Micro Devices, Inc. All rights reserved." @@ -19,19 +18,26 @@ external_toc_path = "./sphinx/_toc.yml" external_projects_current_project = "MI3XX-reference" +external_projects_remote_repository = "" html_theme = "rocm_docs_theme" html_theme_options = { "flavor": "instinct", - "show_toc_level": 1, + "show_toc_level": 2, "navbar_align": "content", "link_main_doc": True, "use_download_button": True, } extensions = ["rocm_docs"] +# Configure static files path html_static_path = ['_static'] +# Disable sidebars to handle minimal TOC +html_sidebars = { + "**": [] +} + html_extra_path = ["llms.txt"] EXCLUDED_DIRS = { diff --git a/reference-architecture/MI3XX/index.rst b/reference-architecture/MI3XX/index.rst index 08f5888..6120232 100644 --- a/reference-architecture/MI3XX/index.rst +++ b/reference-architecture/MI3XX/index.rst @@ -1,5 +1,642 @@ -:orphan: +.. meta:: + :description: Reference materials for cluster and associated network builds + :keywords: network validation, cluster, cluster design, cluster architecture, cluster network -.. raw:: html +************************************************************************************************************************ +AMD Instinct MI3XX Reference Design +************************************************************************************************************************ + +This document provides a common reference for designing GPU cluster networks using AMD Instinct MI300X, MI325X, MI350X, +and MI355X series accelerators, supporting up to 8192 GPUs. It covers fundamental cluster design principles, network +topologies, scalable architectures, and bill of materials for large-scale deployments. Also included are practical +examples, diagrams, and recommendations for both fat tree and rail network designs, as well as guidance on scaling, +hardware selection, and best practices for high-performance AI/ML workloads. The audience for this content encompasses +architects, engineers, and IT professionals. + +Common cluster design principles +======================================================================================================================== + +Fat tree network topologies +------------------------------------------------------------------------------------------------------------------------ + +The canonical fat tree topology is a network concept where a switch's connection to upstream peers has at least parity +bandwidth with the total aggregate bandwidth of its downstream connections. This causes links between switches to become +"fatter" as they get closer to the core. + +The "fat tree" topology for AI/ML clusters instead refers to how a host is connected to its upstream switches; in this +case all host NICs terminate on the same switch. It can also be considered 1-rail network. The network itself is +generally a 3-stage or 5-stage folded Clos network due to the fixed radix of network switches. + +Rail network topologies +------------------------------------------------------------------------------------------------------------------------ + +Rail networks leverage the same folded Clos network as tree networks, but host connections are instead aggregated onto +switches based on NIC rank. These shared ranks are referred to as rails and allow the network to provide preferential +latency for connections which share the same rail. The downside to this design is any traffic which needs to cross +rails/ranks must traverse either the network spine layer, or Infinity Fabric (PXN). + +Comparison between fat tree and rail networks +------------------------------------------------------------------------------------------------------------------------ + +Rail networks can provide better latency for traffic within the same rail, enabling larger single hop ring domains. +However, traffic that needs to cross rails can experience higher latency, which can be a bottleneck in large clusters +with high cross-rail traffic. + +.. image:: ./data/basic-network-topology-design-examples/rail-network-traversals.png + :alt: Example of benefits and limitations of rail network traversals + +Fat tree networks handle cross-rank traffic better, but may have higher latency for traffic that could have been +contained within a single rail in a rail network. + +.. image:: ./data/basic-network-topology-design-examples/cross-rank-traffic-tree.png + :alt: Example of benefits and limitations of fat tree network traversals + +The choice between the two often depends on the specific workload and communication patterns of the applications being +run on the cluster. + +Basic network topologies +======================================================================================================================== + +The following sections describe basic layouts for rail, tree, and hybrid network topologies that can be used as building +blocks for larger cluster designs. These layouts are not exhaustive, but provide a starting point for understanding the +trade-offs between different network architectures. + +2-tier rail network +------------------------------------------------------------------------------------------------------------------------ + +The 2-tier rail network design enables large, scalable unit sizes suitable for large jobs or replica sizes, offering +efficiency for workloads that utilize ring-based collectives, though it also results in higher infrastructure costs due +to the need for additional networking hardware. + +.. image:: ./data/basic-network-topology-design-examples/2-tier-rail-network.png + :alt: 2-tier rail network diagram + +2-tier tree network +------------------------------------------------------------------------------------------------------------------------ + +The 2-tier tree network design is efficient for small workloads or replicas and can easily scale by adding capacity with +proper planning. It also has the potential to reduce overall infrastructure costs, while its design helps limit the +blast radius compared to rail networks. + +.. image:: ./data/basic-network-topology-design-examples/2-tier-tree-network.png + :alt: 2-tier tree network diagram + +3-tier rail TH5/J3 network +------------------------------------------------------------------------------------------------------------------------ + +In the 3-tier rail TH5/J3 network design, spine switches are replaced with a two-tier Jericho3-AI/Ramon3 fabric to +enable a larger maximum cluster size, where deeper buffers and scheduled fabric help alleviate congestion in large +clusters with only a small latency trade-off. + +3-tier tree TH5/J3 network +------------------------------------------------------------------------------------------------------------------------ + +The 3-tier tree TH5/J3 network design provides all the same benefits from switching to a scheduled spine fabric as with +rail, but retains the primary characteristics of tree networks. + +3-tier rail optimized network +------------------------------------------------------------------------------------------------------------------------ + +The 3-tier rail optimized network design allows for massive scalable unit sizes and delivers the best ring-based +collective performance at scale, though this comes with the trade-off of weaker any-to-any communication performance. + +.. image:: ./data/basic-network-topology-design-examples/3-tier-rail-optimized-network.png + :alt: 3-tier rail optimized network diagram + +3-tier tree network +------------------------------------------------------------------------------------------------------------------------ + +The 3-tier tree network design allows for massive cluster sizes and delivers excellent any-to-any performance at scale, +making it well-suited for large deployments that need strong, predictable connectivity. This architecture is +particularly effective for campus-style environments, where broad distribution and high performance are both required. + +.. image:: ./data/basic-network-topology-design-examples/3-tier-tree-network.png + :alt: 3-tier tree optimized network diagram + +3-tier hybrid rail network +------------------------------------------------------------------------------------------------------------------------ + +The 3-tier hybrid rail network design allows for massive cluster sizes with large scalable units, favoring ring-based +collectives while still maintaining solid any-to-any performance for large jobs. These characteristics also make it +well-suited for campus-style deployments that balance scalability with broad connectivity requirements. + +.. image:: ./data/basic-network-topology-design-examples/3-tier-hybrid-rail-network.png + :alt: 3-tier hybrid rail network diagram + +3-tier fully scheduled rail network +------------------------------------------------------------------------------------------------------------------------ + +The 3-Tier fully scheduled rail network designuses medium-sized scalable units and delivers excellent congestion +performance thanks to deep buffers and scheduled fabric, though technical limitations restrict the recommended cluster +size to roughly 32,000 GPUs. + +.. image:: ./data/basic-network-topology-design-examples/3-tier-fully-scheduled-rail-network.png + :alt: 3-tier fully scheduled rail network diagram + +Scaling networks +======================================================================================================================== + +As cluster size increases, the network must be scaled to accommodate the additional bandwidth and connectivity +requirements. For a 2-tier tree network, spine switches do not need to be added until a second scalable unit is deployed +as all rail/rank traffic occurs at the unit-level. In a 2-tier rail network, spine switches are needed at deployment to +connect rails at any scalable unit number. + +.. image:: ./data/basic-network-topology-design-examples/2-tier-network-backend-scaling.png + :alt: Example of network backend scaling for 2-tier network design + +In a 3-tier network, a tree design does not require a super spine until a super-scalable unit is deployed. + +.. image:: ./data/basic-network-topology-design-examples/3-tier-network-backend-scaling.png + :alt: Example of network backend scaling for 3-tier network design + +This holds true for hybrid rail as well, where the super spine is only needed at super-scalable unit deployments, but a +fully scheduled rail network requires a super spine from the initial deployment. + +.. image:: ./data/basic-network-topology-design-examples/3-tier-network-backend-deploy-rail.png + :alt: Example of network backend scaling for 3-tier network design + +Network subscription +======================================================================================================================== + +Subscription is the relationship between what is provided by the upstream network and what is required by the downstream +network in demand side. + +It is typically represented as a ratio: + +.. math:: + + Downstream Demand : Upstream Capacity + +In a 1:1 subscribed network the downstream capacity is equal to the upstream capacity, while in 1:1.16 subscribed +network there is .16 more upstream capacity. + +This can also be represented as a percentage: + +.. math:: + + Subscription Rate = \frac{Downstream Demand}{Upstream Capacity} + +An 80% subscription ratio could be referred to as "20% undersubscribed", or a 120% subscription ratio could be referred +to as "20% oversubscribed". + +Hardware and software components +======================================================================================================================== + +128 to 1024 GPU generic BOM +------------------------------------------------------------------------------------------------------------------------ + +The following table provides a generic bill of materials (BOM) for cluster and network designs ranging from 128 to 1024 +GPUs. The actual components and quantities may vary based on specific design choices, vendor selection, and scalability +requirements. + +**Cluster** + ++------------------+-------------------------+ +| Cluster Size | 128 to 1024 GPU | ++==================+=========================+ +| Platforms | Dell XE9680 | +| | Lenovo SR685a V3 | +| | SMCI AS-8125GS | ++------------------+-------------------------+ +| OS | Ubuntu 22.04 (or above) | ++------------------+-------------------------+ +| Linux kernel | 5.15 - 6.80 | ++------------------+-------------------------+ +| ROCm | 6.33 (Or above) | ++------------------+-------------------------+ + ++------------------+-------------------------+ +| Storage Type | | ++==================+=========================+ +| Local storage | 1.6 TB (or greater) | ++------------------+-------------------------+ +| Utility storage | Pure, Vast, RYO | ++------------------+-------------------------+ +| Bulk storage | Vast, DDN, WekaIO | ++------------------+-------------------------+ +| Scratch storage | Vast, DDN, WekaIO, | +| | Hammerspace | ++------------------+-------------------------+ +| Archive/object | S3 compataible | +| storage | | ++------------------+-------------------------+ + +**Network** + ++---------------------------+-----------------------------------------------------+ +| Backside Network Topology | 2 Tier Rail Optimized / Fat Tree | ++===========================+=====================================================+ +| NIC | Pollara 400, BCM957608 (Thor2) | ++---------------------------+-----------------------------------------------------+ +|| Switch || Arista, Dell, Juniper, Cisco, Nokia | +|| || (TH 4/5, Jericho/Ramon) | ++---------------------------+-----------------------------------------------------+ +| Network OS | SONiC, Junos, EOS, IOS | ++---------------------------+-----------------------------------------------------+ +| Subscription ratio | 1:1.16=16% Undersubscribed (AMD recommended) | ++---------------------------+-----------------------------------------------------+ +| Optics | Vendor ACL/HCL transceivers or direct attach copper | ++---------------------------+-----------------------------------------------------+ +| Fabric | RoCEv2 Ethernet | ++---------------------------+-----------------------------------------------------+ + ++-----------------------------------------+----------------------------------------------+ +| Frontside Network Segement | Adapter Recommended | ++=========================================+==============================================+ +| All-in one network | Ethernet 100 GbE 2-port QSFP28 adapter | ++-----------------------------------------+----------------------------------------------+ +| Storage network (optional) | Ethernet 100 GbE 2-port QSFP28 adapter | ++-----------------------------------------+----------------------------------------------+ +| Virtualization network (optional) | Ethernet 100 GbE 2-port QSFP28 adapter | ++-----------------------------------------+----------------------------------------------+ +| Host in-band | Ethernet 10/25GbE 4-Port SFP28 adapter | ++-----------------------------------------+----------------------------------------------+ +| BMC OOB Mgt | 1G Copper | ++-----------------------------------------+----------------------------------------------+ + + +1024 to 8192 GPU generic BOM +------------------------------------------------------------------------------------------------------------------------ + +The following table provides a generic bill of materials for cluster and network designs ranging from 1024 to 8192 GPUs. +The actual components and quantities may vary based on specific design choices, vendor selection, and scalability +requirements. + +**Cluster** + ++------------------+-------------------------+ +| Cluster Size | 1024 to 8192 GPU | ++==================+=========================+ +| Platforms | Dell XE9680 | +| | Lenovo SR685a V3 | +| | SMCI AS-8125GS | ++------------------+-------------------------+ +| OS | Ubuntu 22.04 (or above) | ++------------------+-------------------------+ +| Linux kernel | 5.15 - 6.80 | ++------------------+-------------------------+ +| ROCm | 6.33 (Or above) | ++------------------+-------------------------+ + ++------------------+-------------------------+ +| Storage Type | | ++==================+=========================+ +| Local storage | 1.6 TB (or greater) | ++------------------+-------------------------+ +| Utility storage | Pure, Vast, RYO | ++------------------+-------------------------+ +| Bulk storage | Vast, DDN, WekaIO | ++------------------+-------------------------+ +| Scratch storage | Vast, DDN, WekaIO, | +| | Hammerspace | ++------------------+-------------------------+ +| Archive/object | S3 compataible | +| storage | | ++------------------+-------------------------+ + +**Network** + ++---------------------------+----------------------------------------------+ +| Backside Network Topology | 2 Tier Rail Optimized / Fat Tree | ++===========================+==============================================+ +| NIC | Pollara 400, BCM957608 (Thor2) | ++---------------------------+----------------------------------------------+ +| Switch | Arista, Dell, Juniper, Cisco, Nokia | +| | (TH 4/5, Scheduled Fabrics) | ++---------------------------+----------------------------------------------+ +| Network OS | SONiC, Junos, EOS, IOS, DriveNets | ++---------------------------+----------------------------------------------+ +| Subscription ratio | 1:1.16=16% Undersubscribed (AMD recommended) | ++---------------------------+----------------------------------------------+ +| Optics | Vendor ACL/HCL | ++---------------------------+----------------------------------------------+ +| Fabric | RoCEv2 Ethernet | ++---------------------------+----------------------------------------------+ + ++-----------------------------------------+----------------------------------------------+ +| Frontside Network Segement | Adapter Recommended | ++=========================================+==============================================+ +| All-in one network | Ethernet 100 GbE 2-port QSFP28 adapter | ++-----------------------------------------+----------------------------------------------+ +| Storage network (optional) | Ethernet 100 GbE 2-port QSFP28 adapter | ++-----------------------------------------+----------------------------------------------+ +| Virtualization network (optional) | Ethernet 100 GbE 2-port QSFP28 adapter | ++-----------------------------------------+----------------------------------------------+ +| Host in-band | Ethernet 10/25GbE 4-Port SFP28 adapter | ++-----------------------------------------+----------------------------------------------+ +| BMC OOB Mgt | 1G Copper | ++-----------------------------------------+----------------------------------------------+ + +Power requirements +======================================================================================================================== + +MI355X +------------------------------------------------------------------------------------------------------------------------ + +These are design assumptions for a 4MW cluster with 2K MI355X GPUs, including options for 51.2T or scheduled fabrics +switches (Arista, Dell, Juniper, Cisco, Nokia). These assumptions are based on typical power consumption values for the +specified hardware components, and actual power usage may vary based on specific workloads, configurations, and +environmental conditions. + +.. note:: + These are estimates only; Please consult with hardware vendor model data sheets for more accurate power + specifications. + ++------------------------------------------------+----------------------------------------------------+ +| **System design** | ++------------------------------------------------+----------------------------------------------------+ +| Quantity | 256 MI355X DLC - 2K GPUs | ++------------------------------------------------+----------------------------------------------------+ +| Average Power per system | ≈ 14kW | ++------------------------------------------------+----------------------------------------------------+ +| 256 Systems | ≈ 3.584 Megawatts | ++------------------------------------------------+----------------------------------------------------+ +| **51.2T switch design** | ++------------------------------------------------+----------------------------------------------------+ +| Quantity | ≈ 61 switches - 51.2T switch (Dell, Cisco, Arista) | ++------------------------------------------------+----------------------------------------------------+ +| Estimated typical/load power per switch | 540w/1125w ≈ 32.94kW/68.63kW | ++------------------------------------------------+----------------------------------------------------+ +| **Scheduled fabrics design** | ++------------------------------------------------+----------------------------------------------------+ +| Quantity | ≈ 10 x 7720R4-128PE & 64 x 7700R4C | ++------------------------------------------------+----------------------------------------------------+ +| Estimated typical/load power 7720R4-128PE | 1032w/3848w ≈ 10.32kW/38.48kW | ++------------------------------------------------+----------------------------------------------------+ +| Estimated typical/load power 7720R4C-38PE | 593w/1840w ≈ 37.96kW/117.76kW | ++------------------------------------------------+----------------------------------------------------+ +| Scheduled fabrics estimated power typical/load | ≈ 48.28kW/156.24kW | ++------------------------------------------------+----------------------------------------------------+ +| **Storage/Management network design** | ++------------------------------------------------+----------------------------------------------------+ +| Please consult storage and OEM vendors for design and power specifications. | ++------------------------------------------------+----------------------------------------------------+ + +Network design examples +======================================================================================================================== + +Designs included are based on either Jericho or Ramon switch types (Arista, Ciena, Nokia) or 51.2T switch types (Arista, +Cisco, Dell, Juniper). Vendors and switch models vary for port count and features; please consult your desired vendor's +port count directly to confirm. + +The diagrams presented in this section are designed around a scalable unit or POD, which can determine overall network +end to end latency and AI use cases. Certain ML/AI workloads may require a change of scalable unit size. Please consult +with AMD Architecture as required. + +128 GPU topology design examples 51.2T +------------------------------------------------------------------------------------------------------------------------ + +**Single switch design - 8-128 GPU (1-16 nodes)** + +.. image:: ./data/1k-gpu-topology-design-examples/8-128-gpu-single-sw-design.png + :alt: 8-128 GPU single switch design diagram + +256 - 864 GPU topology design examples scheduled fabrics +------------------------------------------------------------------------------------------------------------------------ + +**Tree design - 129-256 GPU (17-32 nodes)** + +.. image:: ./data/1k-gpu-topology-design-examples/129-256-gpu-tree-design.png + :alt: 129-256 GPU tree design diagram + +**Rail design - 129-288 GPU (17-36 nodes)** + +.. image:: ./data/1k-gpu-topology-design-examples/129-288-gpu-rail-design.png + :alt: 129-288 GPU rail design diagram + +**Tree design - 257-512 GPU (33-64 nodes)** + +.. image:: ./data/1k-gpu-topology-design-examples/257-512-gpu-tree-design.png + :alt: 257-512 GPU tree design diagram + +**Rail design - 289-576 GPU (37-72 nodes)** + +.. image:: ./data/1k-gpu-topology-design-examples/289-576-gpu-rail-design.png + :alt: 289-576 GPU rail design diagram + +**Tree design - 513-768 GPU (65-96 nodes)** + +.. image:: ./data/1k-gpu-topology-design-examples/513-768-gpu-tree-design.png + :alt: 513-768 GPU tree design diagram + +**Rail design - 577-864 GPU (73-108 nodes)** + +.. image:: ./data/1k-gpu-topology-design-examples/577-864-gpu-rail-design.png + :alt: 577-864 GPU rail design diagram + +1K GPU topology design examples scheduled fabrics +------------------------------------------------------------------------------------------------------------------------ + +**Tree design - 128-1024 GPU (16-128 nodes)** + +.. image:: ./data/1k-gpu-topology-design-examples/128-1024-gpu-tree-design.png + :alt: 128-1024 GPU tree design diagram + +**Rail design - 128-1152 GPU (16-144 nodes)** + +.. image:: ./data/1k-gpu-topology-design-examples/128-1152-gpu-rail-design.png + :alt: 128-1152 GPU rail design diagram + +2K GPU topology design examples scheduled fabrics +------------------------------------------------------------------------------------------------------------------------ + +**Tree design - 2048 GPU (256 Nodes)** + +.. image:: ./data/2k-gpu-topology-design-examples/network-diagram-2048GPU-tree-design.png + :alt: Network diagram - 2048 GPU (256 Nodes), Tree design + +**Tree scalable unit - 2048 GPU (256 Nodes)** + +.. image:: ./data/2k-gpu-topology-design-examples/network-diagram-2048GPU-tree-scalable-unit.png + :alt: Network diagram - 2048 GPU (256 Nodes), Tree scalable unit + +**Rail design - 2048-2304 GPU (256-288 Nodes)** + +.. image:: ./data/2k-gpu-topology-design-examples/network-diagram-2048-2304GPU-rail-design.png + :alt: Network diagram - 2048-2304 GPU (256-288 Nodes), Rail design + +**Rail scalable unit - 2048-2304 GPU (256-288 Nodes)** + +.. image:: ./data/2k-gpu-topology-design-examples/network-diagram-2048-2304GPU-rail-scalable-unit.png + :alt: Network diagram - 2048-2304 GPU (256-288 Nodes), Rail scalable unit + +2K GPU topology design examples 51.2T +------------------------------------------------------------------------------------------------------------------------ + +**Tree design - 2072 GPU (259 Nodes)** + +.. image:: ./data/2k-gpu-topology-design-examples/network-diagram-2072GPU-tree-design.png + :alt: Network diagram - 2072 GPU (259 Nodes), Tree design + +**Tree scalable unit - 2072 GPU (259 Nodes)** + +.. image:: ./data/2k-gpu-topology-design-examples/network-diagram-2072GPU-tree-scalable-unit.png + :alt: Network diagram - 2072 GPU (259 Nodes), Tree scalable unit + +**Rail design - 2080 GPU (260 Nodes)** + +.. image:: ./data/2k-gpu-topology-design-examples/network-diagram-2080GPU-rail-design.png + :alt: Network diagram - 2080 GPU (260 Nodes), Rail design + +**Rail scalable unit - 2080 GPU (260 Nodes)** + +.. image:: ./data/2k-gpu-topology-design-examples/network-diagram-2080GPU-rail-scalable-unit.png + :alt: Network diagram - 2080 GPU (260 Nodes), Rail scalable unit + +4K GPU topology design examples scheduled fabrics +------------------------------------------------------------------------------------------------------------------------ + +**Tree design - 4096 GPU (512 Nodes)** + +.. image:: ./data/4k-gpu-toplogy-design-examples/network-diagram-4096GPU-tree-design.png + :alt: Network diagram - 4096 GPU (512 Nodes), Tree design + +**Tree scalable unit - 4096 GPU (512 Nodes)** + +.. image:: ./data/4k-gpu-toplogy-design-examples/network-diagram-4096GPU-tree-scalable-unit.png + :alt: Network diagram - 4096 GPU (512 Nodes), Tree scalable unit + +**Rail design - 4096-4608 GPU (512-576 Nodes)** + +.. image:: ./data/4k-gpu-toplogy-design-examples/network-diagram-4096-4608GPU-rail-design.png + :alt: Network diagram - 4096-4608 GPU (512-576 Nodes), Rail design + +**Rail scalable unit - 4096-4608 GPU (512-576 Nodes)** + +.. image:: ./data/4k-gpu-toplogy-design-examples/network-diagram-4096-4608GPU-rail-scalable-unit.png + :alt: Network diagram - 4096-4608 GPU (512-576 Nodes), Rail scalable unit + +4K GPU topology design examples 51.2T +------------------------------------------------------------------------------------------------------------------------ + +**Tree design - 4144 GPU (518 Nodes)** + +.. image:: ./data/4k-gpu-toplogy-design-examples/network-diagram-4144GPU-tree-design.png + :alt: Network diagram - 4144 GPU (518 Nodes), Tree design + +**Tree scalable unit - 4144 GPU (518 Nodes)** + +.. image:: ./data/4k-gpu-toplogy-design-examples/network-diagram-4144GPU-tree-scalable-unit.png + :alt: Network diagram - 4144 GPU (518 Nodes), Tree scalable unit + +**Rail design - 4104 GPU (513 Nodes)** + +.. image:: ./data/4k-gpu-toplogy-design-examples/network-diagram-4104GPU-rail-design.png + :alt: Network diagram - 4104 GPU (518 Nodes), Rail design + +**Rail scalable unit - 4104 GPU (513 Nodes)** + +.. image:: ./data/4k-gpu-toplogy-design-examples/network-diagram-4104GPU-rail-scalable-unit.png + :alt: Network diagram - 4104 GPU (518 Nodes), Rail scalable unit + +6K GPU topology design examples scheduled fabrics +------------------------------------------------------------------------------------------------------------------------ + +**Tree design - 6016 GPU (752 Nodes)** + +.. image:: ./data/6k-gpu-toplogy-design-examples/network-diagram-6016GPU-tree-design.png + :alt: Network diagram - 6016 GPU (752 Nodes), Tree design + +**Tree scalable unit - 6016 GPU (752 Nodes)** + +.. image:: ./data/6k-gpu-toplogy-design-examples/network-diagram-6016GPU-tree-scalable-unit.png + :alt: Network diagram - 6016 GPU (752 Nodes), Tree scalable unit + +**Rail design - 6144-6912 GPU (768-864 Nodes)** + +.. image:: ./data/6k-gpu-toplogy-design-examples/network-diagram-6144-6912GPU-rail-design.png + :alt: Network diagram - 6144-6912 GPU (768-864 Nodes), Rail design + +**Rail scalable unit - 6144-6912 GPU (768-864 Nodes)** + +.. image:: ./data/6k-gpu-toplogy-design-examples/network-diagram-6144-6912GPU-rail-scalable-unit.png + :alt: Network diagram - 6144-6912 GPU (768-864 Nodes), Rail scalable unit + +6K GPU topology design examples 51.2T +------------------------------------------------------------------------------------------------------------------------ + +**Tree design - 6048 GPU (756 Nodes)** + +.. image:: ./data/6k-gpu-toplogy-design-examples/network-diagram-6048GPU-tree-design.png + :alt: Network diagram - 6048 GPU (756 Nodes), Tree design + +**Tree scalable unit - 6048 GPU (756 Nodes)** + +.. image:: ./data/6k-gpu-toplogy-design-examples/network-diagram-6048GPU-tree-scalable-unit.png + :alt: Network diagram - 6048 GPU (756 Nodes), Tree scalable unit + +**Rail design - 6032 GPU (754 Nodes)** + +.. image:: ./data/6k-gpu-toplogy-design-examples/network-diagram-6032GPU-rail-design.png + :alt: Network diagram - 6032 GPU (754 Nodes), Rail design + +**Rail scalable unit - 6032 GPU (754 Nodes)** + +.. image:: ./data/6k-gpu-toplogy-design-examples/network-diagram-6032GPU-rail-scalable-unit.png + :alt: Network diagram - 6032 GPU (754 Nodes), Rail scalable unit + +8K GPU topology design examples scheduled fabrics +------------------------------------------------------------------------------------------------------------------------ + +**Tree design - 8192 GPU (1024 Nodes)** + +.. image:: ./data/8k-gpu-toplogy-design-examples/network-diagram-8192GPU-tree-design.png + :alt: Network diagram - 8192 GPU (1024 Nodes), Tree design + +**Tree scalable unit - 8192 GPU (1024 Nodes)** + +.. image:: ./data/8k-gpu-toplogy-design-examples/network-diagram-8192GPU-tree-scalable-unit.png + :alt: Network diagram - 8192 GPU (1024 Nodes), Tree scalable unit + +**Rail design - 8192-9216 GPU (1024-1152 Nodes)** + +.. image:: ./data/8k-gpu-toplogy-design-examples/network-diagram-8192-9216GPU-rail-design.png + :alt: Network diagram - 8192-9216 GPU (1024-1152 Nodes), Rail design + +**Rail scalable unit - 8192-9216 GPU (1024-1152 Nodes)** + +.. image:: ./data/8k-gpu-toplogy-design-examples/network-diagram-8192-9216GPU-rail-scalable-unit.png + :alt: Network diagram - 8192-9216 GPU (1024-1152 Nodes), Rail scalable unit + +Legal information +======================================================================================================================== +.. 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Amazon S3, Arista, Arista OSFP, APC, Broadcom, + Ciena, Cisco, CloudVision, DataDirect Networks, Dell, DriveNets, EOS, FS.com, Hammerspace, Hewlett-Packard Enterprise, IOS, + Juniper, JUNOS, Lenovo, Linux, MTP, Netshelter, Nokia, Proliant, Pure Storage, Schneider Electric, SONiC, Super Micro + Computer Inc, Tomahawk, Ubuntu, Vast Data, Weka, and other product names used in this publication are for identification + purposes only and may be trademarks of their respective owners. Certain AMD technologies may require third-party + enablement or activation. Supported features may vary by operating system. Please confirm with the system manufacturer + for specific features. No technology or product can be completely secure. - diff --git a/reference-architecture/MI3XX/legal-information.rst b/reference-architecture/MI3XX/legal-information.rst deleted file mode 100644 index 1d2ebc3..0000000 --- a/reference-architecture/MI3XX/legal-information.rst +++ /dev/null @@ -1,40 +0,0 @@ -Legal information -======================================================================================================================== - -DISCLAIMER - -The information contained herein is for informational purposes only, and is subject to change without notice. -While every precaution has been taken in the preparation of this document, it may contain technical inaccuracies, -omissions and typographical errors, and AMD is under no obligation to update or otherwise correct this information. -Advanced Micro Devices, Inc. makes no representations or warranties with respect to the accuracy or completeness of the -contents of this document, and assumes no liability of any kind, including the implied warranties of noninfringement, -merchantability or fitness for particular purposes, with respect to the operation or use of AMD hardware, software or -other products described herein. No license, including implied or arising by estoppel, to any intellectual property -rights is granted by this document. Terms and limitations applicable to the purchase or use of AMD's products are as -set forth in a signed agreement between the parties or in AMD's Standard Terms and Conditions of Sale. GD-18 - -COMPLIANCE WITH LAWS - -Customer shall adhere to all applicable export laws and regulations including, without limitation, those -administered by the U.S. Department of Commerce - Bureau of Industry and Security (U.S. Export Administration -Regulations 15 CFR 730 et seq.) and those administered by the U.S. Department of State in accordance with the U.S. -International Traffic in Arms Regulations (ITAR) set forth in Subchapter M, Title 22, Code of Federal Regulations, Parts -120 through 130 (22 CFR 120-130), as the same may be amended from time to time, and shall not export, re-export, resell, -transfer, or disclose, directly or indirectly, any Products or technical data, or the direct product of any Products or -technical data, to any proscribed person, entity, or country, or foreign national thereof, unless properly authorized by -the U.S. government and/or any other applicable or relevant government or regulatory body, including the export -authorities of all respective countries. For the avoidance of doubt, Customer shall not use Products in, or re-export -Products to Belarus, Russia and the Donetsk (DNR) or Luhansk (LNR) regions of Ukraine, regardless of the applicable -export laws and regulations. Customer shall impose upon its customers terms at least as restrictive as those contained -in this Clause 14 with respect to any sale, distribution or export of Products. - -© 2025 Advanced Micro Devices, Inc. All -rights reserved. AMD, the AMD Arrow logo, AMD Instinct, AMD together we advance\_, Infinity Fabric, ROCm, and -combinations thereof are trademarks of Advanced Micro Devices, Inc. Amazon S3, Arista, Arista OSFP, APC, Broadcom, -Ciena, Cisco, CloudVision, DataDirect Networks, Dell, DriveNets, EOS, FS.com, Hammerspace, Hewlett-Packard Enterprise, IOS, -Juniper, JUNOS, Lenovo, Linux, MTP, Netshelter, Nokia, Proliant, Pure Storage, Schneider Electric, SONiC, Super Micro -Computer Inc, Tomahawk, Ubuntu, Vast Data, Weka, and other product names used in this publication are for identification -purposes only and may be trademarks of their respective owners. Certain AMD technologies may require third-party -enablement or activation. Supported features may vary by operating system. Please confirm with the system manufacturer -for specific features. No technology or product can be completely secure. - diff --git a/reference-architecture/MI3XX/llms.txt b/reference-architecture/MI3XX/llms.txt new file mode 100644 index 0000000..1bf9575 --- /dev/null +++ b/reference-architecture/MI3XX/llms.txt @@ -0,0 +1,19 @@ +# AMD Instinct MI3XX Reference Design + +> A common reference for designing GPU cluster networks using AMD Instinct MI300X, MI325X, MI350X, and MI355X series accelerators, supporting up to 8192 GPUs. Covers cluster design principles, network topologies, scalable architectures, subscription ratios, power requirements, and bill of materials for large-scale deployments, including fat tree and rail network designs with diagrams and best practices for high-performance AI/ML workloads. + +## Topics + +- [Common cluster design principles](https://instinct.docs.amd.com/projects/MI3XX-reference/en/latest/index.html#common-cluster-design-principles): Fat tree and rail network topologies, how each connects hosts to upstream switches, and a comparison of their latency and cross-rail/cross-rank traffic trade-offs. +- [Basic network topologies](https://instinct.docs.amd.com/projects/MI3XX-reference/en/latest/index.html#basic-network-topologies): Building-block layouts for 2-tier and 3-tier rail, tree, hybrid rail, and fully scheduled rail networks, including TH5/J3 and rail-optimized variants. +- [Scaling networks](https://instinct.docs.amd.com/projects/MI3XX-reference/en/latest/index.html#scaling-networks): When spine and super-spine switches are required as cluster size grows across 2-tier and 3-tier tree, rail, and hybrid designs. +- [Network subscription](https://instinct.docs.amd.com/projects/MI3XX-reference/en/latest/index.html#network-subscription): Subscription ratios and rates that relate downstream demand to upstream capacity, including over- and undersubscription. +- [Hardware and software components](https://instinct.docs.amd.com/projects/MI3XX-reference/en/latest/index.html#hardware-and-software-components): Generic bill of materials for 128–1024 and 1024–8192 GPU cluster and network designs, covering platforms, OS, ROCm, storage, NICs, switches, optics, and fabric. +- [Power requirements](https://instinct.docs.amd.com/projects/MI3XX-reference/en/latest/index.html#power-requirements): Power design assumptions for a 4MW, 2K MI355X GPU cluster with 51.2T or scheduled fabric switch options. +- [Network design examples](https://instinct.docs.amd.com/projects/MI3XX-reference/en/latest/index.html#network-design-examples): Topology design diagrams for tree and rail designs from 128 to 8192 GPUs using Jericho/Ramon scheduled fabrics or 51.2T switches. + +## Legal + +- [Legal information](https://instinct.docs.amd.com/projects/MI3XX-reference/en/latest/index.html#legal-information): Disclaimer, compliance with export laws, and trademark notices. + +--- diff --git a/reference-architecture/MI3XX/overview.rst b/reference-architecture/MI3XX/overview.rst deleted file mode 100644 index 9ba35dd..0000000 --- a/reference-architecture/MI3XX/overview.rst +++ /dev/null @@ -1,599 +0,0 @@ -.. meta:: - :description: Reference materials for cluster and associated network builds - :keywords: network validation, cluster, cluster design, cluster architecture, cluster network - -************************************************************************************************************************ -AMD Instinct MI3XX Reference Design -************************************************************************************************************************ - -This document provides a common reference for designing GPU cluster networks using AMD Instinct MI300X, MI325X, MI350X, -and MI355X series accelerators, supporting up to 8192 GPUs. It covers fundamental cluster design principles, network -topologies, scalable architectures, and bill of materials for large-scale deployments. Also included are practical -examples, diagrams, and recommendations for both fat tree and rail network designs, as well as guidance on scaling, -hardware selection, and best practices for high-performance AI/ML workloads. The audience for this content encompasses -architects, engineers, and IT professionals. - -Common cluster design principles -======================================================================================================================== - -Fat tree network topologies ------------------------------------------------------------------------------------------------------------------------- - -The canonical fat tree topology is a network concept where a switch's connection to upstream peers has at least parity -bandwidth with the total aggregate bandwidth of its downstream connections. This causes links between switches to become -"fatter" as they get closer to the core. - -The "fat tree" topology for AI/ML clusters instead refers to how a host is connected to its upstream switches; in this -case all host NICs terminate on the same switch. It can also be considered 1-rail network. The network itself is -generally a 3-stage or 5-stage folded Clos network due to the fixed radix of network switches. - -Rail network topologies ------------------------------------------------------------------------------------------------------------------------- - -Rail networks leverage the same folded Clos network as tree networks, but host connections are instead aggregated onto -switches based on NIC rank. These shared ranks are referred to as rails and allow the network to provide preferential -latency for connections which share the same rail. The downside to this design is any traffic which needs to cross -rails/ranks must traverse either the network spine layer, or Infinity Fabric (PXN). - -Comparison between fat tree and rail networks ------------------------------------------------------------------------------------------------------------------------- - -Rail networks can provide better latency for traffic within the same rail, enabling larger single hop ring domains. -However, traffic that needs to cross rails can experience higher latency, which can be a bottleneck in large clusters -with high cross-rail traffic. - -.. image:: ./data/basic-network-topology-design-examples/rail-network-traversals.png - :alt: Example of benefits and limitations of rail network traversals - -Fat tree networks handle cross-rank traffic better, but may have higher latency for traffic that could have been -contained within a single rail in a rail network. - -.. image:: ./data/basic-network-topology-design-examples/cross-rank-traffic-tree.png - :alt: Example of benefits and limitations of fat tree network traversals - -The choice between the two often depends on the specific workload and communication patterns of the applications being -run on the cluster. - -Basic network topologies -======================================================================================================================== - -The following sections describe basic layouts for rail, tree, and hybrid network topologies that can be used as building -blocks for larger cluster designs. These layouts are not exhaustive, but provide a starting point for understanding the -trade-offs between different network architectures. - -2-tier rail network ------------------------------------------------------------------------------------------------------------------------- - -The 2-tier rail network design enables large, scalable unit sizes suitable for large jobs or replica sizes, offering -efficiency for workloads that utilize ring-based collectives, though it also results in higher infrastructure costs due -to the need for additional networking hardware. - -.. image:: ./data/basic-network-topology-design-examples/2-tier-rail-network.png - :alt: 2-tier rail network diagram - -2-tier tree network ------------------------------------------------------------------------------------------------------------------------- - -The 2-tier tree network design is efficient for small workloads or replicas and can easily scale by adding capacity with -proper planning. It also has the potential to reduce overall infrastructure costs, while its design helps limit the -blast radius compared to rail networks. - -.. image:: ./data/basic-network-topology-design-examples/2-tier-tree-network.png - :alt: 2-tier tree network diagram - -3-tier rail TH5/J3 network ------------------------------------------------------------------------------------------------------------------------- - -In the 3-tier rail TH5/J3 network design, spine switches are replaced with a two-tier Jericho3-AI/Ramon3 fabric to -enable a larger maximum cluster size, where deeper buffers and scheduled fabric help alleviate congestion in large -clusters with only a small latency trade-off. - -3-tier tree TH5/J3 network ------------------------------------------------------------------------------------------------------------------------- - -The 3-tier tree TH5/J3 network design provides all the same benefits from switching to a scheduled spine fabric as with -rail, but retains the primary characteristics of tree networks. - -3-tier rail optimized network ------------------------------------------------------------------------------------------------------------------------- - -The 3-tier rail optimized network design allows for massive scalable unit sizes and delivers the best ring-based -collective performance at scale, though this comes with the trade-off of weaker any-to-any communication performance. - -.. image:: ./data/basic-network-topology-design-examples/3-tier-rail-optimized-network.png - :alt: 3-tier rail optimized network diagram - -3-tier tree network ------------------------------------------------------------------------------------------------------------------------- - -The 3-tier tree network design allows for massive cluster sizes and delivers excellent any-to-any performance at scale, -making it well-suited for large deployments that need strong, predictable connectivity. This architecture is -particularly effective for campus-style environments, where broad distribution and high performance are both required. - -.. image:: ./data/basic-network-topology-design-examples/3-tier-tree-network.png - :alt: 3-tier tree optimized network diagram - -3-tier hybrid rail network ------------------------------------------------------------------------------------------------------------------------- - -The 3-tier hybrid rail network design allows for massive cluster sizes with large scalable units, favoring ring-based -collectives while still maintaining solid any-to-any performance for large jobs. These characteristics also make it -well-suited for campus-style deployments that balance scalability with broad connectivity requirements. - -.. image:: ./data/basic-network-topology-design-examples/3-tier-hybrid-rail-network.png - :alt: 3-tier hybrid rail network diagram - -3-tier fully scheduled rail network ------------------------------------------------------------------------------------------------------------------------- - -The 3-Tier fully scheduled rail network designuses medium-sized scalable units and delivers excellent congestion -performance thanks to deep buffers and scheduled fabric, though technical limitations restrict the recommended cluster -size to roughly 32,000 GPUs. - -.. image:: ./data/basic-network-topology-design-examples/3-tier-fully-scheduled-rail-network.png - :alt: 3-tier fully scheduled rail network diagram - -Scaling networks -======================================================================================================================== - -As cluster size increases, the network must be scaled to accommodate the additional bandwidth and connectivity -requirements. For a 2-tier tree network, spine switches do not need to be added until a second scalable unit is deployed -as all rail/rank traffic occurs at the unit-level. In a 2-tier rail network, spine switches are needed at deployment to -connect rails at any scalable unit number. - -.. image:: ./data/basic-network-topology-design-examples/2-tier-network-backend-scaling.png - :alt: Example of network backend scaling for 2-tier network design - -In a 3-tier network, a tree design does not require a super spine until a super-scalable unit is deployed. - -.. image:: ./data/basic-network-topology-design-examples/3-tier-network-backend-scaling.png - :alt: Example of network backend scaling for 3-tier network design - -This holds true for hybrid rail as well, where the super spine is only needed at super-scalable unit deployments, but a -fully scheduled rail network requires a super spine from the initial deployment. - -.. image:: ./data/basic-network-topology-design-examples/3-tier-network-backend-deploy-rail.png - :alt: Example of network backend scaling for 3-tier network design - -Network subscription -======================================================================================================================== - -Subscription is the relationship between what is provided by the upstream network and what is required by the downstream -network in demand side. - -It is typically represented as a ratio: - -.. math:: - - Downstream Demand : Upstream Capacity - -In a 1:1 subscribed network the downstream capacity is equal to the upstream capacity, while in 1:1.16 subscribed -network there is .16 more upstream capacity. - -This can also be represented as a percentage: - -.. math:: - - Subscription Rate = \frac{Downstream Demand}{Upstream Capacity} - -An 80% subscription ratio could be referred to as "20% undersubscribed", or a 120% subscription ratio could be referred -to as "20% oversubscribed". - -Hardware and software components -======================================================================================================================== - -128 to 1024 GPU generic BOM ------------------------------------------------------------------------------------------------------------------------- - -The following table provides a generic bill of materials (BOM) for cluster and network designs ranging from 128 to 1024 -GPUs. The actual components and quantities may vary based on specific design choices, vendor selection, and scalability -requirements. - -**Cluster** - -+------------------+-------------------------+ -| Cluster Size | 128 to 1024 GPU | -+==================+=========================+ -| Platforms | Dell XE9680 | -| | Lenovo SR685a V3 | -| | SMCI AS-8125GS | -+------------------+-------------------------+ -| OS | Ubuntu 22.04 (or above) | -+------------------+-------------------------+ -| Linux kernel | 5.15 - 6.80 | -+------------------+-------------------------+ -| ROCm | 6.33 (Or above) | -+------------------+-------------------------+ - -+------------------+-------------------------+ -| Storage Type | | -+==================+=========================+ -| Local storage | 1.6 TB (or greater) | -+------------------+-------------------------+ -| Utility storage | Pure, Vast, RYO | -+------------------+-------------------------+ -| Bulk storage | Vast, DDN, WekaIO | -+------------------+-------------------------+ -| Scratch storage | Vast, DDN, WekaIO, | -| | Hammerspace | -+------------------+-------------------------+ -| Archive/object | S3 compataible | -| storage | | -+------------------+-------------------------+ - -**Network** - -+---------------------------+-----------------------------------------------------+ -| Backside Network Topology | 2 Tier Rail Optimized / Fat Tree | -+===========================+=====================================================+ -| NIC | Pollara 400, BCM957608 (Thor2) | -+---------------------------+-----------------------------------------------------+ -|| Switch || Arista, Dell, Juniper, Cisco, Nokia | -|| || (TH 4/5, Jericho/Ramon) | -+---------------------------+-----------------------------------------------------+ -| Network OS | SONiC, Junos, EOS, IOS | -+---------------------------+-----------------------------------------------------+ -| Subscription ratio | 1:1.16=16% Undersubscribed (AMD recommended) | -+---------------------------+-----------------------------------------------------+ -| Optics | Vendor ACL/HCL transceivers or direct attach copper | -+---------------------------+-----------------------------------------------------+ -| Fabric | RoCEv2 Ethernet | -+---------------------------+-----------------------------------------------------+ - -+-----------------------------------------+----------------------------------------------+ -| Frontside Network Segement | Adapter Recommended | -+=========================================+==============================================+ -| All-in one network | Ethernet 100 GbE 2-port QSFP28 adapter | -+-----------------------------------------+----------------------------------------------+ -| Storage network (optional) | Ethernet 100 GbE 2-port QSFP28 adapter | -+-----------------------------------------+----------------------------------------------+ -| Virtualization network (optional) | Ethernet 100 GbE 2-port QSFP28 adapter | -+-----------------------------------------+----------------------------------------------+ -| Host in-band | Ethernet 10/25GbE 4-Port SFP28 adapter | -+-----------------------------------------+----------------------------------------------+ -| BMC OOB Mgt | 1G Copper | -+-----------------------------------------+----------------------------------------------+ - - -1024 to 8192 GPU generic BOM ------------------------------------------------------------------------------------------------------------------------- - -The following table provides a generic bill of materials for cluster and network designs ranging from 1024 to 8192 GPUs. -The actual components and quantities may vary based on specific design choices, vendor selection, and scalability -requirements. - -**Cluster** - -+------------------+-------------------------+ -| Cluster Size | 1024 to 8192 GPU | -+==================+=========================+ -| Platforms | Dell XE9680 | -| | Lenovo SR685a V3 | -| | SMCI AS-8125GS | -+------------------+-------------------------+ -| OS | Ubuntu 22.04 (or above) | -+------------------+-------------------------+ -| Linux kernel | 5.15 - 6.80 | -+------------------+-------------------------+ -| ROCm | 6.33 (Or above) | -+------------------+-------------------------+ - -+------------------+-------------------------+ -| Storage Type | | -+==================+=========================+ -| Local storage | 1.6 TB (or greater) | -+------------------+-------------------------+ -| Utility storage | Pure, Vast, RYO | -+------------------+-------------------------+ -| Bulk storage | Vast, DDN, WekaIO | -+------------------+-------------------------+ -| Scratch storage | Vast, DDN, WekaIO, | -| | Hammerspace | -+------------------+-------------------------+ -| Archive/object | S3 compataible | -| storage | | -+------------------+-------------------------+ - -**Network** - -+---------------------------+----------------------------------------------+ -| Backside Network Topology | 2 Tier Rail Optimized / Fat Tree | -+===========================+==============================================+ -| NIC | Pollara 400, BCM957608 (Thor2) | -+---------------------------+----------------------------------------------+ -| Switch | Arista, Dell, Juniper, Cisco, Nokia | -| | (TH 4/5, Scheduled Fabrics) | -+---------------------------+----------------------------------------------+ -| Network OS | SONiC, Junos, EOS, IOS, DriveNets | -+---------------------------+----------------------------------------------+ -| Subscription ratio | 1:1.16=16% Undersubscribed (AMD recommended) | -+---------------------------+----------------------------------------------+ -| Optics | Vendor ACL/HCL | -+---------------------------+----------------------------------------------+ -| Fabric | RoCEv2 Ethernet | -+---------------------------+----------------------------------------------+ - -+-----------------------------------------+----------------------------------------------+ -| Frontside Network Segement | Adapter Recommended | -+=========================================+==============================================+ -| All-in one network | Ethernet 100 GbE 2-port QSFP28 adapter | -+-----------------------------------------+----------------------------------------------+ -| Storage network (optional) | Ethernet 100 GbE 2-port QSFP28 adapter | -+-----------------------------------------+----------------------------------------------+ -| Virtualization network (optional) | Ethernet 100 GbE 2-port QSFP28 adapter | -+-----------------------------------------+----------------------------------------------+ -| Host in-band | Ethernet 10/25GbE 4-Port SFP28 adapter | -+-----------------------------------------+----------------------------------------------+ -| BMC OOB Mgt | 1G Copper | -+-----------------------------------------+----------------------------------------------+ - -Power requirements -======================================================================================================================== - -MI355X ------------------------------------------------------------------------------------------------------------------------- - -These are design assumptions for a 4MW cluster with 2K MI355X GPUs, including options for 51.2T or scheduled fabrics -switches (Arista, Dell, Juniper, Cisco, Nokia). These assumptions are based on typical power consumption values for the -specified hardware components, and actual power usage may vary based on specific workloads, configurations, and -environmental conditions. - -.. note:: - These are estimates only; Please consult with hardware vendor model data sheets for more accurate power - specifications. - -+------------------------------------------------+----------------------------------------------------+ -| **System design** | -+------------------------------------------------+----------------------------------------------------+ -| Quantity | 256 MI355X DLC - 2K GPUs | -+------------------------------------------------+----------------------------------------------------+ -| Average Power per system | ≈ 14kW | -+------------------------------------------------+----------------------------------------------------+ -| 256 Systems | ≈ 3.584 Megawatts | -+------------------------------------------------+----------------------------------------------------+ -| **51.2T switch design** | -+------------------------------------------------+----------------------------------------------------+ -| Quantity | ≈ 61 switches - 51.2T switch (Dell, Cisco, Arista) | -+------------------------------------------------+----------------------------------------------------+ -| Estimated typical/load power per switch | 540w/1125w ≈ 32.94kW/68.63kW | -+------------------------------------------------+----------------------------------------------------+ -| **Scheduled fabrics design** | -+------------------------------------------------+----------------------------------------------------+ -| Quantity | ≈ 10 x 7720R4-128PE & 64 x 7700R4C | -+------------------------------------------------+----------------------------------------------------+ -| Estimated typical/load power 7720R4-128PE | 1032w/3848w ≈ 10.32kW/38.48kW | -+------------------------------------------------+----------------------------------------------------+ -| Estimated typical/load power 7720R4C-38PE | 593w/1840w ≈ 37.96kW/117.76kW | -+------------------------------------------------+----------------------------------------------------+ -| Scheduled fabrics estimated power typical/load | ≈ 48.28kW/156.24kW | -+------------------------------------------------+----------------------------------------------------+ -| **Storage/Management network design** | -+------------------------------------------------+----------------------------------------------------+ -| Please consult storage and OEM vendors for design and power specifications. | -+------------------------------------------------+----------------------------------------------------+ - -Network design examples -======================================================================================================================== - -Designs included are based on either Jericho or Ramon switch types (Arista, Ciena, Nokia) or 51.2T switch types (Arista, -Cisco, Dell, Juniper). Vendors and switch models vary for port count and features; please consult your desired vendor's -port count directly to confirm. - -The diagrams presented in this section are designed around a scalable unit or POD, which can determine overall network -end to end latency and AI use cases. Certain ML/AI workloads may require a change of scalable unit size. Please consult -with AMD Architecture as required. - -128 GPU topology design examples 51.2T ------------------------------------------------------------------------------------------------------------------------- - -**Single switch design - 8-128 GPU (1-16 nodes)** - -.. image:: ./data/1k-gpu-topology-design-examples/8-128-gpu-single-sw-design.png - :alt: 8-128 GPU single switch design diagram - -256 - 864 GPU topology design examples scheduled fabrics ------------------------------------------------------------------------------------------------------------------------- - -**Tree design - 129-256 GPU (17-32 nodes)** - -.. image:: ./data/1k-gpu-topology-design-examples/129-256-gpu-tree-design.png - :alt: 129-256 GPU tree design diagram - -**Rail design - 129-288 GPU (17-36 nodes)** - -.. image:: ./data/1k-gpu-topology-design-examples/129-288-gpu-rail-design.png - :alt: 129-288 GPU rail design diagram - -**Tree design - 257-512 GPU (33-64 nodes)** - -.. image:: ./data/1k-gpu-topology-design-examples/257-512-gpu-tree-design.png - :alt: 257-512 GPU tree design diagram - -**Rail design - 289-576 GPU (37-72 nodes)** - -.. image:: ./data/1k-gpu-topology-design-examples/289-576-gpu-rail-design.png - :alt: 289-576 GPU rail design diagram - -**Tree design - 513-768 GPU (65-96 nodes)** - -.. image:: ./data/1k-gpu-topology-design-examples/513-768-gpu-tree-design.png - :alt: 513-768 GPU tree design diagram - -**Rail design - 577-864 GPU (73-108 nodes)** - -.. image:: ./data/1k-gpu-topology-design-examples/577-864-gpu-rail-design.png - :alt: 577-864 GPU rail design diagram - -1K GPU topology design examples scheduled fabrics ------------------------------------------------------------------------------------------------------------------------- - -**Tree design - 128-1024 GPU (16-128 nodes)** - -.. image:: ./data/1k-gpu-topology-design-examples/128-1024-gpu-tree-design.png - :alt: 128-1024 GPU tree design diagram - -**Rail design - 128-1152 GPU (16-144 nodes)** - -.. image:: ./data/1k-gpu-topology-design-examples/128-1152-gpu-rail-design.png - :alt: 128-1152 GPU rail design diagram - -2K GPU topology design examples scheduled fabrics ------------------------------------------------------------------------------------------------------------------------- - -**Tree design - 2048 GPU (256 Nodes)** - -.. image:: ./data/2k-gpu-topology-design-examples/network-diagram-2048GPU-tree-design.png - :alt: Network diagram - 2048 GPU (256 Nodes), Tree design - -**Tree scalable unit - 2048 GPU (256 Nodes)** - -.. image:: ./data/2k-gpu-topology-design-examples/network-diagram-2048GPU-tree-scalable-unit.png - :alt: Network diagram - 2048 GPU (256 Nodes), Tree scalable unit - -**Rail design - 2048-2304 GPU (256-288 Nodes)** - -.. image:: ./data/2k-gpu-topology-design-examples/network-diagram-2048-2304GPU-rail-design.png - :alt: Network diagram - 2048-2304 GPU (256-288 Nodes), Rail design - -**Rail scalable unit - 2048-2304 GPU (256-288 Nodes)** - -.. image:: ./data/2k-gpu-topology-design-examples/network-diagram-2048-2304GPU-rail-scalable-unit.png - :alt: Network diagram - 2048-2304 GPU (256-288 Nodes), Rail scalable unit - -2K GPU topology design examples 51.2T ------------------------------------------------------------------------------------------------------------------------- - -**Tree design - 2072 GPU (259 Nodes)** - -.. image:: ./data/2k-gpu-topology-design-examples/network-diagram-2072GPU-tree-design.png - :alt: Network diagram - 2072 GPU (259 Nodes), Tree design - -**Tree scalable unit - 2072 GPU (259 Nodes)** - -.. image:: ./data/2k-gpu-topology-design-examples/network-diagram-2072GPU-tree-scalable-unit.png - :alt: Network diagram - 2072 GPU (259 Nodes), Tree scalable unit - -**Rail design - 2080 GPU (260 Nodes)** - -.. image:: ./data/2k-gpu-topology-design-examples/network-diagram-2080GPU-rail-design.png - :alt: Network diagram - 2080 GPU (260 Nodes), Rail design - -**Rail scalable unit - 2080 GPU (260 Nodes)** - -.. image:: ./data/2k-gpu-topology-design-examples/network-diagram-2080GPU-rail-scalable-unit.png - :alt: Network diagram - 2080 GPU (260 Nodes), Rail scalable unit - -4K GPU topology design examples scheduled fabrics ------------------------------------------------------------------------------------------------------------------------- - -**Tree design - 4096 GPU (512 Nodes)** - -.. image:: ./data/4k-gpu-toplogy-design-examples/network-diagram-4096GPU-tree-design.png - :alt: Network diagram - 4096 GPU (512 Nodes), Tree design - -**Tree scalable unit - 4096 GPU (512 Nodes)** - -.. image:: ./data/4k-gpu-toplogy-design-examples/network-diagram-4096GPU-tree-scalable-unit.png - :alt: Network diagram - 4096 GPU (512 Nodes), Tree scalable unit - -**Rail design - 4096-4608 GPU (512-576 Nodes)** - -.. image:: ./data/4k-gpu-toplogy-design-examples/network-diagram-4096-4608GPU-rail-design.png - :alt: Network diagram - 4096-4608 GPU (512-576 Nodes), Rail design - -**Rail scalable unit - 4096-4608 GPU (512-576 Nodes)** - -.. image:: ./data/4k-gpu-toplogy-design-examples/network-diagram-4096-4608GPU-rail-scalable-unit.png - :alt: Network diagram - 4096-4608 GPU (512-576 Nodes), Rail scalable unit - -4K GPU topology design examples 51.2T ------------------------------------------------------------------------------------------------------------------------- - -**Tree design - 4144 GPU (518 Nodes)** - -.. image:: ./data/4k-gpu-toplogy-design-examples/network-diagram-4144GPU-tree-design.png - :alt: Network diagram - 4144 GPU (518 Nodes), Tree design - -**Tree scalable unit - 4144 GPU (518 Nodes)** - -.. image:: ./data/4k-gpu-toplogy-design-examples/network-diagram-4144GPU-tree-scalable-unit.png - :alt: Network diagram - 4144 GPU (518 Nodes), Tree scalable unit - -**Rail design - 4104 GPU (513 Nodes)** - -.. image:: ./data/4k-gpu-toplogy-design-examples/network-diagram-4104GPU-rail-design.png - :alt: Network diagram - 4104 GPU (518 Nodes), Rail design - -**Rail scalable unit - 4104 GPU (513 Nodes)** - -.. image:: ./data/4k-gpu-toplogy-design-examples/network-diagram-4104GPU-rail-scalable-unit.png - :alt: Network diagram - 4104 GPU (518 Nodes), Rail scalable unit - -6K GPU topology design examples scheduled fabrics ------------------------------------------------------------------------------------------------------------------------- - -**Tree design - 6016 GPU (752 Nodes)** - -.. image:: ./data/6k-gpu-toplogy-design-examples/network-diagram-6016GPU-tree-design.png - :alt: Network diagram - 6016 GPU (752 Nodes), Tree design - -**Tree scalable unit - 6016 GPU (752 Nodes)** - -.. image:: ./data/6k-gpu-toplogy-design-examples/network-diagram-6016GPU-tree-scalable-unit.png - :alt: Network diagram - 6016 GPU (752 Nodes), Tree scalable unit - -**Rail design - 6144-6912 GPU (768-864 Nodes)** - -.. image:: ./data/6k-gpu-toplogy-design-examples/network-diagram-6144-6912GPU-rail-design.png - :alt: Network diagram - 6144-6912 GPU (768-864 Nodes), Rail design - -**Rail scalable unit - 6144-6912 GPU (768-864 Nodes)** - -.. image:: ./data/6k-gpu-toplogy-design-examples/network-diagram-6144-6912GPU-rail-scalable-unit.png - :alt: Network diagram - 6144-6912 GPU (768-864 Nodes), Rail scalable unit - -6K GPU topology design examples 51.2T ------------------------------------------------------------------------------------------------------------------------- - -**Tree design - 6048 GPU (756 Nodes)** - -.. image:: ./data/6k-gpu-toplogy-design-examples/network-diagram-6048GPU-tree-design.png - :alt: Network diagram - 6048 GPU (756 Nodes), Tree design - -**Tree scalable unit - 6048 GPU (756 Nodes)** - -.. image:: ./data/6k-gpu-toplogy-design-examples/network-diagram-6048GPU-tree-scalable-unit.png - :alt: Network diagram - 6048 GPU (756 Nodes), Tree scalable unit - -**Rail design - 6032 GPU (754 Nodes)** - -.. image:: ./data/6k-gpu-toplogy-design-examples/network-diagram-6032GPU-rail-design.png - :alt: Network diagram - 6032 GPU (754 Nodes), Rail design - -**Rail scalable unit - 6032 GPU (754 Nodes)** - -.. image:: ./data/6k-gpu-toplogy-design-examples/network-diagram-6032GPU-rail-scalable-unit.png - :alt: Network diagram - 6032 GPU (754 Nodes), Rail scalable unit - -8K GPU topology design examples scheduled fabrics ------------------------------------------------------------------------------------------------------------------------- - -**Tree design - 8192 GPU (1024 Nodes)** - -.. image:: ./data/8k-gpu-toplogy-design-examples/network-diagram-8192GPU-tree-design.png - :alt: Network diagram - 8192 GPU (1024 Nodes), Tree design - -**Tree scalable unit - 8192 GPU (1024 Nodes)** - -.. image:: ./data/8k-gpu-toplogy-design-examples/network-diagram-8192GPU-tree-scalable-unit.png - :alt: Network diagram - 8192 GPU (1024 Nodes), Tree scalable unit - -**Rail design - 8192-9216 GPU (1024-1152 Nodes)** - -.. image:: ./data/8k-gpu-toplogy-design-examples/network-diagram-8192-9216GPU-rail-design.png - :alt: Network diagram - 8192-9216 GPU (1024-1152 Nodes), Rail design - -**Rail scalable unit - 8192-9216 GPU (1024-1152 Nodes)** - -.. image:: ./data/8k-gpu-toplogy-design-examples/network-diagram-8192-9216GPU-rail-scalable-unit.png - :alt: Network diagram - 8192-9216 GPU (1024-1152 Nodes), Rail scalable unit \ No newline at end of file diff --git a/reference-architecture/MI3XX/sphinx/_toc.yml.in b/reference-architecture/MI3XX/sphinx/_toc.yml.in index a915ac8..c098ac4 100644 --- a/reference-architecture/MI3XX/sphinx/_toc.yml.in +++ b/reference-architecture/MI3XX/sphinx/_toc.yml.in @@ -1,9 +1,5 @@ -# mi3xx-reference/sphinx/_toc.yml defaults: numbered: False maxdepth: 6 root: index -entries: - - file: overview - title: Overview - - file: legal-information \ No newline at end of file +title: AMD Instinct MI3XX Reference Design \ No newline at end of file