build(deps): bump openvino from 2024.6.0 to 2026.2.1 in /camera-ui-openvino-legacy

[dependabot]

Bumps openvino from 2024.6.0 to 2026.2.1.

Release notes

Sourced from openvino's releases.

2026.2.1

Summary of improvements  

• Fixed issue ID 187077: YOLO26 fails to compile on GPU
• Resolved an issue in NPU plugin with shared L0 command queue causing violation of app/inference priorities

You can find OpenVINO™ toolkit 2026.2.1 release here:

• Download archives* with OpenVINO™
• OpenVINO™ for Python: pip install openvino==2026.2.1

2026.2.0

Summary of major features and improvements  

• More GenAI coverage and framework integrations to minimize code changes

  • New models supported: Gemma 4 E2B and Gemma 4 E4B
    • Only on CPUs & GPUs: Qwen3-Coder-Next, Qwen3.5, Qwen3.6, Trinity-mini, LFM2-24B-A2B, LFM2-8B-A1B, LFM2.5-350M​
    • Only on CPUs: YOLO26
    • Only on GPUs: Gemma 4 31B and Gemma 4 26B-A4B
    • Extended to GPUs: GPT-OSS-120B
  • Scaled Dot-Product Attention (SDPA) path support added for LFM2 models
  • Support for Hugging Face Transformers v5.0, ensuring compatibility with the latest model architecture for enhanced interoperability.

• Broader LLM model support and more model compression techniques

  • OpenVINO™ GenAI introduces extension support for loading custom extension libraries and registering unsupported operations via the extensions property. This gives developers the flexibility to run models with custom ops that OpenVINO doesn't support out of the box.
  • INT4 KV-cache compression is enabled for GPUs, with substantial memory reduction when KV cache size is significant, such as with large input prompts exceeding 32K tokens.
  • OpenVINO GenAI significantly reduces model loading times on GPU when using cache blobs — preventing bottlenecks for multi-stage AI pipelines, including agentic use cases that rely on multiple models.
  • Optimized IR read mode with independently managed constant buffers to reduce peak memory usage by avoiding unnecessary duplication of weight data unless required for correctness (Linux support added in this release).
  • Preview: Enhanced XAttention accuracy on CPUs and GPUs through by-channel INT8 KV-cache quantization (compared to by-token INT8 KV-cache), matching the default by-channel INT8 KV cache quantization when XAttention is not enabled.

• More portability and performance to run AI at the edge, in the cloud, or locally.

  • OpenVINO™ GenAI extends its JavaScript API to include a Text-to-Speech pipeline and VLM samples for browser and Node.js developers.
  • Prompt Lookup Decoding extended to vision-language pipelines, delivering significantly faster token generation for multimodal workloads on Intel CPUs and GPUs.
  • OpenVINO™ GenAI now has a smaller runtime footprint after eliminating ICU DLL dependencies from tokenization, leading to reduced memory usage, faster startup, and easier deployment.
  • OpenVINO™ Model Server extends tool-calling support to Qwen 3.5 and 3.6 models to enable agentic AI use cases.
  • OpenVINO™ Model Server adds streaming transcription support for speech-to-text, reducing latency for real-time voice applications.

Support Change and Deprecation Notices

• Discontinued in 2026.0:
  • The deprecated openvino.runtime namespace has been removed. Please use the openvino namespace directly.
  • The deprecated openvino.Type.undefined has been removed. Please use openvino.Type.dynamic instead.
  • The PostponedConstant constructor signature has been updated for improved usability:
    • Old (removed): Callable[[Tensor], None]
    • New: Callable[[], Tensor]
  • The deprecated OpenVINO™ GenAI predefined generation configs were removed.
  • The deprecated OpenVINO GenAI support for whisper stateless decoder model has been removed. Please use a stateful model.
  • The deprecated OpenVINO GenAI StreamerBase put method, bool return type for callbacks, and ChunkStreamer class has been removed.
  • NNCF create_compressed_model() method is now deprecated and removed in 2026. Please use nncf.prune() method for unstructured pruning and nncf.quantize() for INT8 quantization.
  • NNCF optimization methods for TensorFlow models and TensorFlow backend in NNCF are deprecated and removed in 2026. It is recommended to use PyTorch analogous models for training-aware optimization methods and OpenVINO™ IR, PyTorch, and ONNX models for post-training optimization methods from NNCF.
  • The following experimental NNCF methods are deprecated and removed: NAS, Structural Pruning, AutoML, Knowledge Distillation, Mixed-Precision Quantization, Movement Sparsity.
  • CPU plugin now requires support for the AVX2 instruction set as a minimum system requirement. The SSE instruction set will no longer be supported.
  • OpenVINO™ migrated builds based on RHEL 8 to RHEL 9.

... (truncated)

Commits

• ede283a Bump product version to 2026.2.1 (#36321)
• 19c87fd [GPU] Enable SDPA Fusion for 3D inputs and implicit GQA broadcasting (#36029)...
• 5905300 [GPU] Fix dynamic quantization large group case (#36147) (#36311)
• bdf9c59 [NPU] Remove npu_enable_strides_for from supported properties if device doesn...
• c5029c2 [DOCS] Update index page: reflect four models and reorder homepage tiles (#36...
• 390f5f0 [NPU] Disable shared common queue feature (#36227)
• d8fdd45 [GPU] Add Sign activation support for integer types (#36003) (#36276)
• b22820d [NPUW]Fixed high memory footprint and missing MoE runtime behavior attachment...
• 9c191f4 [GPU] Enable new shape infer for GatedDeltaNet op (cherry-pick #35917) (#36251)
• 2d8882d Pin kernels Python module version to fix PyTorch Models Tests (#36136) (#36...
• Additional commits viewable in compare view

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