January 2026: Implemented performance optimizations for Qwen3-VL vision attention on Gaudi and expanded multimodal test coverage in vllm-gaudi. Key changes include enabling HPU Fused SDPA for vision attention when query length q_len ≤ 65536, removing the per-block Q/K/V attention loop, and aligning with the optimized Qwen2.5-VL path to preserve identical model outputs. Added Qwen3 multimodal image test coverage with a new test function and configuration file, and introduced an explicit Qwen3 image test case to validate multimodal performance. Commits: 7011e318e8185a5d77aa255e75f6fbd61fff4637 and f8cb8d28b1f18800616c62006cfedbfcf43e68c9. Signed-off contributors: slokesha, Spurthi Lokeshappa, Iryna Boiko. Impact: improved throughput and efficiency on Gaudi, reduced regression risk through broader test coverage. No major bugs fixed reported this month; focus was on feature delivery and test robustness.

December 2025 — vllm-gaudi: Delivered two customer-impacting features that improve performance, accuracy, and input flexibility. Implemented stateful pooling state support for embedding tasks and introduced flexible image input sizing for multimodal models (removing the fixed 112x112 constraint and adding padding/masking). These changes boost throughput, reduce preprocessing overhead, and broaden deployment scenarios in production.

Month 2025-09: Delivered Embedding Model Pooling Support for vllm-gaudi, enabling pooling tasks in the HPU model runner and supporting pooling-based embedding generation. Added test coverage to ensure reliability and compatibility across embedding models, expanding versatility and use cases with minimal disruption to existing workflows.

January 2025 monthly summary for HabanaAI/optimum-habana-fork: Focused on strengthening the model evaluation test suite for Habana hardware with expanded Hugging Face coverage. Implemented enhancements to encoder-decoder tests, refined performance metrics, and introduced a throughput warmup step to align tests with Habana characteristics. Added Gemma-2-27b unit test to broaden coverage. These changes improve validation reliability, reduce regression risk, and accelerate validation for Habana-accelerated models, enabling faster, higher-confidence releases. Technologies demonstrated include Python-based testing (PyTest), Habana accelerator workflows, Hugging Face Transformers coverage, and CI/test configuration improvements.