2026-03 Monthly summary for vllm-gaudi focused on stabilizing the multimodal warmup workflow and ensuring reliable startup under budget constraints. The work delivered restores stable multimodal functionality and reduces downtime for experimentation and deployment.

February 2026 focused on stabilizing vLLM on the HPU backend with torch.compile and tightening async/unified attention paths for Qwen2.5-VL. Delivered two critical bug fixes that reduce crashes, improve sampling reliability, and enhance model accuracy on representative workloads. Key outcomes include a NumPy-free padding path for HPU, dispatch-key compatibility with torch.compile, and corrective logits handling in the async scheduler with unified attention. Overall impact: increased reliability for production inference on HPU, lower risk of runtime crashes, and improved accuracy in evaluated scenarios. Technologies demonstrated include PyTorch, torch.compile, HPU backend optimization, dispatch-key management, and async/unified attention workflows.

In January 2026, delivered stability and performance improvements in the vllm-gaudi project, focusing on multi-modal inference reliability and accuracy parity with GPU baselines. Implemented robust caching strategy to prevent runtime errors in multi-modal models and fixed accuracy regression in Qwen2.5-VL, aligning MMMU performance with expected baselines. These changes reduce production incidents and improve model utility for MMMU workloads.

Month: 2025-06 — HabanaAI/vllm-hpu-extension Key accomplishments and features delivered: - ALiBi support fully enabled in the vLLM HPU extension, introducing memory usage optimizations and environment-variable configurability to simplify deployment and tuning for long-context workloads. - Resolved long-sequence accuracy issues by enabling float32 biases, improving numerical stability and model reliability on Habana AI hardware. - Verified and ensured ALiBi operates correctly in both lazy and eager execution modes, with defined restrictions on supporting features to maintain stability. - Clear traceability and delivery via a focused commit: 2bcd7f8805f3cd6089e7f1a2db64164c70fd28f1 (vLLM-Ext: Full enabling of ALiBi (#34) (#141)).

November 2024 highlights for HabanaAI/vllm-fork. Key features delivered: ALiBi support for vLLM-Base attention with memory optimization, including new environment variables to control ALiBi behavior. Refactored attention bias calculations for both prompt and decode stages to improve accuracy and compatibility across model architectures and attention implementations. Commit bf8726b9134869ba9fe530e34faf28e10bd85c78 documents the full enabling of ALiBi.