February 2026 monthly summary: Delivered a focused feature upgrade in vllm-gaudi by switching Qwen3-VL attention from HPUAttention to HPUMMEncoderAttention, refactoring the attention path for better sequence processing, efficiency, and scalability in multimodal applications. No major bugs fixed this month; efforts centered on robust delivery, code quality, and clear ownership to support subsequent performance optimization and deployment.

December 2025: Gemma3 Multimodal Model Stability and Compatibility Fix for vLLM Gaudi. Delivered a targeted fix to Gemma3 compilation errors in multimodal inputs by replacing dynamic shapes with fixed shapes, aligning with upstream changes, and re-enabling tests to restore multimodal processing stability. Also removed the merge_multimodal workaround and text embedding dynamic paths now that the masked_scatter issue is fixed, resulting in a cleaner, more maintainable code path. Commits include 36d92db13b80c3d767821d11e0eff936eebf59d1 with signed-off attribution, linked to upstream discussions.

Month 2025-10 — Focused on performance and memory optimization for Gemma3 multimodal deployment within vllm-gaudi, delivering substantial improvements in runtime efficiency and memory footprint to enable longer context and scalable inference in production. Key features delivered: - Gemma3 Multimodal Performance and Memory Optimization: introduced bucketing for the vision tower to reduce recompilation overhead, enhanced multimodal merging via torch.where, memory optimizations to support longer sequences, and ensured proper plugin initialization order for reliable startup. - Port and integration work: ported PT_HPU_SDPA_QKV_SLICE_MODE_FWD from vllm-fork to further reduce memory use for longer sequences and improve stability. - Initialization discipline: established 01/02 prefixes for the general plugin initialization order to guarantee ops run before the model, improving startup determinism. Major bugs fixed: - None reported for this repo in Oct 2025; this month’s work focused on performance/memory optimization and initialization correctness rather than bug fixes. Overall impact and accomplishments: - Achieved measurable improvements in memory efficiency and reduced recompilation overhead for Gemma3 multimodal workloads, enabling longer sequences and more scalable deployments with predictable startup behavior. - Strengthened code quality and maintainability through explicit initialization ordering and ported features from a fork with alignment to in-tree practices. Technologies/skills demonstrated: - PyTorch-based model optimization, tensor operations (torch.where), and memory-aware design. - Multimodal systems engineering, repository maintenance, and porting features across forks. - Code hygiene: explicit plugin initialization sequencing and signed-off commits. Commit context: - Repository: vllm-project/vllm-gaudi - Commit: 611f4155ec3e79d4682d58683a841ec88d56522d - Message: Gemma3 Multimodal optimization (#404) with detailed changes and credits. - Sign-offs: Jimin Ha, Mohit Deopujari; Co-authored by Mohit Deopujari.

September 2025 performance summary for vllm-gaudi (Gemma3 and vision model optimizations). Delivered key features and stability improvements enabling Gemma3 enablement and more memory-efficient vision processing, with tangible CI reliability gains and a clear path to further scale. Key features delivered: - Gemma3 Model Improvements and Testing: Added interleaved sliding window support for longer prompts in Gemma3 (V1 enablement) and enhancements to multimodal testing. Included updates to tests and configuration for the gemma-3-4b model to fix test script naming and add necessary config. Commits 481b163a5ae23edb7939521f7dbff34deea0a6a3 and a0bbe78f442d5c5e26b383e83b944619d63a5c08. - Vision Model Memory and Performance Optimization: Implemented HPUMultiHeadAttention with FusedSDPA to improve memory efficiency and speed in vision models. Commit d6611751fa4df6c598e32daf1c0645c42813f279. Major bugs fixed: - Stabilized Gemma3-4b IT test workflow by correcting model file naming and test script paths (gemma-3-4b-it) to ensure reliable CI runs. Commits a0bbe78f442d5c5e26b383e83b944619d63a5c08 and related changes. Overall impact and accomplishments: - Brought Gemma3 closer to production readiness with interleaved sliding window, test, and config polish, while stabilizing CI for gemma-3-4b-it. Achieved notable memory and performance improvements in vision models via FusedSDPA, enabling more efficient multi-image processing and larger prompts. These efforts reduce runtime risk, shorten iteration cycles, and accelerate progress toward larger Gemma3 deployments. Technologies/skills demonstrated: - PyTorch-based model optimization (HPUMultiHeadAttention, FusedSDPA), memory profiling and optimization, test automation and CI stability, model configuration management, and cross-team collaboration to port features from v0 to v1 Gemma3 implementations.

Monthly work summary for 2025-08 focusing on HabanaAI/vllm-fork. Delivered a focused bug fix to max_batch_size initialization for Llama profile runs, which ensures the value is set to 1 only for multimodal models (mrope or mm_optimized). This corrected a profiling-related performance degradation and restored expected throughput for Llama v3.1 70B deployments. The change improves stability under load and reduces risk of regressions in high-traffic inference scenarios.

July 2025 summary focusing on long-context processing, performance, and stability across HabanaAI forks.