April 2026 monthly summary for vllm-gaudi (vllm-project/vllm-gaudi). Focused on delivering high-impact performance improvements for HPU deployments, compatibility enhancements for Granite 4.0, and governance updates to improve code ownership and review practices. All work aligns with business value of faster, more reliable inference on Gaudi/HPU platforms and streamlined collaboration.

March 2026 — vllm-gaudi: performance optimization experiments balanced with stability and robustness. Implemented Mamba prefix caching to accelerate Mamba layers during model inference, followed by a rollback to maintain stability in attention/convolution paths. Fixed HPUMambaMixer2 inheritance initialization to ensure proper startup and stability. Demonstrated risk-managed optimization, rapid issue diagnosis, and cross-team collaboration.

February 2026: Delivered targeted improvements across VLLM GAUDI repos to boost performance, flexibility, and reliability. Implemented default cache-sharing optimization, enhanced the HPU Granite 4.0-h plugin system for broader model configurations, and fixed a padding block identifier to ensure MambaMixer2 reliability. These changes reduce model latency, improve throughput, and strengthen support for diverse deployments, driving business value through faster inference and more robust plugin/configuration capabilities.

January 2026 performance summary for the two repositories: red-hat-data-services/vllm-gaudi and vllm-project/vllm-gaudi. Delivered core platform enhancements for HPU Granite 4.0-h and Mamba ecosystem integration, including new operations for causal convolution and Mamba Mixer, plugin system, attention enhancements, hybrid KV caching, initial state preparation, bucket alignment for Mamba compatibility, padding handling fixes, and optional KV cache sharing for performance. Implemented a bucket corrector to ensure all Mamba buckets are multiples of the chunk size, improving bucketing correctness. Stabilized the sliding window activation logic to prevent unintended enabling and improve stability across models. Performed Mamba bucketing alignment improvements and addressed Mamba metadata padding fixes. Codebase cleanup and header/documentation updates complemented these changes, enhancing maintainability and onboarding.