December 2025 monthly summary for vllm-gaudi focusing on delivering quantization optimizations for MoE models. Implemented per-channel FP8 weight dequantization using a compressed-tensor method and added incremental dynamic quantization (INC) for MoE models by incorporating channel-wise dequantized weights into the MoE operator. These changes enhance inference efficiency and reduce memory footprint for large MoE deployments on Gaudi-backed environments.

Month 2025-10: Delivered Intel HPU Cache Block Size Support for neuralmagic/vllm. Implemented a cache configuration update to include a 256-block size to enable Intel HPU hardware utilization. This is a straightforward configuration enhancement to an existing literal type.

March 2025 monthly summary for red-hat-data-services/vllm-gaudi. Focused on observability quality through a minor, low-risk code-quality fix to improve log clarity. No feature work delivered this month; the effort was a precise correction to logging output to reduce ambiguity during debugging across HPU platforms.