June 2026 monthly summary focusing on CPU quantization-enabled MoE inference across two vLLM forks. Delivered end-to-end INT4 W4A16 group-quantized MoE on CPU with weight repacking, kernel integration, and testing for GPTQ/AWQ quantized MoEs; fixed a regression in symmetric quantization guarding zero-point handling to restore CPU backend compatibility; added INT8 W8A8 MoE quantization support for vLLM on CPU with new kernels, weight pre-packing utilities, and tests. This work broadens CPU MoE quantization coverage, enabling more efficient inference, broader model support, and a stronger testing baseline.

Month 2026-05: CPU-side FP8 quantization and MXFP4 support delivered in jeejeelee/vllm, focusing on performance, memory efficiency, and wider deployment on commodity hardware. Implemented FP8 W8A16 linear operations kernel and FP8 W8A16 MoE support in the CPU backend, and added MXFP4 W4A16 MoE quantization within the MoE framework. These changes reduce latency and memory footprint for large-model workloads and expand CPU optimization coverage. No major bugs were reported in this timeframe; validation and CI checks passed.

Month 2024-10: Focused on stabilizing core data-processing paths in red-hat-data-services/vllm-gaudi. Delivered a targeted bug fix in the one_hot operation to handle negative class indices on CPU, preventing runtime errors in torch compile and eager modes. The change improves reliability of feature encoding in model pipelines and reduces production risk.