May 2026 monthly summary for jeejeelee/vllm focused on delivering a Performance Benchmarking and Tuning Script for the Mamba Selective State Update (SSU) kernel. Implemented a benchmarking and tuning script that enables performance optimization via configuration management and JSON-based tuning for the Mamba SSU kernel, establishing a repeatable, data-driven workflow to identify and mitigate bottlenecks. The work aligns with Triton Mamba SSU kernel goals and is captured in a dedicated commit with multi-author sign-offs. No major bug fixes were documented for this repository in May 2026.

April 2026: Delivered reliability and performance improvements across two repositories (jeejeelee/vllm and flashinfer-ai/flashinfer). Implemented MTP-safe logprob handling, LoRA MoE backend stabilization, and FP8 quantization robustness, with expanded tests and autotuning validations. These changes reduce inference risk, improve backbone performance, and enable safer deployment of larger MoE configurations in production environments.

Concise monthly summary for March 2026 focusing on key accomplishments, bugs fixed, and impact across flashinfer-ai/flashinfer and jeejeelee/vllm. Highlighted reliability improvements for AutoTuner, expanded MXFP8 MoE capabilities, and test coverage enhancements enabling broader production readiness.

February 2026 monthly performance summary for jeejeelee/vllm and flashinfer-ai/flashinfer. Delivered cross-repo FP8/MXFP8 quantization enhancements, MoE optimization improvements, and stability fixes that enable faster, more reliable FP8/MXFP8 inference and easier adoption of MXFP8 checkpoints. Highlights include LoRA FP8 compatibility improvements, MXFP8 dense-model support with flashinfer mm_mxfp8 integration, a Nemotron TP4/B200 fused MoE config, a FlashInfer autotuner reshaping bug fix, and the new MXFP8 GEMM API (mm_mxfp8) with Cutlass. These changes drive higher throughput, lower latency, and greater deployment readiness for ModelOpt MXFP8 workloads.

January 2026 focused on delivering flexible, efficient inference capabilities for Nemotron-H/Nano models in jeejeelee/vllm, along with reliability improvements and benchmarking enhancements. The work emphasizes business value through device-specific optimizations and robust quantization handling, enabling faster deployment and more accurate performance assessments across configurations.

December 2025 performance highlights for flashinfer-ai/flashinfer and jeejeelee/vllm focusing on business value and technical achievements. The month delivered new acceleration and adaptability capabilities, along with robust validation; no critical bug fixes were reported this period.