May 2025 monthly summary for bytedance-iaas/sglang. The team focused on delivering end-to-end QServe quantization to accelerate LLM inference. Delivered CUDA-based W4A8 per-channel and per-group GEMM kernels, with Python bindings, and comprehensive benchmarks and tests. A new quantization configuration was added and integrated into the model's layer processing, enabling 4-bit weights with dynamic per-token symmetric activation quantization. These changes reduce latency and memory footprint in production inference and set the groundwork for broader adoption across models.

Delivered FP8 inference support for Llama4 models in bytedance-iaas/sglang, including a refactor of quantization logic to enable per-channel quantization for INT8 and FP8 formats and tests for the FP8 fused MoE kernel. Core commit: 406524821457fb52123d7b3e433e016b4a2a1d2f (Support Llama4 fp8 inference #5194). Business value: faster, cheaper Llama4 inference with improved accuracy control and robust test coverage; maintainability improved through quantization refactor.

March 2025: Delivered quantization features for bytedance-iaas/sglang with a focus on model efficiency, hardware coverage, and robust validation. Key work includes DeepSeek V3 INT8 quantization (channel-wise and block-wise) with a refactored fused MoE kernel to support INT8, plus tests for correctness and performance. Also added W8A8 FP8 quantization support (kernel/configurations), extended utilities and tests for FP8 on AMD hardware, and documented w8a8_fp8 and w8a8_int8 options in the sg lang backend. Strengthened test coverage and documentation to reduce production risk. Overall impact includes lower inference latency, reduced memory footprint, and broader hardware deployment options, with demonstrated skills in quantization, kernel refactoring, testing, and technical documentation.

January 2025 focused on delivering high-impact FP8 (e4m3) scaled GEMM support with CUTLASS kernels for the sgLang project, enabling faster low-precision matrix multiplications and expanding the library's applicability for inference workloads. The work included new CUDA kernels, Python bindings for FP8 GEMM, a performance benchmark script, and integration of FP8 GEMM into the sgl-kernel library. Key regression-free changes were validated against existing workflows to preserve compatibility with the sgl-kernel API, with careful consideration to maintainability and readability in the kernel codebase.

Month: 2024-12 (fzyzcjy/sglang). This period focused on delivering MoE performance enhancements and stabilizing the FP8 path, with emphasis on business value and production-readiness. Key outcomes include feature delivery for block-wise FP8 quantization, kernel and tuner improvements, and targeted bug fixes that reduce crashes and memory risks in MoE kernel execution.

Monthly summary for 2024-11 focused on the pytorch/ao repository. Delivered the Marlin QQQ kernel support with INT8 Tensor Core mixed-precision GEMM (W4A8 Marlin kernel), including benchmarks and validation tests. No major bugs reported or resolved this period. The work advances performance, efficiency, and reliability for low-precision inference and supports continued optimization of GEMM workloads.