March 2026 performance review: Implemented key quantization enhancements and activation data-type support across two repositories to boost inference performance, expand hardware compatibility, and reduce quantization overhead. In ping1jing2/sglang, MORI EP gained FP4 dispatch and FP8 combine support, with configurable environment variables and improved quantization flow; a fix to the quark quantization path ensures correction bias uses bf16 for stability and efficiency. In ROCm/aiter, FP8 and MXFP4 quantized activation support for fused MOE eliminates redundant re-quantization when inputs are already in target format, boosting MOE throughput. These changes deliver tangible business value through higher throughput, lower latency, and broader format support, while showcasing proficiency with quantization techniques, environment-driven configurability, and cross-repo collaboration.

February 2026 monthly summary for yhyang201/sglang. Key feature delivered: Aiter Attention Backend Performance Optimization by removing redundant Host-to-Device (H2D) operations, refactoring the attention path to minimize data transfers and CPU overhead. This work enhances attention compute throughput on GPU and reduces wasted compute in the critical path.