March 2026 performance-focused month delivering accelerated model execution and enhanced evaluation capabilities. Key work includes CUDA-graph based piecewise execution for NemotronH hybrid models, removal of external dependency by replacing einops with native PyTorch ops, robust handling of non-attention layers in the model runner, and the introduction of a Generative Scoring API for item-query evaluation.

February 2026 monthly summary for kvcache-ai/sglang focusing on embedding model adaptability and performance improvements.

November 2025 monthly summary for kvcache-ai/sglang: Focused on elevating performance visibility and metric accuracy for embeddings and speculative decoding, enabling data-driven optimizations and reliable performance targets across the embeddings API.

October 2025 monthly summary for kvcache-ai/sglang: Delivered CI/CD-based performance benchmarking for the Generative Scores API, enabling automated latency, throughput, and batch-scaling tests. Introduced a test utility to run benchmarks (run_score_benchmark) and integrated tests into the PR CI workflow to ensure early detection of regressions and SLA-aligned performance goals.

Month: 2025-09 | Repo: kvcache-ai/sglang. Delivered CI-driven testing for the Score API by integrating the Score API test suite into the GitHub Actions CI/CD pipeline to run automatically on every commit, significantly improving regression coverage and release confidence for the Score API feature. No major bugs fixed this month; the focus was on establishing automated quality gates that reduce manual testing effort. Overall impact: higher quality Score API releases, faster feedback to developers, and more trustworthy feature delivery. Technologies/skills demonstrated: GitHub Actions CI/CD, Python-based test automation, test orchestration, and collaborative development (co-authored commits).