skills/model-optimization/sglang/sglang-internvl35-optimization/SKILL.md
PR-backed optimization manual for InternVL3.5 in SGLang. Use when an engineer needs to audit, debug, extend, or document InternVL3.5 multimodal processor, video support, ViT DP / CUDA graph, and non-CUDA backend compatibility.
npx skillsauth add BBuf/AI-Infra-Auto-Driven-SKILLS sglang-internvl35-optimizationInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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This skill covers InternVL3.5 multimodal processor, video support, ViT DP / CUDA graph, and non-CUDA backend compatibility.
Evidence snapshot:
c122d343adb969cd9bbd1af2ca86727a11be3845e88b0fd8ac5b1caa6eb42766035029220053369breferences/pr-history.mdmodel-pr-optimization-history/sglang/internvl35/README.zh.md and README.en.mdUse skills/model-optimization/model-pr-diff-dossier/SKILL.md as the production bar.
Every PR cited for this family must be based on diff reading, not only PR titles.
sglang/python/sglang/srt/models/internvl.pySupport InternVL3: Initial InternVL family support that later carried 3.5.Support Piecewise CUDA Graph for InternVL: Added graph capture support on the encoder path.Support InternVL Vision Encoder Data Parallelism: Opened the multi-GPU ViT path.Support Video for InternVL3_5: Extended support to 3.5 video use cases.Support processor and embedding inputs for InternVL: Hardened processor / embed input interoperability.development
Perform SGLang code review in the style of human maintainers by consulting the full non-agent PR review episode corpus from project start through the latest refresh (June 2026), including inline review threads, top-level PR comments, review submissions, original multilingual text, and multi-round discussions. Use when reviewing SGLang PRs, diffs, patches, or local changes for correctness, tests, performance, GPU/runtime risks, API compatibility, and maintainability.
documentation
Use when an SGLang, vLLM, or TensorRT-LLM serving/model optimization task needs prior model-family PR evidence. Query and read the PR-driven history docs under model-pr-optimization-history before choosing source paths, fast paths, kernel/fusion ideas, regression risks, or validation lanes.
development
Run an autonomous Humanize-governed vLLM SOTA performance loop for one LLM model: first perform the fixed fair vLLM/SGLang/TensorRT-LLM deployment search and benchmark, then start one RLCR loop that repeatedly decides the gap, profiles the current bottleneck, runs layer/kernel pipeline analysis, patches vLLM code, optionally uses ncu-report-skill for kernel evidence, and revalidates until vLLM matches or beats the best observed framework under the same workload and SLA.
devops
Inspect LLM torch profiler traces at forward-pass, layer, and kernel level. Use when you need layer timings, anchor-kernel boundaries, representative kernel flows, or Perfetto time ranges.