h100
H100
Overview
Use this skill to do SGLang development on the H100 box through h100_sglang.
The default container is sglang_bbuf and the repo lives at /sgl-workspace/sglang.
Prefer it whenever local validation is insufficient for CUDA, Triton, diffusion pipelines, or other GPU-backed SGLang behavior.
This environment is already prepared:
sglang_bbufis running onlmsysorg/sglang:dev- the repo is cloned at
/sgl-workspace/sglang - editable installs for
python[all]andpython[diffusion]are already done /root/.cacheis mounted as the cache path- Infiniband paths are mounted into the container for RDMA-aware workflows:
/sys/class/infiniband,/dev/infiniband, and/usr/sbin/show_gids
Hugging Face cache is already mounted, but do not assume HF_TOKEN is visible in
every docker exec context. Interactive shells and non-interactive `docker exec
More from bbuf/sglang-auto-driven-skills
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SSH into host `h100_sglang`, enter Docker container `sglang_bbuf`, work in `/data/bbuf/repos/sglang`, and use the ready H100 remote environment for SGLang **diffusion** development and validation. Use when a task needs diffusion model smoke tests, Triton/CUDA kernel validation, torch.compile diffusion checks, or a safe remote copy for diffusion-specific SGLang changes.
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18sglang-sota-performance
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15sglang-minimax-m2-series-optimization
PR-backed and current-main optimization manual for the `MiniMaxAI/MiniMax-M2` series, including M2, M2.1, M2.5, M2.7, and M2.7-highspeed. Use when Codex needs to recover, extend, or audit MiniMax-specific optimizations, TP QK norm/all-reduce behavior, parser contracts, distributed runtime behavior, quantized loading, or backend-specific validation.
15