vllm-ascend-server
Skill177 starsChanged 8 months agoNo licenceView on GitHub
Deploy vLLM inference services on Ascend NPU servers with automatic model detection and optimized configuration. Supports local and remote deployment across bare metal, containers, and Docker images. Handles model discovery, quantization auto-detection, tensor parallelism configuration, graph/eager mode selection, and service health verification. Use when users need to: (1) Start or deploy vLLM server on NPU, (2) Launch LLM inference service, (3) Configure multi-card tensor parallel deployment, (4) Enable speculative decoding (Eagle) or quantization, (5) Run vllm offline batch inference, (6) Check or test vLLM service status.
In this repository40 other files it gives its agents.
- AGENTS.mdAGENTS.md
- create-prSkill
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- github-issue-summarySkill
- tech-docs-guardSkill
- vllm-daily-pr-issue-trackerSkill
- ai-for-science-ai4s-mainSkill
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- ascend-avi-vnpuSkill
- ascend-dmiSkill
- ascend-dockerSkill
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- torch_npuSkill
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- ascend-migration-analysisSkill
- atc-model-converterSkill
- inferencex-reportSkill
- migration-ascend-torchnpu-skillsSkill
- npu-torchair-inferSkill
- vllm-ascendSkill
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- wan-ascend-adaptationSkill
- ascendcSkill
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- profiling-analysisSkill
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- training-mfu-calculatorSkill
- hccl-testSkill
- rl-msprobeSkill
- torch-npu-comm-testSkill
- verl-quickstartSkill
The file
We split this skill into the rules it gives an agent
2 rules. About 0 are common, 1 is distinctive, and 1 is very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈1
Distinctive · estimate
This author's own choices. Worth a look.
1
Very specific
Tied to their project's names, files or tools. Probably skip.
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≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
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