vllm-ascend-server
vllm-ascend-server is an MLOps & model serving skill for AI agents, published by ascend-ai-coding in the awesome-ascend-skills repository on GitHub. Skillspace rates vllm-ascend-server D (69/100) and it passed the safety scan; the rating is based on static analysis only.
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.
- Grade
- D
- Quality
- 69 / 100
- Safety
- Passed
- GitHub stars
- 136
Unproven: rated by static analysis only, not yet compared head-to-head.
Our two independent classifiers differ on this skill's category, so treat the placement as provisional.
This rating describes the version of the repository we crawled; it may have changed since, so go by its current files.
Our review
- Architecture
- 91 / 100
- Maturity
- Structured: ships references or scripts
- Security scan
- No issues found
- License
- Not declared. Check the repository before reusing it.
- Adoption
- Adopted by 0 other repositories, in 0 collections
Not checked yet
- Not judged on the rubric yet (not a contender in its category)
- No blind task eval yet
- Scope (portable / vendor / repo-internal) not assessed; only the top of each category is read for scope
- Sibling files (scripts, references) not fetched; security scan covers SKILL.md only
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