De la autor
Use when self-hosting an open-weight LLM for high-throughput concurrent serving with vLLM — running an OpenAI-compatible endpoint, splitting a model across GPUs with tensor or pipeline parallelism, loading quantized weights, serving one or many LoRA adapters, and debugging KV-cache OOM from memory-utilisation and context-length flags. NOT renting or provisioning the GPU box (that is `runpod` or `modal`), NOT single-user laptop inference (that is `ollama`), NOT a hosted inference API you do not operate (that is `together-fireworks` or `huggingface`).
Instalare
npx skills add ericrisco/rsc-harness --skill vllm -a claude-code
Se instalează în .claude/skills/vllm/ · Fiecare comandă descarcă skill-ul direct de pe GitHub cu utilitarul open source skills.
Identic octet cu octet cu commit-ul scanat, cu licența depozitului și un fișier SOURCE.txt.
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Verificări de siguranță pe SKILL.md
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De ce 85
aiqbench calculează scorul de încredere din semnale publice. Nu este un audit de securitate, așa că citește un skill înainte să-l instalezi.
Skill-uri similare
Cele mai apropiate de acesta prin ce fac și când sunt folosite.
- ollamaericrisco/rsc-harnessUse when running open-weight LLMs locally with Ollama — pulling and tagging models, calling the local API, picking a quantization or GGUF, writing Modelfiles, and sizing VRAM and RAM for the machine at hand. NOT remote or managed GPU serving and autoscaling (that is `runpod`), NOT downloading raw weights or datasets (that is `huggingface`), NOT retrieval pipeline design (that is `rag`).Design★ 255
- vllm-serverBagelHole/DevOps-Security-Agent-SkillsDeploy and manage vLLM for high-throughput LLM inference. Configure continuous batching, tensor parallelism, quantization, and OpenAI-compatible API endpoints for production LLM serving.DevOps★ 1.2K
- serving-llms-vllmKalarisLabs/research-agent-skillsServes LLMs with high throughput using vLLM's PagedAttention and continuous batching. Use when deploying production LLM APIs, optimizing inference latency/throughput, or serving models with limited GPU memory. Supports OpenAI-compatible endpoints, quantization (GPTQ/AWQ/FP8), and tensor parallelism.DevOps★ 61
- open-weightsericrisco/rsc-harnessUse when choosing an open-weight LLM and clearing it for use — which family and size fit the task, the hardware and the budget, and above all whether the license permits shipping. Owns the license-class map (OSI-open versus custom-community versus non-commercial), the always-verify-the-model-card rule, size-to-VRAM budgeting and the quant formats. This layer decides WHICH model and whether the license allows it, then hands off. NOT downloading or hosting on the Hub (that is `huggingface`), NOT running it locally (that is `ollama`), NOT serving at throughput (that is `vllm`), NOT fine-tuning it (that is `finetuning`).Productivitate★ 255
- together-fireworksericrisco/rsc-harnessUse when calling open-weight LLMs on Together AI or Fireworks AI's OpenAI-compatible endpoints — `base_url` plus namespaced model id, the cheapest model that clears the bar, per-1M-token cost math, serverless vs batch vs dedicated. NOT renting GPUs to self-host weights (that is `runpod`), NOT running a model locally for free (that is `ollama`).Altele★ 255
- agentsop-vllmagentsope/SkillAlchemyDecision SOP for serving LLMs with vLLM. Covers PagedAttention mental model, quantization/parallelism/batching tradeoffs, OOM triage, and when NOT to use vLLM. Activates when a coder-agent is choosing or tuning an inference engine, debugging vLLM throughput/latency/OOM, or comparing vLLM against TGI/SGLang/TensorRT-LLM/llama.cpp.Inginerie★ 446
Note și recenzii
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