[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6FD1QR0PHEYFC6V9VN04SS0":3,"skill-same:01M6FD1QR0PHEYFC6V9VN04SS0":81},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":15,"categories":16,"keywords":25,"publishedAt":47,"linkOnly":48,"markdownContent":49,"markdownBody":50,"frontMatter":51,"refName":38,"commitSha":61,"skillDirectory":62,"forksCount":63,"repositoryHtmlUrl":64,"repositoryHomepageUrl":6,"ownerHtmlUrl":65,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":66,"install":67,"resources":80},"01M6FD1QR0PHEYFC6V9VN04SS0","vllm-server",null,"Deploy and manage vLLM for high-throughput LLM inference. Configure continuous batching, tensor parallelism, quantization, and OpenAI-compatible API endpoints for production LLM serving.","boisenoise\u002Fskills-collections","boisenoise","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F193180?v=4","01M6CDBDQK6BDN55ZMA6B6PKBM","01M6CDBDVSJ3JPTBYN26FZ7TZK",13,"MIT","https:\u002F\u002Fgithub.com\u002Fboisenoise\u002Fskills-collections\u002Fblob\u002Fmain\u002Fskills\u002Fantigravity-vllm-server\u002FSKILL.md",[17,22],{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000010","后端与接口","SCENARIO","使用场景",{"categoryNo":23,"categoryName":24,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000011","运维与部署",[26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46],"server","vLLM","vllm server","community","2026","openai-compatible","throughput","relevant","scripts","critical","os","platform","main","security","manage","only","configure","access","api","devops-security","openai","2026-11-10 10:12:30",false,"---\nname: vllm-server\ndescription: Deploy and manage vLLM for high-throughput LLM inference. Configure continuous\n  batching, tensor parallelism, quantization, and OpenAI-compatible API endpoints\n  for production LLM serving.\ncategory: devops\nrisk: critical\nsource: https:\u002F\u002Fgithub.com\u002FBagelHole\u002FDevOps-Security-Agent-Skills\nsource_repo: BagelHole\u002FDevOps-Security-Agent-Skills\nsource_type: community\ndate_added: '2026-09-20'\nlicense: MIT\nlicense_source: https:\u002F\u002Fgithub.com\u002FBagelHole\u002FDevOps-Security-Agent-Skills\u002Fblob\u002Fmain\u002FLICENSE\ncompatibility: Requires the relevant OS\u002Fplatform tooling and privileged access where\n  noted. Docs-only; helper scripts and templates not bundled.\nmetadata:\n  author: devops-skills\n  version: '1.0'\n---\n\n# vLLM Server Management\n\nDeploy production-grade LLM inference servers with vLLM — the fastest open-source LLM serving engine with PagedAttention and continuous batching.\n\n## When to Use This Skill\n\nUse this skill when:\n- Serving open-source LLMs (Llama, Mistral, Qwen, Gemma) at scale\n- Building an OpenAI-compatible API endpoint for self-hosted models\n- Optimizing LLM throughput and latency for production traffic\n- Running multi-GPU inference with tensor or pipeline parallelism\n- Deploying quantized models to reduce GPU memory requirements\n\n## Version and exposure boundary\n\nPin a reviewed vLLM release, model revision and container digest. Validate the exact release’s Python\u002FCUDA\u002Fhardware requirements and flags; the versions, memory figures and model\u002Fquantization examples below are historical sketches, not fit guarantees. Model weights, activations and KV cache must fit together. Keep examples loopback-only; remote service needs approved authentication, TLS, quotas and network policy. Never use the sample key as a real secret.\n\n## Prerequisites\n\n- NVIDIA GPU(s) with CUDA 12.1+ (A100\u002FH100 recommended for production)\n- Docker or Python 3.9+ with pip\n- 40GB+ VRAM for 70B models; 8GB+ for 7B models\n- `nvidia-container-toolkit` for Docker GPU passthrough\n\n## Quick Start\n\n```bash\n# Install vLLM\npip install vllm\n\n# Serve a model (OpenAI-compatible API)\nvllm serve meta-llama\u002FLlama-3.1-8B-Instruct \\\n  --host 127.0.0.1 \\\n  --port 8000 \\\n  --api-key your-secret-key\n\n# Test the endpoint\ncurl http:\u002F\u002Flocalhost:8000\u002Fv1\u002Fchat\u002Fcompletions \\\n  -H \"Content-Type: application\u002Fjson\" \\\n  -H \"Authorization: Bearer your-secret-key\" \\\n  -d '{\n    \"model\": \"meta-llama\u002FLlama-3.1-8B-Instruct\",\n    \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]\n  }'\n```\n\n## Docker Deployment\n\n```bash\ndocker run --runtime nvidia --gpus all \\\n  -v ~\u002F.cache\u002Fhuggingface:\u002Froot\u002F.cache\u002Fhuggingface \\\n  -p 127.0.0.1:8000:8000 \\\n  --ipc=host \\\n  vllm\u002Fvllm-openai:latest \\\n  --model meta-llama\u002FLlama-3.1-8B-Instruct \\\n  --api-key your-secret-key\n```\n\n## Docker Compose (Production)\n\n```yaml\nservices:\n  vllm:\n    image: vllm\u002Fvllm-openai:latest\n    runtime: nvidia\n    environment:\n      - NVIDIA_VISIBLE_DEVICES=all\n      - HUGGING_FACE_HUB_TOKEN=${HF_TOKEN}\n    volumes:\n      - model-cache:\u002Froot\u002F.cache\u002Fhuggingface\n    ports:\n      - \"127.0.0.1:8000:8000\"\n    ipc: host\n    command: >\n      --model meta-llama\u002FLlama-3.1-70B-Instruct\n      --tensor-parallel-size 2\n      --max-model-len 32768\n      --gpu-memory-utilization 0.90\n      --api-key ${VLLM_API_KEY}\n    restart: unless-stopped\n    healthcheck:\n      test: [\"CMD\", \"curl\", \"-f\", \"http:\u002F\u002Flocalhost:8000\u002Fhealth\"]\n      interval: 30s\n      timeout: 10s\n      retries: 3\n\nvolumes:\n  model-cache:\n```\n\n## Key Configuration Options\n\n### Multi-GPU Tensor Parallelism\n\n```bash\n# Split one model across 4 GPUs\nvllm serve meta-llama\u002FLlama-3.1-70B-Instruct \\\n  --tensor-parallel-size 4 \\\n  --gpu-memory-utilization 0.90\n```\n\n### Quantization (Lower VRAM)\n\n```bash\n# AWQ quantization (70B on 2x A100 40GB)\nvllm serve casperhansen\u002Fllama-3-70b-instruct-awq \\\n  --quantization awq \\\n  --tensor-parallel-size 2\n\n# GPTQ quantization\nvllm serve TheBloke\u002FLlama-2-70B-Chat-GPTQ \\\n  --quantization gptq\n\n# FP8 (H100 NVL native)\nvllm serve meta-llama\u002FLlama-3.1-405B-Instruct \\\n  --quantization fp8 \\\n  --tensor-parallel-size 8\n```\n\n### Structured Output & Tools\n\n```bash\nvllm serve meta-llama\u002FLlama-3.1-8B-Instruct \\\n  --enable-auto-tool-choice \\\n  --tool-call-parser llama3_json \\\n  --guided-decoding-backend outlines\n```\n\n### LoRA Adapters\n\n```bash\nvllm serve meta-llama\u002FLlama-3.1-8B-Instruct \\\n  --enable-lora \\\n  --lora-modules sql-lora=\u002Fpath\u002Fto\u002Fsql-lora \\\n                 code-lora=\u002Fpath\u002Fto\u002Fcode-lora \\\n  --max-lora-rank 64\n```\n\n## Performance Tuning\n\n```bash\n# Maximize throughput for batch workloads\nvllm serve REPLACE_WITH_REVIEWED_MODEL \\\n  --max-num-seqs 256 \\\n  --max-num-batched-tokens 8192 \\\n  --gpu-memory-utilization 0.95 \\\n  --swap-space 4                  # CPU swap (GiB)\n\n# Minimize latency for interactive use\nvllm serve REPLACE_WITH_REVIEWED_MODEL \\\n  --max-num-seqs 32 \\\n  --enforce-eager              # disable CUDA graph capture\n```\n\n## Benchmarking\n\n```bash\n# Install benchmark tool\npip install vllm\n\n# Run throughput benchmark\npython -m vllm.entrypoints.openai.run_batch \\\n  --model meta-llama\u002FLlama-3.1-8B-Instruct \\\n  --input-file prompts.jsonl \\\n  --output-file results.jsonl\n\n# Benchmark with vllm bench\nvllm bench throughput \\\n  --model meta-llama\u002FLlama-3.1-8B-Instruct \\\n  --num-prompts 1000 \\\n  --input-len 512 \\\n  --output-len 128\n```\n\n## Monitoring\n\n```bash\n# Check running server stats\ncurl http:\u002F\u002Flocalhost:8000\u002Fmetrics  # Prometheus metrics\n\n# Key metrics to watch:\n# vllm:num_requests_running       - active requests\n# vllm:gpu_cache_usage_perc       - KV cache utilization\n# vllm:generation_tokens_per_s    - throughput\n# vllm:time_to_first_token_ms     - TTFT latency\n# vllm:e2e_request_latency_seconds - end-to-end latency\n```\n\n## Common Issues\n\n| Issue | Cause | Fix |\n|-------|-------|-----|\n| `CUDA out of memory` | Model too large for VRAM | Add `--quantization awq` or reduce `--gpu-memory-utilization` |\n| Slow cold start | Model not cached | Pre-pull with `huggingface-cli download \u003Cmodel>` |\n| Low throughput | Too few concurrent requests | Increase `--max-num-seqs` |\n| KV cache full errors | Context length too long | Set `--max-model-len` lower |\n| `tokenizer error` | Tokenizer mismatch | Use `--tokenizer` to specify correct tokenizer |\n\n## Best Practices\n\n- Use `--gpu-memory-utilization 0.90` to leave headroom for CUDA kernels.\n- Pin model versions with `--revision` for reproducible deployments.\n- Set `HF_HUB_OFFLINE=1` in production to prevent unexpected downloads.\n- Use AWQ or GPTQ quantization before tensor parallelism — lower VRAM first.\n- Enable `--enable-chunked-prefill` for long-context workloads.\n- Monitor `gpu_cache_usage_perc` — above 95% causes queuing.\n\n## Related Skills\n\n- llm-inference-scaling (`llm-inference-scaling`) - Auto-scaling vLLM deployments\n- gpu-server-management (`gpu-server-management`) - GPU driver setup\n- llm-gateway (`llm-gateway`) - Load balancing across vLLM instances\n- llm-cost-optimization (`llm-cost-optimization`) - Cost management\n- model-serving-kubernetes (`model-serving-kubernetes`) - K8s deployment\n\n## Limitations\n\n- Infrastructure commands can disrupt services: confirm target host\u002Fscope and have backups\u002Fsnapshots before mutating state.\n- Docs-only import: upstream scripts and templates not bundled.\n\n","# vLLM Server Management\n\nDeploy production-grade LLM inference servers with vLLM — the fastest open-source LLM serving engine with PagedAttention and continuous batching.\n\n## When to Use This Skill\n\nUse this skill when:\n- Serving open-source LLMs (Llama, Mistral, Qwen, Gemma) at scale\n- Building an OpenAI-compatible API endpoint for self-hosted models\n- Optimizing LLM throughput and latency for production traffic\n- Running multi-GPU inference with tensor or pipeline parallelism\n- Deploying quantized models to reduce GPU memory requirements\n\n## Version and exposure boundary\n\nPin a reviewed vLLM release, model revision and container digest. Validate the exact release’s Python\u002FCUDA\u002Fhardware requirements and flags; the versions, memory figures and model\u002Fquantization examples below are historical sketches, not fit guarantees. Model weights, activations and KV cache must fit together. Keep examples loopback-only; remote service needs approved authentication, TLS, quotas and network policy. Never use the sample key as a real secret.\n\n## Prerequisites\n\n- NVIDIA GPU(s) with CUDA 12.1+ (A100\u002FH100 recommended for production)\n- Docker or Python 3.9+ with pip\n- 40GB+ VRAM for 70B models; 8GB+ for 7B models\n- `nvidia-container-toolkit` for Docker GPU passthrough\n\n## Quick Start\n\n```bash\n# Install vLLM\npip install vllm\n\n# Serve a model (OpenAI-compatible API)\nvllm serve meta-llama\u002FLlama-3.1-8B-Instruct \\\n  --host 127.0.0.1 \\\n  --port 8000 \\\n  --api-key your-secret-key\n\n# Test the endpoint\ncurl http:\u002F\u002Flocalhost:8000\u002Fv1\u002Fchat\u002Fcompletions \\\n  -H \"Content-Type: application\u002Fjson\" \\\n  -H \"Authorization: Bearer your-secret-key\" \\\n  -d '{\n    \"model\": \"meta-llama\u002FLlama-3.1-8B-Instruct\",\n    \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]\n  }'\n```\n\n## Docker Deployment\n\n```bash\ndocker run --runtime nvidia --gpus all \\\n  -v ~\u002F.cache\u002Fhuggingface:\u002Froot\u002F.cache\u002Fhuggingface \\\n  -p 127.0.0.1:8000:8000 \\\n  --ipc=host \\\n  vllm\u002Fvllm-openai:latest \\\n  --model meta-llama\u002FLlama-3.1-8B-Instruct \\\n  --api-key your-secret-key\n```\n\n## Docker Compose (Production)\n\n```yaml\nservices:\n  vllm:\n    image: vllm\u002Fvllm-openai:latest\n    runtime: nvidia\n    environment:\n      - NVIDIA_VISIBLE_DEVICES=all\n      - HUGGING_FACE_HUB_TOKEN=${HF_TOKEN}\n    volumes:\n      - model-cache:\u002Froot\u002F.cache\u002Fhuggingface\n    ports:\n      - \"127.0.0.1:8000:8000\"\n    ipc: host\n    command: >\n      --model meta-llama\u002FLlama-3.1-70B-Instruct\n      --tensor-parallel-size 2\n      --max-model-len 32768\n      --gpu-memory-utilization 0.90\n      --api-key ${VLLM_API_KEY}\n    restart: unless-stopped\n    healthcheck:\n      test: [\"CMD\", \"curl\", \"-f\", \"http:\u002F\u002Flocalhost:8000\u002Fhealth\"]\n      interval: 30s\n      timeout: 10s\n      retries: 3\n\nvolumes:\n  model-cache:\n```\n\n## Key Configuration Options\n\n### Multi-GPU Tensor Parallelism\n\n```bash\n# Split one model across 4 GPUs\nvllm serve meta-llama\u002FLlama-3.1-70B-Instruct \\\n  --tensor-parallel-size 4 \\\n  --gpu-memory-utilization 0.90\n```\n\n### Quantization (Lower VRAM)\n\n```bash\n# AWQ quantization (70B on 2x A100 40GB)\nvllm serve casperhansen\u002Fllama-3-70b-instruct-awq \\\n  --quantization awq \\\n  --tensor-parallel-size 2\n\n# GPTQ quantization\nvllm serve TheBloke\u002FLlama-2-70B-Chat-GPTQ \\\n  --quantization gptq\n\n# FP8 (H100 NVL native)\nvllm serve meta-llama\u002FLlama-3.1-405B-Instruct \\\n  --quantization fp8 \\\n  --tensor-parallel-size 8\n```\n\n### Structured Output & Tools\n\n```bash\nvllm serve meta-llama\u002FLlama-3.1-8B-Instruct \\\n  --enable-auto-tool-choice \\\n  --tool-call-parser llama3_json \\\n  --guided-decoding-backend outlines\n```\n\n### LoRA Adapters\n\n```bash\nvllm serve meta-llama\u002FLlama-3.1-8B-Instruct \\\n  --enable-lora \\\n  --lora-modules sql-lora=\u002Fpath\u002Fto\u002Fsql-lora \\\n                 code-lora=\u002Fpath\u002Fto\u002Fcode-lora \\\n  --max-lora-rank 64\n```\n\n## Performance Tuning\n\n```bash\n# Maximize throughput for batch workloads\nvllm serve REPLACE_WITH_REVIEWED_MODEL \\\n  --max-num-seqs 256 \\\n  --max-num-batched-tokens 8192 \\\n  --gpu-memory-utilization 0.95 \\\n  --swap-space 4                  # CPU swap (GiB)\n\n# Minimize latency for interactive use\nvllm serve REPLACE_WITH_REVIEWED_MODEL \\\n  --max-num-seqs 32 \\\n  --enforce-eager              # disable CUDA graph capture\n```\n\n## Benchmarking\n\n```bash\n# Install benchmark tool\npip install vllm\n\n# Run throughput benchmark\npython -m vllm.entrypoints.openai.run_batch \\\n  --model meta-llama\u002FLlama-3.1-8B-Instruct \\\n  --input-file prompts.jsonl \\\n  --output-file results.jsonl\n\n# Benchmark with vllm bench\nvllm bench throughput \\\n  --model meta-llama\u002FLlama-3.1-8B-Instruct \\\n  --num-prompts 1000 \\\n  --input-len 512 \\\n  --output-len 128\n```\n\n## Monitoring\n\n```bash\n# Check running server stats\ncurl http:\u002F\u002Flocalhost:8000\u002Fmetrics  # Prometheus metrics\n\n# Key metrics to watch:\n# vllm:num_requests_running       - active requests\n# vllm:gpu_cache_usage_perc       - KV cache utilization\n# vllm:generation_tokens_per_s    - throughput\n# vllm:time_to_first_token_ms     - TTFT latency\n# vllm:e2e_request_latency_seconds - end-to-end latency\n```\n\n## Common Issues\n\n| Issue | Cause | Fix |\n|-------|-------|-----|\n| `CUDA out of memory` | Model too large for VRAM | Add `--quantization awq` or reduce `--gpu-memory-utilization` |\n| Slow cold start | Model not cached | Pre-pull with `huggingface-cli download \u003Cmodel>` |\n| Low throughput | Too few concurrent requests | Increase `--max-num-seqs` |\n| KV cache full errors | Context length too long | Set `--max-model-len` lower |\n| `tokenizer error` | Tokenizer mismatch | Use `--tokenizer` to specify correct tokenizer |\n\n## Best Practices\n\n- Use `--gpu-memory-utilization 0.90` to leave headroom for CUDA kernels.\n- Pin model versions with `--revision` for reproducible deployments.\n- Set `HF_HUB_OFFLINE=1` in production to prevent unexpected downloads.\n- Use AWQ or GPTQ quantization before tensor parallelism — lower VRAM first.\n- Enable `--enable-chunked-prefill` for long-context workloads.\n- Monitor `gpu_cache_usage_perc` — above 95% causes queuing.\n\n## Related Skills\n\n- llm-inference-scaling (`llm-inference-scaling`) - Auto-scaling vLLM deployments\n- gpu-server-management (`gpu-server-management`) - GPU driver setup\n- llm-gateway (`llm-gateway`) - Load balancing across vLLM instances\n- llm-cost-optimization (`llm-cost-optimization`) - Cost management\n- model-serving-kubernetes (`model-serving-kubernetes`) - K8s deployment\n\n## Limitations\n\n- Infrastructure commands can disrupt services: confirm target host\u002Fscope and have backups\u002Fsnapshots before mutating state.\n- Docs-only import: upstream scripts and templates not bundled.\n\n",{"name":5,"risk":35,"source":52,"license":14,"category":53,"metadata":54,"date_added":57,"description":7,"source_repo":58,"source_type":29,"compatibility":59,"license_source":60},"https:\u002F\u002Fgithub.com\u002FBagelHole\u002FDevOps-Security-Agent-Skills","devops",{"author":55,"version":56},"devops-skills","1.0","2026-09-20","BagelHole\u002FDevOps-Security-Agent-Skills","Requires the relevant OS\u002Fplatform tooling and privileged access where noted. 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