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Use when you need to run training or batch jobs across multiple clouds, leverage spot instances with auto-recovery, or optimize GPU costs across providers.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Finfrastructure-skypilot\u002FSKILL.md",[],[457,458,459,253,460,461,215,277,317,319,462,463,464,465,466,281,467,468,469,470,471,287,472,266],"infrastructure","infrastructure skypilot","SkyPilot","orchestration","cost optimization","batch","cloud","Optimization","multi-cloud","recovery","auto","cost","instances","leverage","you","automatic",{"skillNo":474,"name":475,"nameZh":18,"description":476,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":477,"categories":478,"keywords":479,"publishedAt":236},"01M6ADR29HTR5RGY443BNPAZ22","infrastructure-modal","Serverless GPU cloud platform for running ML workloads. Use when you need on-demand GPU access without infrastructure management, deploying ML models as APIs, or running batch jobs with automatic scaling.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Finfrastructure-modal\u002FSKILL.md",[],[457,480,481,253,215,482,319,462,463,483,484,281,485,423,486,487,471,286,287,472,266,488,489],"infrastructure modal","modal","deployment","GPU Cloud","access","scaling","as","infrastructure management","running","On-Demand GPU",{"skillNo":491,"name":492,"nameZh":18,"description":493,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":494,"categories":495,"keywords":496,"publishedAt":236},"01M6ADQZ07Y485PKZQFWQ72PWT","infrastructure-lambda-labs","Reserved and on-demand GPU cloud instances for ML training and inference. Use when you need dedicated GPU instances with simple SSH access, persistent filesystems, or high-performance multi-node clusters for large-scale training.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Finfrastructure-lambda-labs\u002FSKILL.md",[],[457,497,498,499,500,253,215,277,319,463,501,483,502,484,503,281,469,504,505,471,286,287,266,489,506],"infrastructure lambda labs","lambda","inference","performance","Large-Scale Training","Lambda Labs","node","high-performance","Multi-Node","Clusters",{"skillNo":508,"name":509,"nameZh":18,"description":510,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":511,"categories":512,"keywords":516,"publishedAt":236},"01M6ADQVXMCK6RD2RXYMXKM7GX","inference-serving-vllm","Serves LLMs with high throughput using vLLM's PagedAttention and continuous batching. Use when deploying production LLM APIs, optimizing inference latency\u002Fthroughput, or serving models with limited GPU memory. Supports OpenAI-compatible endpoints, quantization (GPTQ\u002FAWQ\u002FFP8), and tensor parallelism.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Finference-serving-vllm\u002FSKILL.md",[513],{"categoryNo":514,"categoryName":515,"taxonomyType":11,"taxonomyTypeDesc":12},"01JBSEEDCAT000000000000010","后端与接口",[499,517,518,519,234,520,521,253,125,522,319,523,524,300,525,526,527,528,529,530,286,531,266],"Inference Serving","inference serving vllm","vLLM","llms","memory","openai api","tensor","FP8","api","latency","AWQ","GPTQ","throughput","openai-compatible","batching",{"skillNo":533,"name":534,"nameZh":18,"description":535,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":536,"categories":537,"keywords":538,"publishedAt":236},"01M6ADQSBJ0W6SCS00023ZG114","inference-serving-tensorrt-llm","Optimizes LLM inference with NVIDIA TensorRT for maximum throughput and lowest latency. Use for production deployment on NVIDIA GPUs (A100\u002FH100), when you need 10-100x faster inference than PyTorch, or for serving models with quantization (FP8\u002FINT4), in-flight batching, and multi-GPU scaling.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Finference-serving-tensorrt-llm\u002FSKILL.md",[],[499,517,539,540,234,541,253,220,482,319,464,524,542,485,526,543,529,471,286,287,531,266],"inference serving tensorrt llm","tensorrt llm","tensorrt","NVIDIA","H100",{"skillNo":545,"name":546,"nameZh":18,"description":547,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":548,"categories":549,"keywords":551,"publishedAt":236},"01M6ADQNC9S4G3CEKPE9M1G301","inference-serving-sglang","Fast structured generation and serving for LLMs with RadixAttention prefix caching. Use for JSON\u002Fregex outputs, constrained decoding, agentic workflows with tool calls, or when you need 5× faster inference than vLLM with prefix sharing. Powers 300,000+ GPUs at xAI, AMD, NVIDIA, and LinkedIn.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Finference-serving-sglang\u002FSKILL.md",[550],{"categoryNo":311,"categoryName":312,"taxonomyType":11,"taxonomyTypeDesc":12},[499,552,234,553,520,253,554,555,556,542,517,557,558,559,560,561,562,563,564,300,565,261,471,566,266],"inference serving sglang","agentic","workflows","agentic workflows","fast","SGLang","Structured Generation","RadixAttention","Prefix Caching","Constrained Decoding","JSON Output","Fast Inference","Production Scale","xai","at",{"skillNo":568,"name":569,"nameZh":18,"description":570,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":571,"categories":572,"keywords":573,"publishedAt":236},"01M6ADQHW3BVTD43RSFYBX5DP6","inference-serving-llama-cpp","Runs LLM inference on CPU, Apple Silicon, and consumer GPUs without NVIDIA hardware. Use for edge deployment, M1\u002FM2\u002FM3 Macs, AMD\u002FIntel GPUs, or when CUDA is unavailable. Supports GGUF quantization (1.5-8 bit) for reduced memory and 4-10× speedup vs PyTorch on CPU.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Finference-serving-llama-cpp\u002FSKILL.md",[],[499,574,575,576,517,577,234,578,521,253,220,482,579,542,580,581,582,583,584,585,586,587,588,589,266],"cpp","Llama","inference serving llama cpp","llama cpp","python","edge","apple","bit","silicon","CPU","GGUF","llama.cpp","CPU Inference","Edge Deployment","Non-NVIDIA","AMD GPUs",189,18]