spb/tools-spboucher-ai

1{2  "slug": "vllm",3  "name": "vLLM",4  "aliases": [],5  "vendor": { "name": "vLLM project (originated at UC Berkeley Sky Computing Lab)", "slug": "vllm-project", "website": "https://vllm.ai" },6  "homepage_url": "https://vllm.ai/",7  "docs_url": "https://docs.vllm.ai/en/latest/",8  "repo_url": "https://github.com/vllm-project/vllm",9  "license_url": "https://github.com/vllm-project/vllm/blob/main/LICENSE",10  "product_type": "library",11  "product_status": "active",12  "summary": "High-throughput LLM inference and serving engine with PagedAttention, continuous batching, quantization and an OpenAI-compatible server for GPU and accelerator clusters",13  "description": "vLLM is an Apache-2.0 inference and serving engine for large language models, started at UC Berkeley's Sky Computing Lab and now maintained by a large community of academic and industry contributors. Its defining technique is PagedAttention, which manages the attention key-value cache in pages so that many requests can share GPU memory efficiently; combined with continuous batching, chunked prefill and prefix caching this gives far higher throughput than naive per-request inference. Further optimizations include speculative decoding (n-gram, EAGLE and others), FP8, INT8, INT4, GPTQ, AWQ and GGUF quantization, CUDA/HIP graphs and torch.compile.\n\nThe engine runs as a Python library or as an OpenAI-compatible HTTP server that also implements the Anthropic Messages API and gRPC, with streaming, structured output via grammar backends, tool-calling and reasoning parsers and multi-LoRA serving. It scales across tensor, pipeline, data, expert and context parallelism and supports disaggregated prefill and decode. Supported hardware covers NVIDIA and AMD GPUs, x86, ARM and PowerPC CPUs, Google TPUs, Intel Gaudi and several other accelerators, with Apple Silicon available through a hardware plugin. Over 200 Hugging Face architectures load directly, including mixture-of-experts, hybrid state-space, multimodal, embedding and reward models.\n\nvLLM is the usual choice when an open-weight model must serve many concurrent users on data-center GPUs, whether on premises or in a cloud. It is heavier than laptop-oriented runtimes: installation depends on matching CUDA or ROCm versions, memory is pre-allocated for the KV cache, and single-user latency on a workstation is not its design goal. Apple Silicon support is a community plugin rather than a primary target.",14  "capabilities": ["PagedAttention KV-cache management with continuous batching and prefix caching", "OpenAI-compatible server plus Anthropic Messages API and gRPC", "Quantization: FP8, INT8, INT4, GPTQ, AWQ, GGUF and others", "Tensor, pipeline, data, expert and context parallelism; disaggregated prefill/decode", "Speculative decoding, structured outputs, tool-calling and reasoning parsers", "Multi-LoRA serving for dense and MoE layers", "Support for 200+ Hugging Face model architectures including multimodal and embedding models", "NVIDIA, AMD, CPU, TPU, Gaudi and plugin-based hardware backends"],15  "workflows": ["Serve an open-weight chat model to a team behind an OpenAI-compatible endpoint", "Run offline batch generation over a large dataset with the Python API", "Deploy a quantized model across several GPUs with tensor parallelism", "Host embedding and reranking models alongside generation"],16  "advantages": ["High throughput and memory efficiency on GPUs", "Drop-in replacement for OpenAI-style clients", "Very wide model and hardware coverage", "Active community and frequent releases"],17  "limitations": ["Installation sensitive to CUDA/ROCm and PyTorch versions", "Designed for servers; not a laptop runtime", "Apple Silicon only through a plugin", "Pre-allocated KV cache requires tuning on small GPUs"],18  "prerequisites": ["Linux server with supported GPU or accelerator and matching drivers", "Python environment with PyTorch"],19  "audiences": ["ML platform and inference engineers", "Research groups serving models to many users", "Teams self-hosting open-weight models"],20  "alternatives": ["llama-cpp", "ollama", "transformers", "together-ai"],21  "comparison_notes": "llama.cpp and Ollama target single machines and CPU or consumer-GPU inference with GGUF weights, whereas vLLM targets concurrent serving on data-center accelerators from native Hugging Face checkpoints. Transformers can generate text but lacks vLLM's serving optimizations and is usually the upstream model definition vLLM loads. Together AI is a hosted alternative that removes operations work but keeps data off-premises.",22  "categories": [{ "slug": "local-inference", "primary": true }, { "slug": "distributed-computing", "label": "Multi-GPU and multi-node serving" }],23  "tags": ["llm", "open-source", "self-hosted", "api", "python", "serving", "gpu"],24  "use_cases": ["serve-models", "run-local-models"],25  "operating_systems": ["linux"],26  "execution_modes": ["self-hosted"],27  "interfaces": ["api", "library", "cli"],28  "deployment_models": ["on-premises", "managed-cloud"],29  "integrations": ["Hugging Face Hub", "Transformers", "PyTorch", "Kubernetes", "Ray", "LangChain", "LlamaIndex"],30  "data_formats": ["Safetensors", "GGUF", "GPTQ/AWQ checkpoints", "JSON"],31  "languages": ["Python", "C++", "CUDA"],32  "source_model": "open-source",33  "license_spdx": "Apache-2.0",34  "pricing_model": ["open-source", "free"],35  "self_hostable": true,36  "sources": [37    { "url": "https://vllm.ai/", "type": "official_site", "title": "vLLM", "supports": ["name", "summary"], "checked_at": "2026-11-02" },38    { "url": "https://docs.vllm.ai/en/latest/", "type": "official_docs", "title": "vLLM documentation", "supports": ["capabilities", "operating_systems", "interfaces"], "checked_at": "2026-11-02" },39    { "url": "https://github.com/vllm-project/vllm", "type": "repository", "supports": ["license_spdx", "languages"], "checked_at": "2026-11-02" },40    { "url": "https://github.com/vllm-project/vllm/blob/main/LICENSE", "type": "license_page", "supports": ["license_spdx", "source_model"], "checked_at": "2026-11-02" }41  ],42  "confidence": "high",43  "discovery_method": "editorial-research",44  "depth": "deep",45  "editorial_status": "reviewed",46  "visibility": "public",47  "featured": false,48  "author": "batch-A"49}50