Backend Management

vLLM is a fast and easy-to-use library for LLM inference and serving. Originally developed in the Sky Computing Lab at UC Berkeley, vLLM has evolved into a community-driven project with contributions from both academia and industry. vLLM is fast with: State-of-the-art serving throughput Efficient management of attention key and value memory with PagedAttention Continuous batching of incoming requests Fast model execution with CUDA/HIP graph Quantizations: GPTQ, AWQ, AutoRound, INT4, INT8, and FP8 Optimized CUDA kernels, including integration with FlashAttention and FlashInfer Speculative decoding Chunked prefill

vLLM is a fast and easy-to-use library for LLM inference and serving. Originally developed in the Sky Computing Lab at UC Berkeley, vLLM has evolved into a community-driven project with contributions from both academia and industry. vLLM is fast with: State-of-the-art serving throughput Efficient management of attention key and value memory with PagedAttention Continuous batching of incoming requests Fast model execution with CUDA/HIP graph Quantizations: GPTQ, AWQ, AutoRound, INT4, INT8, and FP8 Optimized CUDA kernels, including integration with FlashAttention and FlashInfer Speculative decoding Chunked prefill

vLLM is a fast and easy-to-use library for LLM inference and serving. Originally developed in the Sky Computing Lab at UC Berkeley, vLLM has evolved into a community-driven project with contributions from both academia and industry. vLLM is fast with: State-of-the-art serving throughput Efficient management of attention key and value memory with PagedAttention Continuous batching of incoming requests Fast model execution with CUDA/HIP graph Quantizations: GPTQ, AWQ, AutoRound, INT4, INT8, and FP8 Optimized CUDA kernels, including integration with FlashAttention and FlashInfer Speculative decoding Chunked prefill

vLLM is a fast and easy-to-use library for LLM inference and serving. Originally developed in the Sky Computing Lab at UC Berkeley, vLLM has evolved into a community-driven project with contributions from both academia and industry. vLLM is fast with: State-of-the-art serving throughput Efficient management of attention key and value memory with PagedAttention Continuous batching of incoming requests Fast model execution with CUDA/HIP graph Quantizations: GPTQ, AWQ, AutoRound, INT4, INT8, and FP8 Optimized CUDA kernels, including integration with FlashAttention and FlashInfer Speculative decoding Chunked prefill

vLLM is a fast and easy-to-use library for LLM inference and serving. Originally developed in the Sky Computing Lab at UC Berkeley, vLLM has evolved into a community-driven project with contributions from both academia and industry. vLLM is fast with: State-of-the-art serving throughput Efficient management of attention key and value memory with PagedAttention Continuous batching of incoming requests Fast model execution with CUDA/HIP graph Quantizations: GPTQ, AWQ, AutoRound, INT4, INT8, and FP8 Optimized CUDA kernels, including integration with FlashAttention and FlashInfer Speculative decoding Chunked prefill

vLLM is a fast and easy-to-use library for LLM inference and serving. Originally developed in the Sky Computing Lab at UC Berkeley, vLLM has evolved into a community-driven project with contributions from both academia and industry. vLLM is fast with: State-of-the-art serving throughput Efficient management of attention key and value memory with PagedAttention Continuous batching of incoming requests Fast model execution with CUDA/HIP graph Quantizations: GPTQ, AWQ, AutoRound, INT4, INT8, and FP8 Optimized CUDA kernels, including integration with FlashAttention and FlashInfer Speculative decoding Chunked prefill

vLLM is a fast and easy-to-use library for LLM inference and serving. Originally developed in the Sky Computing Lab at UC Berkeley, vLLM has evolved into a community-driven project with contributions from both academia and industry. vLLM is fast with: State-of-the-art serving throughput Efficient management of attention key and value memory with PagedAttention Continuous batching of incoming requests Fast model execution with CUDA/HIP graph Quantizations: GPTQ, AWQ, AutoRound, INT4, INT8, and FP8 Optimized CUDA kernels, including integration with FlashAttention and FlashInfer Speculative decoding Chunked prefill

vLLM is a fast and easy-to-use library for LLM inference and serving. Originally developed in the Sky Computing Lab at UC Berkeley, vLLM has evolved into a community-driven project with contributions from both academia and industry. vLLM is fast with: State-of-the-art serving throughput Efficient management of attention key and value memory with PagedAttention Continuous batching of incoming requests Fast model execution with CUDA/HIP graph Quantizations: GPTQ, AWQ, AutoRound, INT4, INT8, and FP8 Optimized CUDA kernels, including integration with FlashAttention and FlashInfer Speculative decoding Chunked prefill

vLLM is a fast and easy-to-use library for LLM inference and serving. Originally developed in the Sky Computing Lab at UC Berkeley, vLLM has evolved into a community-driven project with contributions from both academia and industry. vLLM is fast with: State-of-the-art serving throughput Efficient management of attention key and value memory with PagedAttention Continuous batching of incoming requests Fast model execution with CUDA/HIP graph Quantizations: GPTQ, AWQ, AutoRound, INT4, INT8, and FP8 Optimized CUDA kernels, including integration with FlashAttention and FlashInfer Speculative decoding Chunked prefill

vLLM is a fast and easy-to-use library for LLM inference and serving. Originally developed in the Sky Computing Lab at UC Berkeley, vLLM has evolved into a community-driven project with contributions from both academia and industry. vLLM is fast with: State-of-the-art serving throughput Efficient management of attention key and value memory with PagedAttention Continuous batching of incoming requests Fast model execution with CUDA/HIP graph Quantizations: GPTQ, AWQ, AutoRound, INT4, INT8, and FP8 Optimized CUDA kernels, including integration with FlashAttention and FlashInfer Speculative decoding Chunked prefill

vLLM is a fast and easy-to-use library for LLM inference and serving. Originally developed in the Sky Computing Lab at UC Berkeley, vLLM has evolved into a community-driven project with contributions from both academia and industry. vLLM is fast with: State-of-the-art serving throughput Efficient management of attention key and value memory with PagedAttention Continuous batching of incoming requests Fast model execution with CUDA/HIP graph Quantizations: GPTQ, AWQ, AutoRound, INT4, INT8, and FP8 Optimized CUDA kernels, including integration with FlashAttention and FlashInfer Speculative decoding Chunked prefill

vLLM is a fast and easy-to-use library for LLM inference and serving. Originally developed in the Sky Computing Lab at UC Berkeley, vLLM has evolved into a community-driven project with contributions from both academia and industry. vLLM is fast with: State-of-the-art serving throughput Efficient management of attention key and value memory with PagedAttention Continuous batching of incoming requests Fast model execution with CUDA/HIP graph Quantizations: GPTQ, AWQ, AutoRound, INT4, INT8, and FP8 Optimized CUDA kernels, including integration with FlashAttention and FlashInfer Speculative decoding Chunked prefill