vLLM the vLLM Team ## GETTING STARTED 1 Documentation 3 2 Indices and tables 91 Python Module Index 93 Index 95 ## LLM vLLM is a fast and easy-to-use library for LLM inference and serving. vLLM with CUDA/HIP graph - Quantization: GPTQ, AWQ, SqueezeLLM, FP8 KV Cache - Optimized CUDA kernels vLLM is flexible and easy to use with: - Seamless integration with popular HuggingFace models - High-throughput Multi-lora support For more information, check out the following: - vLLM announcing blog post (intro to PagedAttention) - vLLM paper (SOSP 2023) - How continuous batching enables 23x throughput in

vLLM the vLLM Team ## GETTING STARTED 1 Documentation 3 2 Indices and tables 93 Python Module Index 95 Index 97 ## LLM vLLM is a fast and easy-to-use library for LLM inference and serving. vLLM with CUDA/HIP graph - Quantization: GPTQ, AWQ, SqueezeLLM, FP8 KV Cache - Optimized CUDA kernels vLLM is flexible and easy to use with: - Seamless integration with popular HuggingFace models - High-throughput Multi-lora support For more information, check out the following: - vLLM announcing blog post (intro to PagedAttention) - vLLM paper (SOSP 2023) - How continuous batching enables 23x throughput in

vLLM the vLLM Team ## GETTING STARTED 1 Documentation 3 2 Indices and tables 191 Python Module Index 193 Index 195 ## LLM vLLM is a fast and easy-to-use library for LLM inference and serving. vLLM including integration with FlashAttention and FlashInfer. - Speculative decoding - Chunked prefill vLLM is flexible and easy to use with: - Seamless integration with popular HuggingFace models - High-throughput Multi-lora support For more information, check out the following: - vLLM announcing blog post (intro to PagedAttention) - vLLM paper (SOSP 2023) - How continuous batching enables 23x throughput in

vLLM the vLLM Team ## GETTING STARTED 1 Documentation 3 2 Indices and tables 113 Python Module Index 115 Index 117 ## LLM vLLM is a fast and easy-to-use library for LLM inference and serving. vLLM with CUDA/HIP graph - Quantization: GPTQ, AWQ, SqueezeLLM, FP8 KV Cache - Optimized CUDA kernels vLLM is flexible and easy to use with: - Seamless integration with popular HuggingFace models - High-throughput Multi-lora support For more information, check out the following: - vLLM announcing blog post (intro to PagedAttention) - vLLM paper (SOSP 2023) - How continuous batching enables 23x throughput in

vLLM the vLLM Team ## GETTING STARTED 1 Documentation 3 2 Indices and tables 205 Python Module Index 207 Index 209 ## LLM vLLM is a fast and easy-to-use library for LLM inference and serving. vLLM including integration with FlashAttention and FlashInfer. - Speculative decoding - Chunked prefill vLLM is flexible and easy to use with: - Seamless integration with popular HuggingFace models - High-throughput Multi-lora support For more information, check out the following: - vLLM announcing blog post (intro to PagedAttention) - vLLM paper (SOSP 2023) - How continuous batching enables 23x throughput in

vLLM the vLLM Team ## GETTING STARTED 1 Documentation 3 2 Indices and tables 217 Python Module Index 219 Index 221 ## LLM vLLM is a fast and easy-to-use library for LLM inference and serving. vLLM including integration with FlashAttention and FlashInfer. - Speculative decoding - Chunked prefill vLLM is flexible and easy to use with: - Seamless integration with popular HuggingFace models - High-throughput Multi-lora support For more information, check out the following: - vLLM announcing blog post (intro to PagedAttention) - vLLM paper (SOSP 2023) - How continuous batching enables 23x throughput in

vLLM the vLLM Team ## GETTING STARTED 1 Documentation 3 2 Indices and tables 123 Python Module Index 125 Index 127 ## LLM vLLM is a fast and easy-to-use library for LLM inference and serving. vLLM with CUDA/HIP graph - Quantization: GPTQ, AWQ, SqueezeLLM, FP8 KV Cache - Optimized CUDA kernels vLLM is flexible and easy to use with: - Seamless integration with popular HuggingFace models - High-throughput Multi-lora support For more information, check out the following: - vLLM announcing blog post (intro to PagedAttention) - vLLM paper (SOSP 2023) - How continuous batching enables 23x throughput in

vLLM the vLLM Team ## GETTING STARTED 1 Documentation 3 2 Indices and tables 153 Python Module Index 155 Index 157 ## LLM vLLM is a fast and easy-to-use library for LLM inference and serving. vLLM with CUDA/HIP graph - Quantization: GPTQ, AWQ, SqueezeLLM, FP8 KV Cache - Optimized CUDA kernels vLLM is flexible and easy to use with: - Seamless integration with popular HuggingFace models - High-throughput Multi-lora support For more information, check out the following: - vLLM announcing blog post (intro to PagedAttention) - vLLM paper (SOSP 2023) - How continuous batching enables 23x throughput in

vLLM the vLLM Team ## GETTING STARTED 1 Documentation 3 2 Indices and tables 135 Python Module Index 137 Index 139 ## LLM vLLM is a fast and easy-to-use library for LLM inference and serving. vLLM with CUDA/HIP graph - Quantization: GPTQ, AWQ, SqueezeLLM, FP8 KV Cache - Optimized CUDA kernels vLLM is flexible and easy to use with: - Seamless integration with popular HuggingFace models - High-throughput Multi-lora support For more information, check out the following: - vLLM announcing blog post (intro to PagedAttention) - vLLM paper (SOSP 2023) - How continuous batching enables 23x throughput in

vLLM the vLLM Team ## GETTING STARTED 1 Documentation 3 2 Indices and tables 143 Python Module Index 145 Index 147 ## LLM vLLM is a fast and easy-to-use library for LLM inference and serving. vLLM with CUDA/HIP graph - Quantization: GPTQ, AWQ, SqueezeLLM, FP8 KV Cache - Optimized CUDA kernels vLLM is flexible and easy to use with: - Seamless integration with popular HuggingFace models - High-throughput Multi-lora support For more information, check out the following: - vLLM announcing blog post (intro to PagedAttention) - vLLM paper (SOSP 2023) - How continuous batching enables 23x throughput in