following advanced vLLM features: - Prefix caching (--enable-prefix-caching) - Chunked prefill (--enable-chunked-prefill) Table of contents: - Requirements - Quick start using Dockerfile - Build from is turned off. To enable better TPOT / TTFT latency, you can use vLLM's chunked prefill feature (--enable-chunked-prefill). Based on the experiments, the recommended batch size is 256 (--max-num-batched-tokens) -chat- →hf --dataset vllm/benchmarks/ShareGPT_V3_unfiltered_cleaned_split.json --enable- →chunked-prefill --max-num-batched-tokens 256 ``` ## 1.3.5 Limitations - LoRA serving is not supported. - Only

following advanced vLLM features: - Prefix caching (--enable-prefix-caching) - Chunked prefill (--enable-chunked-prefill) Table of contents: - Requirements - Quick start using Dockerfile - Build from is turned off. To enable better TPOT / TTFT latency, you can use vLLM's chunked prefill feature (--enable-chunked-prefill). Based on the experiments, the recommended batch size is 256 (--max-num-batched-tokens) -chat- →hf --dataset vllm/benchmarks/ShareGPT_V3_unfiltered_cleaned_split.json --enable- →chunked-prefill --max-num-batched-tokens 256 ``` ## 1.3.5 Limitations - LoRA serving is not supported. - Only

following advanced vLLM features: - Prefix caching (--enable-prefix-caching) - Chunked prefill (--enable-chunked-prefill) Table of contents: - Requirements - Quick start using Dockerfile - Build from is turned off. To enable better TPOT / TTFT latency, you can use vLLM's chunked prefill feature (--enable-chunked-prefill). Based on the experiments, the recommended batch size is 256 (--max-num-batched-tokens) -chat- →hf --dataset vllm/benchmarks/ShareGPT_V3_unfiltered_cleaned_split.json --enable- →chunked-prefill --max-num-batched-tokens 256 ``` ## 1.3.5 Limitations - LoRA serving is not supported. - Only

kernels, including integration with FlashAttention and FlashInfer. - Speculative decoding - Chunked prefill vLLM is flexible and easy to use with: - Seamless integration with popular HuggingFace models following advanced vLLM features: - Prefix caching (--enable-prefix-caching) - Chunked prefill (--enable-chunked-prefill) Table of contents: - Requirements - Quick start using Dockerfile - Build from as To enable better TPOT / TTFT latency, you can use vLLM's chunked prefill feature (--enable-chunked-prefill). Based on the experiments, the recommended batch size is 256 (--max-num-batched-tokens)

following advanced vLLM features: - Prefix caching (--enable-prefix-caching) - Chunked prefill (--enable-chunked-prefill) Table of contents: - Requirements - Quick start using Dockerfile - Build from is turned off. To enable better TPOT / TTFT latency, you can use vLLM's chunked prefill feature (--enable-chunked-prefill). Based on the experiments, the recommended batch size is 256 (--max-num-batched-tokens) -chat- →hf --dataset vllm/benchmarks/ShareGPT_V3_unfiltered_cleaned_split.json --enable- →chunked-prefill --max-num-batched-tokens 256 ``` ## 1.3.5 Limitations - LoRA serving is not supported. -

following advanced vLLM features: - Prefix caching (--enable-prefix-caching) - Chunked prefill (--enable-chunked-prefill) Table of contents: - Requirements - Quick start using Dockerfile - Build from is turned off. To enable better TPOT / TTFT latency, you can use vLLM's chunked prefill feature (--enable-chunked-prefill). Based on the experiments, the recommended batch size is 256 (--max-num-batched-tokens) -chat- →hf --dataset vllm/benchmarks/ShareGPT_V3_unfiltered_cleaned_split.json --enable- →chunked-prefill --max-num-batched-tokens 256 ``` ## 1.3.5 Limitations - LoRA serving is not supported. - Only

kernels, including integration with FlashAttention and FlashInfer. - Speculative decoding - Chunked prefill vLLM is flexible and easy to use with: - Seamless integration with popular HuggingFace models following advanced vLLM features: - Prefix caching (--enable-prefix-caching) - Chunked prefill (--enable-chunked-prefill) Table of contents: - Requirements - Quick start using Dockerfile - Build from as To enable better TPOT / TTFT latency, you can use vLLM's chunked prefill feature (--enable-chunked-prefill). Based on the experiments, the recommended batch size is 256 (--max-num-batched-tokens)

→SPECULATIVE_DISABLE_BY_BATCH_SIZE] [--scheduler-delay-factor SCHEDULER_DELAY_ [--enable-chunked-prefill] [--speculative-model SPECULATIVE_MODEL] [--num-speculative-tokens NUM_SPECULATIVE_ [--sp previousprompt latency) before scheduling next prompt. Default: 0.0 --enable-chunked-prefill If set, the prefill requests can be chunked based on the max_num_batched_tokens. Default: False --speculative-model counter_prompt_tokens = Counter( name="vllm:prompt_tokens_total", documentation="Number of prefill tokens processed.", labelnames=labelnames) self.counter_generation_tokens = Counter(

kernels, including integration with FlashAttention and FlashInfer. - Speculative decoding - Chunked prefill vLLM is flexible and easy to use with: - Seamless integration with popular HuggingFace models following advanced vLLM features: - Prefix caching (--enable-prefix-caching) - Chunked prefill (--enable-chunked-prefill) Table of contents: - Requirements - Quick start using Dockerfile - Build from as To enable better TPOT / TTFT latency, you can use vLLM's chunked prefill feature (--enable-chunked-prefill). Based on the experiments, the recommended batch size is 256 (--max-num-batched-tokens)

previousprompt latency) before scheduling next prompt. Default: 0.0 --enable-chunked-prefill If set, the prefill requests can be chunked based on the max_num_batched_tokens. Default: False --speculative-model counter_prompt_tokens = Counter( name="vllm:prompt_tokens_total", documentation="Number of prefill tokens processed.", labelnames=labelnames) self.counter_generation_tokens = Counter( = Histogram( name="vllm:request_prompt_tokens", documentation="Number of prefill tokens processed.", labelnames=labelnames, buckets=build_1_2_5_buckets(max_model_len)