Vllm speculative decoding. Oct 17, 2024 · Learn how speculative decoding in vLLM leverages smaller and larger models to accelerate token generation without sacrificing accuracy. In this blog, post we covered the basics of how speculative decoding works and how to implement it using vLLM. Aug 27, 2024 · A tool like speculative decoding can be a great solution while maintaining high output quality. g. May 1, 2025 · Yes, you can specify the speculative decoding method (like ngram) using the --speculative-config flag, passing a JSON string with parameters such as method, num_speculative_tokens, and prompt_lookup_max. 8. May 1, 2025 · At the time of publishing, Arctic Inference is the fastest speculative decoding solution for vLLM (v0. See examples of EAGLE and Draft Model-Based Speculative Decoding methods and how to evaluate their performance with Gen-AI Perf tool. Speculating with a draft model The following code configures vLLM in an offline mode to use speculative decoding with a draft model, speculating 5 tokens at a time. , FP8 quantization. Speculative decoding is a technique which improves inter-token latency in memory-bound LLM inference. . Mar 24, 2025 · In vLLM, speculative decoding has been supported to boost the serving efficiency, with various speculative algorithms and optimization techniques available, e. Explore different types of speculative decoding, their benefits and trade-offs, and how they work with vLLM's continuous batching system. May 8, 2025 · This page explains vLLM's implementation of speculative decoding, which can significantly reduce inference latency while maintaining the high-quality outputs of the target model. Learn how to use vLLM Backend to serve speculative decoding models for LLM inference with Triton Inference Server. This document shows how to use Speculative Decoding with vLLM. 4), significantly surpassing both the native N-gram and EAGLE speculators in vLLM v1 across several workloads. 2 for faster, efficient AI responses. Boost LLM inference speed with speculative decoding! Learn how to optimize token generation using draft models like Llama 3. This document shows how to use Speculative Decoding with vLLM. Currently, speculative decoding in vLLM is not compatible with pipeline parallelism. For a basic understanding and usage of speculative decoding, please refer to our previous blog: vLLM Speculative Decoding. pgka bicpj lmpqbg xlmna kndow rldofbj ikwe cgfqyrmm icaad gevj