vLLM Tops the Artificial Analysis Leaderboard
·15 min read
How vLLM built the leading deployments of DeepSeek V3.2, MiniMax-M2.5, and Qwen 3.5 397B.
#speculative-decoding
6 posts
Extracting hidden states from vLLM
·8 min read
PR #33736 (included in vllm>=v0.18.0) introduced a new hidden states extraction system to vLLM. This blog post explores the motivation, design, usage, and future direction of this feature, and its...
P-EAGLE: Faster LLM inference with Parallel Speculative Decoding in vLLM
·12 min read
EAGLE is the state-of-the-art method for speculative decoding in large language model (LLM) inference, but its autoregressive drafting creates a hidden bottleneck: the more tokens that you...
Diving into speculative decoding training support for vLLM with Speculators v0.3.0
·11 min read
- Speculative decoding serves as an optimization to improve inference performance; however, training a unique draft model for each LLM can be difficult and time-consuming, while production-ready...
Inside vLLM: Anatomy of a High-Throughput LLM Inference System
·41 min read
In this post, I'll gradually introduce all of the core system components and advanced features that make up a modern high-throughput LLM inference system. In particular I'll be doing a breakdown...
How Speculative Decoding Boosts vLLM Performance by up to 2.8x
·10 min read
Speculative decoding in vLLM is a powerful technique that accelerates token generation by leveraging both small and large models in tandem. In this blog, we’ll break down speculative decoding in...