The Evolution of LLM Inference: Decoding algorithms — Part 1

2026-05-30 · Source: Towards AI - Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cloud Computing & & IT Infrastructure · Depth: Advanced, long

Summary

This article, "The Evolution of LLM Inference: Decoding algorithms — Part 1," details advanced decoding algorithms that reduce the number of sequential steps in Large Language Model (LLM) inference, moving beyond basic autoregressive generation. It explains classic speculative decoding, which uses a small draft model to propose tokens for a larger target model to verify in parallel, significantly cutting expensive forward passes. The piece then introduces tree speculative decoding, where a draft model proposes a tree of possible future tokens, verified by the target LLM using tree attention. Finally, it covers multi-head speculative decoding techniques like Medusa and Hydra, which integrate multiple prediction heads directly into the target LLM, eliminating the need for a separate draft model and simplifying deployment.

Key takeaway

For MLOps Engineers optimizing LLM serving, understanding advanced decoding algorithms is crucial for reducing inference latency. You should evaluate classic speculative decoding for predictable tasks, considering draft model size and acceptance rates. For interactive applications, explore multi-head methods like Medusa or Hydra if you can fine-tune the model, as they offer acceleration without a separate draft model, directly impacting time-to-first-token and inter-token latency.

Key insights

LLM inference speed hinges on reducing sequential decoding steps by predicting and verifying multiple tokens in parallel.

Principles

• Target model distribution must be preserved for lossless speculative decoding.
• Draft model accuracy and size are critical for high acceptance rates and speedup.
• Tree attention masks enable parallel verification of multiple candidate paths.

Method

Speculative decoding involves a draft model proposing tokens, which a target model verifies in parallel. Tree-based methods propose multiple branches, while multi-head techniques integrate prediction heads directly into the target LLM for draft-free operation.

In practice

• Classic speculative decoding excels in predictable tasks like code completion or summarization.
• Medusa/Hydra are suitable for interactive LLM serving where model modification is possible.
• Avoid speculative decoding for highly random outputs or when GPU is already saturated.

Topics

• LLM Inference Optimization
• Speculative Decoding
• Tree Speculative Decoding
• Multi-head Speculative Decoding
• Medusa
• Hydra

Best for: AI Engineer, NLP Engineer, Research Scientist, AI Scientist, Machine Learning Engineer, MLOps Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Towards AI - Medium.