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EAGLE, Medusa or a separate draft model: which speculative decoding method do you pick, and why?

Compare proposal methods by committed output over complete round time, with explicit sampling guarantees and target compatibility.

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Compare proposal methods by committed output over complete round time, with explicit sampling guarantees and target compatibility.

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Work through the numbers

Change the assumptions in a free calculator, then explain what the result establishes and what still needs measurement.

The concepts behind this question

Use these concept explanations to review the ideas behind this question.

Advanced

🚀 Inference & Serving🔒 Premium

Speculative DecodingA draft proposes tokens; the target checks them with a sampling rule that preserves its distribution. Work through the correction after rejection, count accepted drafts separately from emitted tokens, and decide whether the measured round cost earns a speedup.

Advanced

🚀 Inference & Serving🔒 Premium

Hybrid State Checkpoints and RollbackA hybrid model must restore attention history and recurrent state to the same token boundary. Learn why a cache hit can require replay, how rejected draft tokens corrupt an in-place state, and what saved checkpoints cost.

Foundational

🚀 Inference & Serving

Prefill vs DecodeFollow one prompt through its first prediction and later decode steps, then separate GPU work from the wait a user sees. Learn why batch size and context change the likely bottleneck.

Foundational

🚀 Inference & Serving

The KV CacheThe KV cache saves attention keys and values from processed tokens. Follow one decode step, calculate its memory cost, and see why longer conversations reduce the number of requests a GPU can hold.

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FEDITOR'S NOTE

Count committed output over the entire speculation round, including drafting and verification. State which acceptance procedure supports the claimed sampling guarantee.

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