EAGLE: Speculative Sampling Requires Rethinking Feature Uncertainty
Auto-regressive decoding makes the inference of Large Language Models (LLMs)
time-consuming. We propose a simple framework, EAGLE (Extrapolation Algorithm
for Greater Language-model Efficiency), for lossless acceleration. Unlike
traditional speculative sampling methods, EAGLE operates the drafting process
auto-regressively at the more regular (second-top-layer) feature level and
addresses the sampling uncertainty issues in the next-feature prediction
problems by integrating tokens from one time step ahead. The acceleration
provided by EAGLE is lossless: it involves no fine-tuning of the target LLM,
and the generated text maintains the same distribution as that of vanilla
auto-regressive decoding. As of the submission of this paper, EAGLE is the
fastest known framework within the speculative sampling family. On MT-bench,
EAGLE is 3x faster than vanilla decoding, 2x faster than Lookahead, and 1.6x
faster than Medusa. Using gpt-fast, EAGLE attains on average 160 tokens/s with
LLaMA2-Chat 13B on a single RTX 3090 GPU, compared to 24 tokens/s of
Huggingface's implementations.
65b70e5e625ac670a7e8dcdf
Yuhui Li
65b70e5e625ac670a7e8dce0
Fangyun Wei
65b70e5e625ac670a7e8dce1
Chao Zhang
extracted_pending
2025-03-08T21:48:24.721Z
67ccb8fff68cfbec9dfbb658
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/HrIbjkCfydS7Ne2Cmj47E.png
Hongyang Zhang
user
hongyanz
65b70e5e625ac670a7e8dce2
Hongyang Zhang
EAGLE: Speculative Sampling Requires Rethinking Feature Uncertainty
Auto-regressive decoding makes the inference of Large Language Models (LLMs)
time-consuming. We propose a simple framework, EAGLE (Extrapolation Algorithm
for Greater Language-model Efficiency), for lossless acceleration. Unlike
traditional speculative sampling methods, EAGLE operates the drafting process
auto-regressively at the more regular (second-top-layer) feature level and
addresses the sampling uncertainty issues in the next-feature prediction
problems by integrating tokens from one time step ahead. The acceleration
provided by EAGLE is lossless: it involves no fine-tuning of the target LLM,
and the generated text maintains the same distribution as that of vanilla
auto-regressive decoding. As of the submission of this paper, EAGLE is the
fastest known framework within the speculative sampling family. On MT-bench,
EAGLE is 3x faster than vanilla decoding, 2x faster than Lookahead, and 1.6x
faster than Medusa. Using gpt-fast, EAGLE attains on average 160 tokens/s with
LLaMA2-Chat 13B on a single RTX 3090 GPU, compared to 24 tokens/s of
Huggingface's implementations.
EAGLE framework accelerates large language model inference by operating at a feature level with lossless performance relative to vanilla auto-regressive decoding.
auto-regressive decoding Large Language Models (LLMs) speculative sampling methods second-top-layer feature level lossless acceleration feature level sampling uncertainty next-feature prediction drafting process vanilla auto-regressive decoding MT-bench Lookahead Medusa gpt-fast LLaMA2-Chat 13B