SafeAILab/EAGLE

Health v2 · maintenance only

Flags: no_releases no_license

Adoption not part of the score

2510 stars · 296 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

EAGLE is the official implementation of the EAGLE family of speculative decoding algorithms (EAGLE-1/2/3) for accelerating LLM text generation. It uses a lightweight draft model that extrapolates the target LLM's feature vectors to draft tokens, achieving 2-4x lossless speedups over vanilla decoding.

Use cases

• speed up llm inference
• speculative decoding for large language models
• reduce llm generation latency
• faster text generation without quality loss
• accelerate autoregressive decoding
• deploy llms with lower inference cost

When to choose

• you need lossless LLM decoding speedups with provable output distribution consistency
• you want the fastest speculative decoding method and can train a draft model on modest GPUs
• you use supported models and want compatibility with vLLM, FlashAttention, or quantization

When to avoid

• you need plug-and-play inference without training a draft model for your specific LLM
• your model architecture is not among the supported target models
• you only need small-model inference where decoding is not the bottleneck

Facets

library · maturity active

llm-inference machine-learning gpu-computing large-language-models machine-learning deep-learning performance python speculative-decoding llm-acceleration draft-model lossless-decoding inference-optimization gpu linux docker

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