EAGLE-4 adapts speculative decoding across hardware

EAGLE-4 makes speculative decoding feel more like production infrastructure than a fixed benchmark trick, though deployment remains specialized.

• –Dynamic tree sizing targets the real bottleneck: serving conditions change as concurrency and hardware load shift.
• –A low-rank candidate head reduces drafter overhead without truncating the vocabulary.
• –Reported gains reach 2.99× on single-request vLLM and 2.07× on SGLang at moderate concurrency. [Release benchmarks](https://github.com/eagle-hq/EAGLE-4/releases/tag/v0.1.0)
• –AMD support broadens the practical hardware path, with measured acceleration on MI355X GPUs. [AMD technical report](https://www.amd.com/en/developer/resources/technical-articles/2026/eagle-4-on-amd-instinct-gpus.html)
• –Creative AI platforms benefit indirectly through faster prompt expansion, orchestration, and tool-call loops—not through higher-quality image or video generation itself.