EAGLE-4 Technical Deepdive
fal and Fireworks publish a technical analysis of EAGLE-4, a speculative decoding method, with a public dataset of 400 creative prompts. On NVIDIA B300 GPUs, the EAGLE-4 Harness reaches 622.9 tokens per second at batch size 1, 1.85x the fastest alternative.
What the article says
fal, in collaboration with Fireworks, has published a technical analysis of EAGLE-4 and a public dataset, Prompt Enhancement SpecBench, for benchmarking speculative decoding on creative tasks. Speculative decoding uses a small drafter that proposes tokens which the target model verifies together, without changing the output distribution. The prompt-enhancement step of image and video models takes a growing share of latency as generators get faster.
EAGLE-4 extends EAGLE-3 with sliding-window attention and a low-rank candidate head; verification runs over a tree of branches with a harness that adapts to the GPU. On NVIDIA B300 GPUs, the authors report 660.6 tokens per second on the Fireworks bench, 1.63 times the fastest alternative, and a 21% to 26% acceptance-length gain for the tree over the chain at a 16-token budget. The dataset has 400 examples drawn from 18 datasets.
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