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Eagle AI has officially released EAGLE-4 on GitHub with inference code and a paper; the paper reports 3.59x the throughput of vanilla decoding on 4x NVIDIA B300 GPUs at concurrency 1, matching the forum claim.
Unconfirmed → Confirmed
- Confirmed
Eagle AI has released EAGLE-4, the latest version of the EAGLE speculative-decoding method, with open inference code, a paper and a draft model for Qwen3.8-27B. One drafter adjusts how far ahead it speculates to the batch size, hardware and workload while keeping the target model's output distribution. In the paper, EAGLE-4 delivers 3.59x the throughput of vanilla decoding on 4x NVIDIA B300 GPUs at concurrency 1, a 2.71x average speedup on a 4-vCPU AWS c5.xlarge instance, and a 3.62x speedup on HumanEval on a Snapdragon 8 Elite smartphone.
Source: Eagle AI (GitHub) · Official story → - Unconfirmed
A post on a local-LLM forum says EAGLE-4 has been released and lists a 3.59x speedup over vanilla decoding on Qwen 3.8 27B, ahead of DFlash 2 at 2.80x, DSpark at 2.57x and MTP at 2.22x. Only Qwen 3.8 27B is said to be supported so far.
Source: r/LocalLLaMA
- Has Eagle AI confirmed it?
- Yes. Eagle AI confirmed it on Oct 20.
- Eagle AI releases EAGLE-4, a speculative-decoding drafter that adapts to batch size and hardware Oct 21 brief
- Unconfirmed
- Circulating with at least one public source we can link to; no official confirmation.
- Reported
- Reported by a news outlet; not confirmed by the company.
- Confirmed
- Confirmed by the company or another official source.
- Debunked
- Denied by the company, retracted by the original source, or shown to be false by the evidence.
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