The checkpoint is intended for speculative decoding with a compatible EAGLE-4 implementation and the target model; it is not a standalone text-generation model.
The model card describes EAGLE-4 as a flexible drafter intended to adapt its decoding strategy across batch sizes, hardware platforms, and workloads. In speculative decoding, the draft model proposes candidate tokens which are verified by the target model.
For a qualitative SPEED-Bench split at concurrency one, using four NVIDIA B300 GPUs with tensor parallelism four and a 4,096-token maximum output, the authors report 863.8 tokens per second and a 3.59x speedup over vanilla decoding at temperature zero. At temperature one, they report 608.6 tokens per second and a 2.59x speedup. These are author-reported results under a specified configuration; the source does not establish independent performance, performance on other hardware or workloads, or production availability. It also instructs users to check licenses for both the checkpoint and underlying Qwen3.8-27B weights.