SpecForge: A Flexible and Efficient Open-Source Training Framework for Speculative Decoding
Large language models incur high inference latency due to sequential autoregressive decoding. Speculative decoding alleviates this bottleneck by using a lightweight draft model to propose multiple tokens for batched verification. However, its adoption has been limited by the lack of high-quality draft models and scalable training infrastructure. We introduce SpecForge, an open-source, production-oriented framework for training speculative decoding models with full support for EAGLE-3. SpecForge incorporates target-draft decoupling, hybrid parallelism, optimized training kernels, and integration with production-grade inference engines, enabling up to 9.9x faster EAGLE-3 training for Qwen3-...
| Authors | Shenggui Li, Chao Wang, Yikai Zhu et al. |
|---|---|
| Primary category | cs.LG |
| Published | 19 March 2026 |
| Last updated at source | 19 March 2026 |
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