TorchSpec Enables Disaggregated Speculative Decoding Training at Scale
zhyncs42 · x · 2026-07-30
The TorchSpec team published an article on their open-source project for large-scale disaggregated speculative decoding training. As frontier models like Kimi K2.5 and GLM 5 scale to hundreds of billions of parameters, efficient inference has become a critical systems challenge.
The article highlights that training draft models (e.g., EAGLE-3) requires transferring massive volumes of hidden states from the target model (a single 128K-token sample requires 7GB). Traditional methods of precomputing and storing these on disk create severe I/O bottlenecks. TorchSpec proposed and implemented a disaggregated training architecture to solve this. The project has been adopted by companies like DigitalOcean and CoreWeave, and Tencent Hunyuan has even extended it with a new algorithm called AngelSpec.
More from Infra
- Debunking the DeepSeek and Chinese Lithography Panic: Exaggerated Costs and Gaps — teortaxesTex · 2026-07-30
- Running Kimi K3 on CPU: Custom Q3 Quantization Takes 1.1TB, Hits 4.2 t/s — Fun-Meaning-6474 · 2026-07-30
- GPT-6 Expected to Autonomously Optimize Its Own Inference Compute — imjustnewatai · 2026-07-30
- Kimi K3 Available on Baseten with vLLM-Powered Production API — vllm_project · 2026-07-30
- Budget Inference Dilemma: 24GB GPU for Dense Models vs. RAM for MoE? — Agitated_Camel1886 · 2026-07-30
- Amazon and Microsoft to Spend $200B Each on AI Data Centers as Investors Demand Returns — luisdans · 2026-07-30