DFlash 2: Keep Drafting Parallel

Inference is the bottleneck of the agent era. Agents read, plan, and call tools, often for hours or days. They consume tokens at a rate chat never approached. Every one of those tokens takes a full forward pass over the model. At Inco AI, we are building the inference stack scaled to the token economics of tomorrow. This post is a sneak peek. Our team released DFlash in January; it now runs in SGLang, vLLM, TensorRT-LLM, and llama.cpp. NVIDIA measured up to 15× throughput with it on Blackwell GPUs; Google reported 3× more tokens per second on TPUs; CoreWeave's production Kimi K2.7 Code endpoint, the fastest for that model on Artificial Analysis , runs DFlash by default. The ecosystem now builds on it: NVIDIA , Red Hat , and Modal have all published DFlash drafters; Meta ( Muse Glimmer ), Poolside ( Laguna ), Xiaomi ( MiMo-V2.5-Pro ), and NVIDIA ( Nemotron 3.5 Lightning ) ship official drafters with their own models. On Hugging Face, DFlash models have been downloaded more than 3.5 million times (as of August 2026). Speculative decoding is a core piece of the modern inference stack. 1 A small draft model guesses a block of tokens, and the target model verifies the whole block in one forward pass. Good guesses turn one pass into several tokens; bad ones just get thrown away. For years, though, the draft itself stayed autoregressive : one token at a time. DFlash made it one-pass too: the entire block, every position, predicted in parallel . DFlash 2 pushes parallel drafting…