Modal launches Auto Endpoints, citing speculative decoding for state-of-the-art inference latency

Modal is launching Modal Auto Endpoints, which bring Modal's infrastructure scalability to state-of-the-art inference performance with a single click while keeping control of the code. Modal says the main latency win comes from speculative decoding with custom DFlash speculator models, which cut about 100ms of end-to-end latency in a Decagon Voice case study, roughly 40% of server-side decode latency, making it over 60ms faster than the fastest proprietary alternative. Modal also released open source DFlash speculator models with Z Lab and SGLang, including one for Qwen 3.5 397B-A17B that improves on MTP by over 50%.