Achieve state-of-the-art inference latencies with speculative decoding

Modal has launched Auto Endpoints, a deployment offering designed to combine scalable infrastructure with low-latency AI inference while preserving user control over code. The company argues that speculative decoding, particularly when supported by Blackwell GPUs, SGLang, regional Modal Servers, and optimized kernels, is the most consequential latency optimization because decoding typically dominates end-to-end inference time. In a collaboration with AI agent platform Decagon, Modal reduced a voice-agent inference subsystem’s median latency from roughly 290 milliseconds by about 100 milliseconds, ultimately outperforming proprietary providers for the same workload by more than 60 milliseconds. The largest gains came from DFlash speculative decoding models customized through “mid-training” on task-specific synthetic data, which improved token-acceptance rates and removed about 40% of server-side decode latency beyond an already optimized baseline. Modal also describes improving communication routing, GPU prefill kernels, and inference-engine host overhead, while positioning Auto Endpoints as a self-service option for teams seeking both performance and operational flexibility.

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