Inference Optimization Platform for Open-Weight Models
P6/10May 5, 2026
WhatA managed platform that automatically applies the best inference acceleration techniques (MTP drafters, speculative decoding, quantization) to any open-weight model, delivering maximum tokens-per-second with one API call.
SignalMulti-token prediction and speculative decoding deliver major speed gains with negligible quality loss, but integrating these techniques requires deep expertise in model architecture, drafter training, and serving infrastructure that most teams lack.
Why NowMTP and speculative decoding are just now becoming practical for production use, with Google, NVIDIA, and others releasing drafter models and reference implementations in mid-2026, but tooling to easily adopt them is fragmented.
MarketAI application developers and enterprises self-hosting models; TAM overlaps with the ~$10B+ inference infrastructure market; competes with vLLM, TensorRT-LLM, but none offer turnkey drafter-model optimization as a service.
MoatAccumulating performance benchmarks and optimized drafter-model pairings across hundreds of model variants creates a compounding data and engineering moat.
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