OneTriangle押注KV缓存传输可大幅降低长上下文推理成本

做的是什么生意

An AI inference startup serving open-weight models (DeepSeek V4 Flash, Qwen3.6 27B) from managed GPU stacks, while developing cross-model KV-cache transfer so a small model prefills prompts and a larger model decodes from the transferred cache.

启动资金：$125,000 micro-seed from Y Combinator (June 2026).

起因

Hannah Chung (CEO) and Medha Venkatapathy (CTO), MIT CSAIL graduates, co-founded OneTriangle (initially TrustAI) in 2026 with three other MIT engineers whose backgrounds span Google DeepMind, Jane Street and MIT Lincoln Laboratory. Their premise: inference has surpassed training as AI's largest compute cost, and for long-context and agent workloads prefill dominates both cost and latency — a hidden tax every serving team pays.

经过

Accepted into Y Combinator's Summer 2026 batch, OneTriangle raised a $125,000 micro-seed in June 2026. An August 18 research note reported a learned Minitron 4B-to-Llama 3.1 8B handoff: an 8,192-token prompt reached the larger model's first token in 38.25ms with the small model's cache resident versus 302.69ms native prefill (7.9x), at 82.52% top-token agreement over 32 steps. The company claims 20% lower prefill costs and 40% faster time-to-first-token versus present standards and says it is upstreaming the technique into vLLM. On August 24 it launched hosted DeepSeek V4 Flash at $0.15 per million input and $0.35 per million output tokens on an eight-H100 stack, with a delayed tier 40% lower.

结果

Live but unproven: the hosted model service is publicly priced and running, while the cache-transfer technique remains research-stage; OneTriangle's own benchmarks show the headline 7.9x first-token speedup falls to 1.25x when the small model's read is included, and no independent verification or named customer has been published.

可借鉴之处

当所有人都转售相同的开源权重时，服务性能是唯一的切入点——但实验室里的加速若端到端缩水且仅自测，则尚未成为护城河。