According to a recent LinkedIn post from Together AI, the company is highlighting scheduling as a major bottleneck in long-context AI inference workloads. The post contrasts traditional inference queues, where large and small requests are treated identically, with newer approaches that exploit high cache reuse in multi-turn and long-context scenarios.
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The post describes an approach termed cache-aware prefill–decode disaggregation (CPD), which routes cold requests with new context through a prefill and caching phase while sending warm, cache-heavy requests directly to decode. According to the post, this routing can deliver roughly 40% higher sustainable throughput and materially lower time-to-first-token under mixed traffic conditions.
For investors, this emphasis on cache-aware scheduling suggests Together AI is targeting infrastructure-level optimizations that could reduce operating costs and improve service quality for long-context applications such as coding agents and document Q&A. If the claimed efficiency gains prove robust at scale, the company could strengthen its competitive position in AI infrastructure, support higher margins, and become more attractive to enterprise customers running intensive, conversational workloads.