Together AI says LLM inference bottlenecks depend on traffic shape
Overview
Together AI says the limiting factor in LLM performance depends on the traffic pattern.
Short inputs with long outputs are limited by decode speed, while long prompts are limited by prefill. The company says tool-heavy agents are limited by end-to-end latency and workloads with repeated turns by token economics. Together AI advises users to measure the bottleneck their own workload creates rather than assume one.
Written by AI from the articles below · updated Nov 11, 11:08 PM ET
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Together AI@togethercomputeOfficialTogether AI says inference bottlenecks depend on traffic shapeAITogether AI says LLM performance depends on traffic shape, with short inputs and long outputs limited by decode speed and long prompts by prefill. It adds that tool-heavy agents are limited by end-to-end latency and repeated turns by token economics, so users should measure the bottleneck their workload creates.

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