performance depends on the shape of your traffic
short in / long out → decode speed. long prompts → prefill. tool-heavy agents → end-to-end latency. repeated turns → token economics
measure the bottleneck your workload actually creates
Together AI says inference bottlenecks depend on traffic shape
AITogether 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.
Source details
Together AI · x.comPublished · added here
Original title: performance depends on the shape of your traffic