LocalLLaMA debates when inference speed stops mattering

This is a useful reality check on local LLM benchmarks: raw speed is only meaningful relative to the workflow around it. Once the model is generating code, reasoning traces, or machine-consumed output, humans stop being the limiting factor and throughput starts to matter a lot.

• –For coding, faster output reduces iteration latency and helps preserve flow state, even if no one reads every token as it streams.
• –For reasoning models, much of the generated text is intermediate work rather than final answer, so higher tokens/sec cuts dead waiting time.
• –For agentic or batch use, throughput becomes system capacity, not reading comfort, which is why local AI users obsess over it.
• –For creative or research workflows where the user reads closely and can multitask while waiting, model quality and prompt processing speed can matter more than output speed itself.