How fast will it feel? Estimate time to first token, per-call latency, and the end-to-end wall-clock of a multi-step agent — and see how much of it is spent waiting, not generating.
3.07s to first token, 4.94s per call. This 8-step agent task runs ~39.5s end-to-end — 62% of it spent waiting for first tokens, not generating.
A request's wall-clock has two regimes. Time to first token (TTFT) is fixed overhead — queueing, scheduling, the network round-trip — plus the time to prefill the prompt: the model reads every input token before it can emit one. Then generation streams the answer one token at a time at the model's decode speed. So a single reply is overhead + prompt/prefill_rate + output/decode_rate, and for a long chat answer the decode term dominates — which is why "tokens per second" is the headline everyone quotes.
Agents break that intuition. An agent serializes many short calls: each tool-use step re-reads a growing context (a long prefill) and emits a tiny action — a function call, a few words of plan (a short decode). It pays the TTFT tax once per turn while barely touching the decode regime, so end-to-end the task is dominated by time-to-first-token, not raw throughput. Push the step count up with a short output and watch the "time spent waiting" figure climb past half. The practical consequence: a high-tokens/sec model can feel sluggish in a loop, and a model with a snappier TTFT can win a multi-step task despite a lower throughput headline. The fixes that matter are the prefill-side ones — prompt caching to skip re-reading the unchanged context, and fewer, fatter turns.
The model × hardware speeds here are typical order-of-magnitude defaults, not a benchmark — every field is editable, so drop in your own measured TTFT and tokens/sec. The concepts are unpacked in TTFT vs. TPOT: the two numbers that define LLM latency and prefill vs. decode; the agent-side playbook is in how to reduce an AI agent's latency. Sizing the hardware or the bill instead? See the VRAM calculator and the cost calculator.
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