Neura saysLatency matters: time-to-first-token, tokens-per-second, and streaming shape UX.
Two metrics define LLM responsiveness: TTFT (time to first token — how long until something appears) and TPS (tokens per second — how fast it streams after). Streaming the response token-by-token makes long answers feel fast because the user reads as it generates. For UX, prioritize low TTFT and stream; for throughput/cost, batch. Model size, context length, and load all affect both.
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TTFT: time to first token
TPS: streaming speed after
Streaming makes long answers feel fast
Size/context/load affect both
The Overfit Ogre attacks — common mistakes
Waiting for the full response (no streaming)
Ignoring TTFT on interactive UX
Confusing throughput with latency
Boss battleExplain why streaming improves perceived speed even at the same TPS.
Example code
<!doctype html><html><head><meta charset="utf-8"></head>
<body style="background:#06040d;color:#e6e0ff;font-family:monospace;padding:20px"><pre>TTFT: how soon the first token appears
TPS: how fast it streams after
stream → user reads as it generates → feels fast</pre></body></html>