@hasantoxr: AI models are getting ridiculously FAST. And it could change how AI agents work. Here's why this matters: An agent does…
Summary
The post argues that raw inference speed is becoming as important as model intelligence for AI agents, since agents run dozens of chained calls per task — comparing top throughput setups like N0.5 Flash (~2,000 tok/s), DeepSeek V4.1 Flash, GLM-5.3-Flash, and MiMo-V2.5-Pro UltraSpeed.
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AI models are getting ridiculously FAST.
And it could change how AI agents work.
Here’s why this matters:
An agent doesn’t make one call. It makes dozens, sometimes hundreds, in a loop. Plan, write code, run it, read the error, fix it, repeat.
Every step waits on the one before it.
At 100 tok/s, a 2,000 token output takes 20 seconds. At 2,000 tok/s, it takes 1 second.
Stack that across 100 steps and you go from 30+ minutes of waiting to under 2.
The fastest setups right now:
→ Naive N0.5 Flash: ~2,000 tok/s → DeepSeek V4.1 Flash on Fireworks: ~1,500 tok/s → GLM-5.3-Flash on LithosAI: ~1,400 tok/s → MiMo-V2.5-Pro UltraSpeed: ~1,000 tok/s
These are different model sizes on different serving stacks, so it’s not a perfect comparison.
Still, the trend is clear.
For years the race was about which model is smartest. For agents, it’s shifting to which model is smart enough and fastest.
A model that’s 20x faster can retry, branch, and check its own work before the user notices any delay.
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