I run an AI platform and we need to tokenize fast and early to make a lot of decisions on the subsequent steps (things like routing, rate limiting and such). Its really important to do this efficiently even though its not a large % of total end to end time for the request.
replyTo concur it's "latency critical", not "performance critical", people often confuse those two - optimize it all, but especially the chained critical path latency!
reply1/1000 of inference compute is a non-trivial workload at scale. Gartner estimates ~$28B in inference spend for 2026 making this a $28 million dollar per year workload (edit: based on the assumption above)
replySource: https://www.gartner.com/en/newsroom/press-releases/2026-07-2...
The issue is it’s cpu compute which is underutilized in gpu clusters anyway, so practically it’s not really 1/1000.
replyTotally, edited my comment to specify "based on the assumption above." The main takeaway I was going for was 0.1% is not a small number in this context
replyAlways good to make it 0.001%
replyTime to first token, especially for smaller models, can be sharply reduced.
replyLatency can be just as important as overall throughput, especially for inference providers like Groq and Cerebras.
Tokenization is <0.1% of the inference time for the first token in the same way it is <0.1% for the last.
replyTime to first token refers to the time until the model outputs one token, which includes the time to process the entire prompt (doing prefill). The GPU time per token is much lower when doing prefill, so the significance of tokenization is higher.
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