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.
To concur it's "latency critical", not "performance critical", people often confuse those two - optimize it all, but especially the chained critical path latency!
Just to clarify, latency is one form of performance, and a separate thing to optimize from total resource usage in more classic "performance critical" situations. That performance might be energy, space, or other dimensions besides latency. It might also be something like reliability, accuracy, precision, or even the very human factors like simplicity, modifiability, and visibility.
Heck, even latency alone you can just reduce the standard deviation and get smoother flows. Little's law is a great callout here too, one of my favorite computer science principles.
I don't think that's accurate. If tokenization takes say 10ms and the rest of the inference steps take 50 ms then, improving tokenization will improve the time to first token but won't affect throughput much. After the first token, the inference steps effectively hide the tokenization time.
Same here, as we're sitting in the middle between requests and what budget constraints are allowed given a particular token allowance there can be 10 ~ 100 milliseconds improvement in the UX (TTFT) given such massive tokenization speed up.
We use vllm as it generally has the best ecosystem support.
Parameters are largely dependent on what type of requests you are serving (concurrency, input/output ratios, cached hit patterns).
We've never had a limitation at the tokenizer step. Limitations at peak tend to manifest more on slower time in vllm doing prefill or decode though we actively try and minimize this.
