Tokenization Is the Bottleneck You're Not Measuring
Tokenization can significantly slow down request processing in event-loop architectures. The time taken for tokenization, which can range from 5 to 13 milliseconds, creates a bottleneck that affects overall throughput. Implementing an LRU cache for tokenized inputs can help mitigate this issue by reducing redundant tokenization calls.
- ▪Tokenization in LLM proxies is often treated as instantaneous, but it can block requests for 5-13 milliseconds.
- ▪In event-loop architectures, this blocking can severely limit the number of requests processed per second.
- ▪Implementing an LRU cache for tokenized inputs can improve efficiency by avoiding repeated tokenization of the same text.
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Tokenization Is the Bottleneck You're Not Measuring May 25, 2026 • Minds Aspire You’ve optimized your GPU serving stack. You’ve tuned vLLM’s batch size, configured PagedAttention, maybe even set up prefix-aware routing for KV cache locality. Your P99 looks good. Your throughput is climbing. And somewhere in your proxy layer, every single request is blocking for 5-13 milliseconds while a tokenizer turns text into integers. You’re probably not measuring it. Most LLM proxies treat tokenization as instantaneous—call the function, get the tokens, move on. But on an event-loop architecture, 5-13ms isn’t a rounding error. It’s an eternity.
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