tokenizers v1: encode, decode and scaling, measured

tokenizers v1: encode, decode and scaling, measured tokenizers v1: encode, decode and scaling, measured Published September 21, 2026 Update on GitHub Upvote 74 Arthur Zucker ArthurZ Simon Brandeis sbrandeis Luc Georges mcpotato Lysandre lysandre The tokenizer has not historically been the bottleneck within ML workflows. Compute-wise, tokenization is light compared to the heavy modeling happening in the rest of the pipeline.

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• tokenizers v1: encode, decode and scaling, measured Published September 21, 2026 Update on GitHub Upvote 74 Arthur Zucker ArthurZ Simon Brandeis sbrandeis Luc Georges mcpotato Lysandre lysandre The tokenizer has not historically been the bottleneck within ML workflows.
• Compute-wise, tokenization is light compared to the heavy modeling happening in the rest of the pipeline.
• Yet, in some cases, it has rapidly become key to accelerating (or slowing down) your machine learning work.
• As models become faster and workloads scale, that balance begins to shift.
• Training on massive datasets, serving many concurrent requests, or repeatedly processing long inputs can put enough pressure on the tokenizer that it starves the model of data.
• This is why we have chosen to heavily focus on performance for the upcoming version 1 of tokenizers.