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GPUTOK: GPU Accelerated Byte Level BPE Tokenization

arXiv – CS AI|Venu Gopal Kadamba, Kanishkha Jaisankar|March 4, 2026 at 05:00 AM|1 views

🤖AI Summary

Researchers developed GPUTOK, a GPU-accelerated tokenizer for large language models that processes text significantly faster than existing CPU-based solutions. The optimized version shows 1.7x speed improvement over tiktoken and 7.6x over HuggingFace's GPT-2 tokenizer while maintaining output quality.

Key Takeaways

→GPU-based tokenizer addresses CPU bottlenecks as language models scale to million-token context windows
→Optimized version achieves 1.7x speedup over tiktoken and 7.6x over HuggingFace GPT-2 tokenizer on long sequences
→Memory allocation accounts for 70-80% of processing time, indicating memory pooling could provide further improvements
→Output quality remains comparable to existing tokenizers with less than 1% difference in similarity metrics
→Technology makes long-context inference more practical for large language model applications

#gpu-acceleration #tokenization #large-language-models #performance-optimization #cuda #bpe #gpt-2 #machine-learning #inference-speed

Read Original →via arXiv – CS AI

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