New online tokenization algorithm slashes LLM latency and memory use

Researchers have developed a new online tokenization algorithm that significantly reduces latency and memory usage for large language models. This algorithm achieves optimal delay in near-linear time, providing a substantial speed advantage over existing methods like OpenAI's tiktoken and Hugging Face's tokenizers. The practical implementation demonstrates latency improvements of up to 34.8x and throughput gains of up to 7x, while maintaining constant memory usage. AI

IMPACT Significantly reduces latency and memory footprints for LLMs, potentially enabling faster and more efficient inference.

RANK_REASON Academic paper detailing a new algorithm for LLM tokenization. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• byte-pair encoding
• DL-proline
• GigaToken
• Hugging Face
• International Conference on Architectural Support for Programming Languages and Operating Systems
• International Conference on Machine Learning
• Jiang and Gong
• Li, Yang and Mamouras
• Mamouras, Li and Yang
• .openai
• Programming Language Design and Implementation
• tiktoken

• paper
• infra

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