Sakana AI and NVIDIA show a GPU-friendly way to cut LLM data waste

SakanaAILabs · x · 2026-07-22

Sakana AI's joint research with NVIDIA on reducing internal computation in large language models was featured by Nikkei Digital Governance.

• The accompanying graphic explains a new way to reduce data-processing waste while preserving performance.
• Llion Jones said sharing NVIDIA's specialized expertise was a key factor in the project’s success.
• The image contrasts the traditional ELL approach with TwELL, which reorders data within the GPU’s processing window to avoid unnecessary rearrangement.
• The reported idea is that better structural handling of sparse data can make GPU processing faster without sacrificing model quality.

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