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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