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Sparser, Faster, Lighter Transformer Language Models

Modern large language models (LLMs) are powerful but costly. A new collaboration between Sakana AI and NVIDIA introduces TwELL sparse packing format and custom CUDA kernels to leverage unstructured sparsity in feedforward layers, achieving over 20% speedup on H100 GPUs with minimal performance loss.

SourceHacker News AIAuthor: hardmaru
Sparser, Faster, Lighter Transformer Language Models
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Key points

  • Sakana AI and NVIDIA collaborate to introduce TwELL (Tile-wise ELLPACK), a sparse packing format designed for tiled matrix multiplication kernels.
  • Custom CUDA kernels fuse multiple matrix multiplications to maximize throughput and reduce storage.
  • L1 regularization induces >95% sparsity in ReLU-based LLMs with negligible downstream impact.
  • Over 20% speedup for batched inference and training on H100 GPUs, with reduced energy and memory consumption.

Highlights and analysis are generated automatically and may contain errors. Check the original source.

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