Speeding Up LLMs Without Retraining
A training-free approach to activation sparsity for faster inference
TEAL introduces a simple, practical method for creating activation sparsity in large language models without expensive retraining, enabling significant performance gains.
- Reduces compute and memory requirements during forward pass
- Achieves up to 2.8x inference speedup on modern hardware
- Works with existing LLM architectures without modification
- Eliminates need for continued pre-training on billions of tokens
This engineering breakthrough makes LLM deployment more efficient, reducing operational costs and enabling broader applications across resource-constrained environments.