We show for the first time that large-scale generative pretrained transformer (GPT) family models can be pruned to at least 50% sparsity in one-shot, without any retraining, at minimal loss of accuracy. [...] We can execute SparseGPT on the largest available open-source models, OPT-175B and BLOOM-176B, in under 4.5 hours, and can reach 60% unstructured sparsity with negligible increase in perplexity: remarkably, more than 100 billion weights from these models can be ignored at inference time.
— SparseGPT, by Elias Frantar and Dan Alistarh
Recent articles
- Reverse engineering some updates to Claude - 31st July 2025
- Trying out Qwen3 Coder Flash using LM Studio and Open WebUI and LLM - 31st July 2025
- My 2.5 year old laptop can write Space Invaders in JavaScript now, using GLM-4.5 Air and MLX - 29th July 2025