text
We just released a paper (NeuZip) that compresses VRAM in a lossless manner to run larger models. This should be particularly useful when VRAM is insufficient during training/inference. Specifically, we look inside each floating number and find that the exponents are highly compressible (as shown in the figure below).
We just released a paper (NeuZip) that compresses VRAM in a lossless manner to run larger models. This should be particularly useful when VRAM is insufficient during training/inference. Specifically, we look inside each floating number and find that the exponents are highly compressible (as shown in the figure below).
new_line
new_line
text
Read more about the work at 
Read more about the work at 
resource
https://huggingface.co/papers/2410.20650
paper
2410.20650
https://huggingface.co/papers/2410.20650
NeuZip: Memory-Efficient Training and Inference with Dynamic Compression
  of Neural Networks (2410.20650)
We just released a paper (NeuZip) that compresses VRAM in a lossless manner to run larger models. This should be particularly useful when VRAM is insufficient during training/inference. Specifically, we look inside each floating number and find that the exponents are highly compressible (as shown in the figure below).

Read more about the work at https://huggingface.co/papers/2410.20650
🔥
gatorand John6666 AtAndDev Viewegger spooner2 YaTharThShaRma999 prithivMLmods amphealy Jonatandb aaditya Kasnol maywell OrigamiDream Norod78 Chief-Inspector victor yongchanghao jgitsolutions
👀
ijohn07 jgitsolutions qmpzqmpz