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).
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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).
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Read more about the work at 
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Read more about the work at 
NeuZip: Memory-Efficient Training and Inference with Dynamic Compression
  of Neural Networks (2410.20650)
https://huggingface.co/papers/2410.20650
2410.20650
paper
resource
https://huggingface.co/papers/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
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