Comments (2)
As you can see, you have to use very advanced GPU-hardware in order to run the inference.
On the other hand, it should be possible to run it using also much limited GPU memory via huggingface-accelerate. Generally, you have to use the hook for the pytorch forward
method. This hook should load the weights into GPU memory iteratively and save the intermediate representation to disk until a working inference is obtained. There is a discussion here pointing out it is in principle possible.
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As you can see, you have to use very advanced GPU-hardware in order to run the inference.
On the other hand, it should be possible to run it using also much limited GPU memory via huggingface-accelerate. Generally, you have to use the hook for the pytorch
forward
method. This hook should load the weights into GPU memory iteratively and save the intermediate representation to disk until a working inference is obtained. There is a discussion here pointing out it is in principle possible.
Thanks, Evgeny for the help and links, I will listen to discussions and check product updates.
AI is cool π€π»
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Related Issues (20)
- ΠΡΠΈΠ²Π΅Ρ HOT 2
- How did you used LAMB optimizer with ZeRO CPU offload? HOT 2
- Run on networked nodes
- AWS HOT 1
- Could you share the md5 value for those checkpoints? HOT 2
- Would it be possible to run the model on single A100 (40GB) or 2xV100 (32GB) ? HOT 2
- No mention of `bfloat16` in source, and yet weights are `bfloat16`
- CUDA out of memory HOT 6
- NCCL error HOT 1
- PCI x1 or PCI x16 for GPU
- Is there any plans for making cloud service? HOT 1
- Has anyone deployed it on 10x 3090 ? Or any similar configuration? HOT 1
- Provide pruned version for weaker hardware HOT 2
- Citation bibtex? HOT 2
- Request to Open "Russian Pile" Dataset for Public Access
- How to use it with LangChain? HOT 2
- Timeout on 8 x RTX A6000 HOT 2
- Why usage ssh-agent and openssh-client package in docker
- gguf / mlx format?
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