You can run inference across multiple GPUs with 🤗 Accelerate or PyTorch Distributed, which is useful for generating with multiple prompts in parallel.

To load a model in 4-bit for inference with multiple GPUs, you can control how much GPU RAM you want to allocate to each GPU. For example, to distribute ...

Tensor parallelism shards a model onto multiple GPUs, enabling larger model sizes, and parallelizes computations such as matrix multiplication. To enable tensor ...

30 сент. 2023 г. · Based on the current demo, "Distributed inference using Accelerate", it is still not quite clear about how to perform multi-GPU parallel inference for a model ...

13 авг. 2023 г. · How should I load and run this model for inference on two or more GPUs using Accelerate or DeepSpeed ? Any guidance/help would be highly ...

You can run inference across multiple GPUs with 🤗 Accelerate or PyTorch Distributed, which is useful for generating with multiple prompts in parallel.

27 нояб. 2023 г. · Summary · Multi-GPU inference using Hugging Face's Accelerate package significantly improves performance, especially when using batch processing.

Passing "auto" here will automatically split the model across your hardware in the following priority order: GPU(s) > CPU (RAM) > Disk.

Hi, is there a way to create an instance of LLM and load that model into two different GPUs? Note that the instance will be created in two different celery ...

17 окт. 2022 г. · Hi Team, I have trained a t5/mt5 hugging face model, I am looking for a way to to inference 1Million examples on multiple GPU.