Practical overview
When it fits
Use it when a PyTorch model needs to run or train across multiple GPUs, particularly for large-model workloads and small batches.
Who it is for
- PyTorch developers running large models across multiple GPUs.
- Practitioners distributing model training, including fine-tuning workflows.
Documented uses
- Run a large PyTorch model for inference across selected GPU devices.
- Train or fine-tune a model with its parameters distributed across GPUs.
- Save a wrapped model checkpoint in a form usable outside Tensor Parallel.
Requirements & limits
- The workflow requires multiple GPUs; the README says a single-GPU Colab setup has no tensor-parallel use.
- The library defaults to available GPUs, while
device_idslets users select devices. - Distributed mode uses PyTorch distributed and requires
torchrun. - The README positions tensor parallelism for large models and small batches, and says ZeRO can be preferable when a large batch fits.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Install and wrap a PyTorch model for multi-GPU use
Add the library to a Python environment and apply its documented wrapper to a model on selected GPUs.
Before you start
- A Python environment with PyTorch and the model's dependencies.
- A model and multiple GPU devices available to the environment.
Steps
Install the stable package with the command below.
pip install tensor_parallelLoad a PyTorch model, then pass it to
tp.tensor_parallelwith the GPU device IDs to use.For best memory efficiency, call the wrapper while the model is still on the CPU.
Expected outcome
The model is wrapped for multi-GPU execution and can be used for inference or training through its normal model interface.
Guide limitations
- A single GPU does not provide a multi-GPU tensor-parallel workload.
- The README describes speedup as potentially linear; it does not guarantee a particular performance result.
Save a wrapped model for use outside Tensor Parallel
Write a model checkpoint in an unsplit form using the documented save context.
Before you start
- A trained model wrapped with Tensor Parallel.
- PyTorch or Transformers model-saving support.
Steps
Enter the
tp.save_tensor_parallel(model)context.Within the context, save the model state dict with
torch.saveor callmodel.save_pretrained.
Expected outcome
The saved state is gathered as if the model had not been split across GPUs.
Guide limitations
- This guide covers checkpoint saving, not loading or deployment in another environment.
Compare options for Model inference and serving
These projects document a common task and share a project role. This is a source-based comparison, not an execution benchmark or a guarantee of interchangeability.
| Project | Documented purpose | Platform | Access and requirements |
|---|---|---|---|
| Tensor Parallel | Run a large PyTorch model for inference across selected GPU devices. | Cloud, Linux, macOS, Server | |
| Cloud AI SDK | Adapt and run documented model recipes for computer-vision and NLP inference. | Cloud, Server | The GitHub repository is public. Its public visibility does not establish license terms. |
| Turboquant | Replace a model’s existing inference cache with a compressed cache. | Cloud, Linux, macOS, Self-hosted | The project describes itself as open source. The README documents installation through pip or from a source checkout. |
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 16 October 2026
- Original website title
- BlackSamorez/tensor_parallel · Official homepage title · retained in its source language