---
title: 314 Billion Parameter Grok-1 Inference Accelerated by 3.8x, Efficient and Easy-to-Use PyTorch+HuggingFace version is Here!
description: Colossal-AI team followed up Gork-1 and provided an easy-to-use Python + PyTorch + HuggingFace version of Grok-1 for all AI developers.
image: https://company.hpc-ai.com/hubfs/%E4%B8%93%E5%AE%B6%E5%B9%B6%E8%A1%8C%E5%86%8D%E5%8D%87%E7%BA%A7%20(5).png
---

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 March 25, 2024

# 314 Billion Parameter Grok-1 Inference Accelerated by 3.8x, Efficient and Easy-to-Use PyTorch+HuggingFace version is Here!

 1 minute read

![](https://company.hpc-ai.com/hubfs/%E4%B8%93%E5%AE%B6%E5%B9%B6%E8%A1%8C%E5%86%8D%E5%8D%87%E7%BA%A7%20(5).png)

Grok-1, the **314-billion-parameter** Mixture of Experts (MoE) model open-sourced by Musk's xAI, is the largest open-source large language model, and allows for free distribution and commercialization of changes.

 

Grok-1 has attracted a lot of attention in the open source community since its release, and has been ranked No. 1 in the world on the GitHub Trending.

 

![B1](https://company.hpc-ai.com/hs-fs/hubfs/B1.png?width=1983&height=1417&name=B1.png)

However, **Grok-1 is built using ****Rust****+JAX**, which has a high threshold for users who are used to mainstream software ecosystems such as Python+PyTorch+HuggingFace to get started.

Colossal-AI team followed up immediately and provided an **easy-to-use Python + ****PyTorch**** + HuggingFace version of Grok-1** for all AI developers.

 

HuggingFace Download: [https://huggingface.co/hpcai-tech/grok-1](https://huggingface.co/hpcai-tech/grok-1)

 

## Performance Optimization

Combined with Colossal-AI's accumulation in large AI model system optimizations, it has rapidly supported tensor parallelism for Grok-1.

 

On a 8*H800 80GB server, the **inference latency is accelerated by nearly 4 times** compared to methods such as JAX and HuggingFace's auto device map.

 

 

![屏幕截图 2024-12-09 151302](https://company.hpc-ai.com/hs-fs/hubfs/%E5%B1%8F%E5%B9%95%E6%88%AA%E5%9B%BE%202024-12-09%20151302.png?width=1027&height=580&name=%E5%B1%8F%E5%B9%95%E6%88%AA%E5%9B%BE%202024-12-09%20151302.png)

## **Tutorial**

After downloading and installing Colossal-AI, just run the inference scripts

 

`./run_inference_fast.sh hpcaitech/grok-1`

 

Model weights will be downloaded and loaded automatically and the inference results will alos be aligned. The following figure shows a test of Grok-1 greedy search.

 

![B3](https://company.hpc-ai.com/hs-fs/hubfs/B3.png?width=2236&height=962&name=B3.png)

 

More details can be found in:

[https://github.com/hpcaitech/ColossalAI/tree/main/examples/language/grok-1](https://github.com/hpcaitech/ColossalAI/tree/main/examples/language/grok-1)

 

Colossal-AI will further introduce optimizations for Grok-1 in parallel acceleration, quantization reduction of cost, etc. in the near future, welcome to stay tuned.

Colossal-AI open source address: [https://github.com/hpcaitech/ColossalAI](https://github.com/hpcaitech/ColossalAI)

 

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