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

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.


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.

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.



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


More details can be found in:
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: