# Hugging Face and Graphcore partner for IPU-optimized Transformers

huggingface.co · Sally Doherty · Sep 14, 2021

**Type:** Announcement

## Summary

Hugging Face launched its Hardware Partner Program at the 2021 AI Hardware Summit to offer device-optimized models and software integrations. As a founding member of the program, Graphcore collaborated with Hugging Face to optimize Transformer models for its Intelligence Processing Unit (IPU). Graphcore IPUs use a massively parallel MIMD architecture with on-die ultra-high bandwidth memory designed for AI workloads featuring fine-grained parallelism, low precision arithmetic, and sparsity. Through Hugging Face's open-source Optimum toolkit, developers will receive plug-and-play access to certified, hardware-optimized models across vision, speech, translation, and text generation. Graphcore's Poplar SDK integrates with PyTorch, TensorFlow, Docker, and Kubernetes, simplifying the process of porting workloads to IPU-POD datacenter compute systems.

## Context

Developers seeking to harness hardware acceleration for Transformer models face challenges with coding complexity and learning new software workflows when targeting specialized AI hardware instead of GPUs.

## Approach / What changed

Graphcore joined Hugging Face's Hardware Partner Program to release certified, IPU-optimized Transformer models via the open-source Optimum library, supported by the Poplar SDK's integrations with PyTorch, TensorFlow, Docker, and Kubernetes.

## Takeaways

- Graphcore IPUs use a massively parallel MIMD architecture with ultra-high bandwidth on-die memory, contrasting with traditional SIMD/SIMT GPU architectures.
- Hugging Face introduced Optimum, an open-source library and toolkit providing developers with plug-and-play access to hardware-optimized models.
- Graphcore's Poplar SDK integrates directly with standard frameworks such as PyTorch and TensorFlow alongside orchestration tools like Docker and Kubernetes.

**Tags:** [Deployment](https://yomu.fyi/topic/deployment), [Machine Learning](https://yomu.fyi/topic/machine-learning), [Open Source](https://yomu.fyi/topic/open-source), [Performance](https://yomu.fyi/topic/performance)

[Read original post](https://huggingface.co/blog/graphcore)