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 ## Key features
 
 - Run inference or fine-tune large language models like [BLOOM-176B](https://huggingface.co/bigscience/bloom) by joining compute resources with people all over the Internet. No need to have high-end GPUs.
-- It's difficult to fit the whole BLOOM-176B into GPU memory [unless](https://twitter.com/Tim_Dettmers/status/1559892918395031552) you have multiple high-end GPUs. Instead, Petals allows to load and serve a small part of the model, then team up with people serving all the other parts to run inference or fine-tuning.
+- It's difficult to fit the whole BLOOM-176B into GPU memory [unless](https://twitter.com/Tim_Dettmers/status/1559892918395031552) you have multiple high-end GPUs. Instead, **Petals** allows to load and serve a small part of the model, then team up with people serving all the other parts to run inference or fine-tuning.
 - This way, one inference step takes ≈ 1 sec — much faster than possible with offloading. Enough for chatbots and other interactive apps.
 - Beyond traditional language model APIs — you can employ any fine-tuning and sampling methods by executing custom paths through the model or accessing its hidden states. This allows for the comforts of an API with the flexibility of PyTorch.