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transformers/docs/source/en/quantization/fbgemm_fp8.md

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+
+# FBGEMM
+
+[FBGEMM (Facebook GEneral Matrix Multiplication)](https://github.com/pytorch/FBGEMM) is a low-precision matrix multiplication library for small batch sizes and support for accuracy-loss minimizing techniques such as row-wise quantization and outlier-aware quantization. With FBGEMM, quantize a models weights to 8-bits/channel and the activations to 8-bits/token (also known as fp8 or w8a8).
+
+> [!TIP]
+> You need a GPU with [compute capability 9+](https://developer.nvidia.com/cuda-gpus#collapseOne) like a H100.
+
+Install the FBGEMM_GPU package with the command below to ensure you have the latest version.
+
+```bash
+pip install --upgrade accelerate fbgemm-gpu torch
+```
+
+If you're having installation issues, try installing the [nightly release](https://pytorch.org/FBGEMM/fbgemm_gpu-development/InstallationInstructions.html#fbgemm-gpu-install-libraries:~:text=found%20here.-,Install%20the%20FBGEMM_GPU%20Package,-Install%20through%20PyTorch).
+
+Create a [`FbgemmFp8Config`] and pass it to [`~PreTrainedModel.from_pretrained`] to quantize a model to fp8.
+
+```py
+from transformers import FbgemmFp8Config, AutoModelForCausalLM
+
+quantization_config = FbgemmFp8Config()
+quantized_model = AutoModelForCausalLM.from_pretrained(
+    "meta-llama/Meta-Llama-3-8B",
+    dtype="auto",
+    device_map="auto",
+    quantization_config=quantization_config
+)
+```
+
+[`~PreTrainedModel.save_pretrained`] and [`~PreTrainedModel.from_pretrained`] enable saving and loading a quantized model.
+
+```py
+quant_path = "/path/to/save/quantized/model"
+model.save_pretrained(quant_path)
+model = AutoModelForCausalLM.from_pretrained(quant_path, device_map="auto")
+```
+
+## Resources
+
+Read the [Open-sourcing FBGEMM for state-of-the-art server-side inference](https://engineering.fb.com/2018/11/07/ml-applications/fbgemm/) blog post for more details on FBGEMM.