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| # Quantization |
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|
| Quantization techniques reduce memory and computational costs by representing weights and activations with lower-precision data types like 8-bit integers (int8). This enables loading larger models you normally wouldn't be able to fit into memory, and speeding up inference. |
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| > [!TIP] |
| > Learn how to quantize models in the [Quantization](../quantization/overview) guide. |
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| ## PipelineQuantizationConfig |
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| [[autodoc]] quantizers.PipelineQuantizationConfig |
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| ## BitsAndBytesConfig |
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| [[autodoc]] quantizers.quantization_config.BitsAndBytesConfig |
| |
| ## GGUFQuantizationConfig |
| |
| [[autodoc]] quantizers.quantization_config.GGUFQuantizationConfig |
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| ## QuantoConfig |
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| [[autodoc]] quantizers.quantization_config.QuantoConfig |
| |
| ## TorchAoConfig |
| |
| [[autodoc]] quantizers.quantization_config.TorchAoConfig |
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| ## DiffusersQuantizer |
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| [[autodoc]] quantizers.base.DiffusersQuantizer |
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|
