bitsandbytes · Hugging Face

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Inference Providers

8-bit (LLM.int8() algorithm)

bitsandbytes is the easiest option for quantizing a model to 8 and 4-bit. 8-bit quantization multiplies outliers in fp16 with non-outliers in int8, converts the non-outlier values back to fp16, and then adds them togethe

8-bit (LLM.int8() algorithm)

bitsandbytes is the easiest option for quantizing a model to 8 and 4-bit. 8-bit quantization multiplies outliers in fp16 with non-outliers in int8, converts the non-outlier values back to fp16, and then adds them togethe

BitsAndBytesConfig

bitsandbytes is the easiest option for quantizing a model to 8 and 4-bit. 8-bit quantization multiplies outliers in fp16 with non-outliers in int8, converts the non-outlier values back to fp16, and then adds them togethe

8-bit (LLM.int8() algorithm)

bitsandbytes is the easiest option for quantizing a model to 8 and 4-bit. 8-bit quantization multiplies outliers in fp16 with non-outliers in int8, converts the non-outlier values back to fp16, and then adds them togethe

FluxTransformer2DModel

Quantizing a model in 8-bit halves the memory-usage:

enable_model_cpu_offload()

Quantizing a model in 8-bit halves the memory-usage:

Stable Diffusion 3

bitsandbytes is the easiest option for quantizing a model to 8 and 4-bit. 8-bit quantization multiplies outliers in fp16 with non-outliers in int8, converts the non-outlier values back to fp16, and then adds them togethe