Quantization Comparison

Compare VRAM usage, tradeoffs, and deployment feasibility across quantization methods.

Compare VRAM requirements across quantization options for the same model and check deployment fit on a selected GPU.

ModelReference GPUPrompt tokensMax output tokensBatch sizeConcurrencyTensor ParallelDefault quantization

Enable lower runtime / KV management overhead for mainstream inference runtimes

This table compares VRAM and deployment fit only; model quality, accuracy loss, and backend throughput still require real benchmark validation.

Quantization	Recommended total VRAM	Per-GPU VRAM	Delta vs INT4	NVIDIA RTX A6000 48GB Fit	Notes
INT4	456.22 GiB	456.22 GiB	Baseline	Insufficient VRAM	The current setup exceeds the selected GPU memory. Use a larger GPU or more parallel cards.
INT8	826.22 GiB	826.22 GiB	+81.1%	Insufficient VRAM	The current setup exceeds the selected GPU memory. Use a larger GPU or more parallel cards.
FP8	826.22 GiB	826.22 GiB	+81.1%	Architecture unsupported	This GPU architecture does not list support for this quantization mode.
FP16	1648.44 GiB	1648.44 GiB	+261.3%	Insufficient VRAM	The current setup exceeds the selected GPU memory. Use a larger GPU or more parallel cards.
BF16	1648.44 GiB	1648.44 GiB	+261.3%	Insufficient VRAM	The current setup exceeds the selected GPU memory. Use a larger GPU or more parallel cards.