Verified65% confidenceFactExact time
INT4 achieves 4× storage compression compared to FP16.
3
Sources
65%
Confidence
Long-term
Relevance
7/12/2026
First Seen
Sources
INT4 ConvRot W4A4 Quantization: Running Large Image Models on Low-VRAM GPUs in ComfyUI
redditr/comfyui7/11/2026
Related Claims
Partially VerifiedA standard FP32 model occupies 4 bytes per parameter, while INT4 quantization compresses this to 0.5 bytes, theoretically achieving an 8x memory saving.80% similarVerified4-bit量化使模型存储空间缩减为FP16的约1/478% similarUnverified一张1024×1024的未压缩RGBA纹理占据4MB存储空间75% similarUnverifiedBlackwell introduces FP4 for inference, and combined with microscaling technology, can compress weight storage to 1/4 of FP16.75% similarVerifiedQuantized KV caches (such as storing KV vectors in FP8 or INT4) can compress the cache size to 1/2 or even 1/4 of the original.75% similar
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