Verified80% confidenceBenchmarkExact time
4-bit quantization (such as Q4_K_M) can typically compress the memory footprint of a 3B model from approximately 6GB to approximately 2GB, with a precision loss of less than 2–3%.
6
Sources
80%
Confidence
Long-term
Relevance
7/16/2026
First Seen
Sources
Related Claims
VerifiedQ4_K_M uses mixed precision, storing some critical weights at 6-bit and the remainder at 4-bit, with an overall average of approximately 4.5-bit.84% similarVerifiedThe GGUF format supports multiple precision levels such as 4-bit and 8-bit, and can compress the memory footprint of a 7-billion-parameter model from approximately 14 GB (FP16) to approximately 4 GB (Q4_K_M quantization), with inference accuracy loss typically within 1–3%.82% similarVerifiedQuantization compresses the storage precision of model parameters from 16-bit down to 8-bit or even 4-bit, thereby reducing memory usage, but at the cost of potential loss in model accuracy.80% similarUnverified一个32K上下文窗口在Q4量化模型上可能额外占用2-4GB内存80% similarVerifiedAfter applying INT4 quantization, the memory footprint of a 3B parameter model can be reduced to approximately 2 GB.80% similar
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