Verified65% confidenceBenchmarkExact time
A model with 70 billion parameters that originally requires 80 GB of VRAM requires only about 40 GB of memory after 4-bit quantization (Q4_K_M level), with inference quality loss typically within 3%–8% on most benchmarks.
3
Sources
65%
Confidence
Long-term
Relevance
7/21/2026
First Seen
Sources
Related Claims
UnverifiedFor models with more than 10B parameters, 4-bit quantization can typically reduce memory usage by approximately 50% while keeping capability loss within 5%.79% similarVerifiedA 7B-parameter model requires only approximately 4 GB of VRAM to run after 4-bit quantization.78% 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%.77% similarVerified4-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%.77% similar
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