Verified80% confidenceFactExact time
4-bit quantization compresses each parameter to 0.5 bytes, theoretically compressing 140GB down to approximately 35–40GB.
9
Sources
80%
Confidence
Long-term
Relevance
7/11/2026
First Seen
Sources
Demystifying Unified Memory Architecture: How Mini PCs Run 70B Large Models
hackernewshackernews7/10/2026
Related Claims
VerifiedQuantization formats can compress the runtime memory of a 70B-parameter model from 140 GB to under 40 GB.83% similarUnverifiedQuantization can compress the storage size of a 70-billion-parameter model from approximately 140 GB in FP16 to approximately 35–40 GB in INT4, enabling it to run on consumer-grade hardware such as a single RTX 4090 with CPU memory offloading.81% similarUnverifiedTernary representation theoretically requires only about 1.58 bits per parameter, which can yield nearly a 10x storage compression compared to FP16.78% 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%.78% similarVerified4-bit quantization in GGUF format can compress model weights from 32-bit floating point to 4-bit integers, reducing model size by approximately 8 times.77% similar
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