Unverified60% confidenceFactExact time
Quantization 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.
2
Sources
60%
Confidence
Long-term
Relevance
7/12/2026
First Seen
Sources
Alibaba Bans Claude Code: The Battle Over AI Coding Tool Security and Data Sovereignty
bilibiliSynthMindX7/4/2026
Related Claims
VerifiedQuantization formats can compress the runtime memory of a 70B-parameter model from 140 GB to under 40 GB.84% similarUnverifiedAWQ和GGUF均能将700亿参数模型存储体积从FP16的约140GB压缩至INT4的约35-40GB，可在单张RTX 4090上运行，精度损失通常控制在1-3%以内83% similarVerified4-bit quantization compresses each parameter to 0.5 bytes, theoretically compressing 140GB down to approximately 35–40GB.81% similarVerifiedA 300B INT4 quantized model requires approximately 150–180GB of storage space.79% similar
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