Unverified50% confidenceFactExact time
After INT8 quantization, a model at the 0.9B scale can theoretically reduce its inference memory footprint to below approximately 1GB.
1
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50%
Confidence
Long-term
Relevance
9/7/2026
First Seen
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Related Claims
Unverified4-bit quantization reduces model size to 1/8 of the original at the cost of approximately 1–3% accuracy loss.76% similarVerifiedINT8 quantization loses approximately 0.5% accuracy while halving VRAM usage; INT4 quantization compresses VRAM to 1/4 of the original with an accuracy loss of approximately 1–3%.73% similarUnverifiedINT8 quantization maps 32-bit floating-point weights to 8-bit integers, compressing model size by 75% with almost no accuracy loss72% similarUnverifiedint8量化将参数从16位压缩至8位，理论上减少约50%显存占用；int4将显存占用降低至原始的25%左右72% similarUnverified轻量版模型单次推理的算力消耗往往只有旗舰版本的十分之一甚至更低72% similar
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