Verified65% confidenceFactExact time
A 300B INT4 quantized model requires approximately 150–180GB of storage space.
3
Sources
65%
Confidence
Long-term
Relevance
7/24/2026
First Seen
Sources
小米静默发布MiMo-V2.5-DFlash：300B模型推理速度或将翻倍
redditr/LocalLLaMA7/12/2026
Related Claims
UnverifiedStoring 100 million 1536-dimensional OpenAI embedding vectors in float32 requires approximately 600 GB of memory, which can be compressed to approximately 150 GB after 8-bit scalar quantization.82% 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.79% similarUnverifiedThe local ComfyUI solution is recommended to be equipped with at least 32GB of RAM and more than 300GB of disk space.79% similarVerifiedA model with 32B parameters requires approximately 64GB of storage space in FP16.79% similar
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