Unverified60% confidenceFactExact time
Storing 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.
2
Sources
60%
Confidence
Long-term
Relevance
7/18/2026
First Seen
Sources
Related Claims
VerifiedA 300B INT4 quantized model requires approximately 150–180GB of storage space.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.78% similarUnverifiedUnder float32 storage, each 1024-dimensional vector occupies 4KB, so an index of a million-scale documents requires at least 4GB of storage.77% similarUnverifiedWhen the model loads, it first allocates a full-precision shell of approximately 1.29 GB and then reads in 643 MB of full weights, with peak memory potentially reaching approximately 3,400 MB.77% similar
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