AllocateWhat is model quantization?
Quantization compresses a model by storing its weights at lower numeric precision, such as 8-bit or 4-bit instead of 16-bit. It cuts memory use and inference cost by 2 to 4 times with small, measurable quality loss, letting the same hardware serve larger models or more traffic.
Production platforms quantize carefully and test against task-specific evaluations, because the acceptable quality loss for a support agent differs from a fraud reviewer.
Quantization is one of several serving optimizations, alongside batching, caching, and speculative decoding, that separate purpose-built inference infrastructure from raw GPU rental.
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Allocate is the cloud inference platform for companies that want to train and run their own models.