Quantization
Four-bit weight-only quantization stores model weights as four-bit integers while keeping activations at higher precision. Serving kernels dequantize or rescale weights as they participate in matrix operations.
Narrow weights reduce checkpoint size, device memory use, and weight bandwidth. Dequantization overhead, packing support, and quantization error decide whether those savings improve serving.
Weights are grouped, scaled, packed, and loaded by kernels that understand the layout. Teams compare memory, throughput, latency, and task quality against a wider baseline.
Four-bit weight-only quantization stores model weights as four-bit integers while keeping activations at higher precision. Serving kernels dequantize or rescale weights as they participate in matrix operations. Weights are grouped, scaled, packed, and loaded by kernels that understand the layout. Teams compare memory, throughput, latency, and task quality against a wider baseline.
Narrow weights reduce checkpoint size, device memory use, and weight bandwidth. Dequantization overhead, packing support, and quantization error decide whether those savings improve serving.
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