How to choose the quantization format for inference on CPU vs GPU?
sobes.tech AI
Answer from AI
The choice of quantization format for inference on CPU and GPU depends on the architecture and hardware features:
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CPU: Usually, CPUs perform better with 8-bit integer (INT8) quantization, as many modern processors support SIMD instructions (e.g., AVX2, AVX-512) for efficient INT8 operations. INT8 quantization reduces memory usage and speeds up computations without significant loss of accuracy.
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GPU: GPUs often use 16-bit floating-point formats (FP16) or mixed precision, as modern GPUs (e.g., NVIDIA with Tensor Cores) are optimized for FP16 and can perform floating-point operations faster and more accurately than integer formats. INT8 quantization on GPU is also possible but requires support from drivers and libraries.
Summary:
- INT8 quantization is preferred for CPU.
- FP16 or mixed precision is preferred for GPU if supported.
The choice also depends on the trade-off between model accuracy and performance, as well as support from specific hardware and frameworks.