ONNX Runtime is actually decently well optimized to run on CPUs; even with large models. However, the simple truth is that there’s really no escaping that Billion+parameter models need to be quantized and even pruned heavily to fit in memory and not saturate the CPU cache so inferences/generations don’t take forever. That’s a reduction in accuracy, so the quality of the generations aren’t great.

There is a lot of really interesting research and development being done right now on smart quantization and pruning. Model serving technologies are improving rapidly too—paged attention is a really cool technique (for transformer based models) for effectively leveraging tensor core hardware—I don’t think that’s supported on CPU yet but it’s probably not that far off.

It’s a really active field and there’s just as much interest in running huge models on huge hardware as there is big models on small hardware. I recently heard of layerwise inference for CPUs; load each layer of the network to the CPU cache on demand. That’s typically a bottleneck operation on GPUs but CPU memoery so bloody fast that it might actually work fine. I haven’t played with it myself, or read the paper all that deeply so I can’t really comment more than it’s an interesting idea.