Hi Georgi - thanks for all the work, have been following and using since the availability of Llama base layers!

Wasn’t implying it’s CPU only, just that it started as a CPU optimized library.

As a person burned by nvidia, I can't thank you enough for the OpenCL support

(a novice here who knows a couple of fancy terms)

> ...lately we have been augmenting it with GPU support.

Would you say you'd then be building an equivalent to Google's JAX?

Someone even asked if anyone would build a C++ to JAX transpiler [0]... I am wondering if that's something you may implement? Thanks.

[0] https://news.ycombinator.com/item?id=35475675

Yes, but to my knowledge it doesn't do any of the complicated optimization stuff that SOTA quantisation methods use. It basically is just doing a bunch of rounding.

There are advantages to simplicity, after all.

The weights are preprocessed into integer quants combined with scaling factors in various configurations (4, 5, 8-bits and recently more exotic 2, 3 and 6-bit quants). At runtime, we use efficient SIMD implementations to perform the matrix multiplication at integer level, carefully optimizing for both compute and memory bandwidth. Similar strategies are applied when running GPU inference - using custom kernels for fast Matrix x Vector multiplications