It depends on what you mean by "this." MLC's catch is that you need to define/compile models for it with TVM. Here is the list of supported model architectures:

It depends on what you mean by "this." MLC's catch is that you need to define/compile models for it with TVM. Here is the list of supported model architectures: https://github.com/mlc-ai/mlc-llm/blob/main/python/mlc_llm/model/model.py https://github.com/mlc-ai/mlc-llm/blob/main/python/mlc_llm/m... llama.cpp has a much bigger supported model list, as does vLLM and of course PyTorch/HF transformers covers everything else, all of which work w/ ROCm on RDNA3 w/o too much fuss these days. For inference, the biggest caveat is that Flash Attention is only an aotriton implementation, which besides being less performant sometimes, also doesn't support SWA. For CDNA there is a better CK-based version of FA, but CK doesn't not have RDNA support. There are a couple people at AMD apparently working on native FlexAttention, os I guess we'll how that turns out. (Note the recent SemiAccurate piece was on training, which I'd agree is in a much worse state (I have personal experience with it being often broken for even the simplest distributed training runs). Funnily enough, if you're running simple fine tunes on a single RDNA3 card, you'll probably have a better time. OOTB, a 7900 XTX will train at about the same speed as an RTX 3090 (4090s blow both of those away, but you'll probably want more cards and VRAM of just move to H100s).