[bsaul]

Mojo is marketed as a way to get maximum hardware performance on any hardware, not just nvidia.

This may appeal to people wanting to run their code on different hardware brands fro various reasons.

[pjmlp]

True, however that goal is not yet available today, it doesn't even run on Windows natively.

And for those that care, Julia is available today on different hardware brands, as there are other Python DSL JITs as well.

I agree they will get there, now the question is will they get there fast enough to matter, versus what the mainstream market cares about.

[melodyogonna]

Mojo GPU kernels can run on both Nvidia and AMD GPUs today

[eigenspace]

Julia has GPU compilers for Nvidia, AMD, Intel, and Apple, and we have KernelAbstractions.jl for writing a kernel that is portable between all of them (plus the CPU!)

[pjmlp]

Specific models if I recall correctly.

[GeekyBear]

Just as LLVM doesn't automatically have a backend or every new CPU architecture, Mojo/MLIR doesn't automatically have a backend for every new CPU/GPU/TPU.

However, writing an LLVM backend for RISC-V sure did add support for a whole lot of different programming languages and the software you have access to through them in one fell swoop.

The same is true here.

Instead of rewiting all your GPU code every time you need to target a new GPU/TPU architecture, you just need a new backend.

[melodyogonna]

Nah, outside of Models you can write Mojo code today that work on both Nvidia and AMD gpus, the code itself doesn't have to be AI specific.

[fwip]

I think they meant models of GPU, not models of LLM.