Modular now joins SYCL, OpenCL, and One API on the list of cross platform languages which never really became cross platform.
After so long and so much investment in AI, the best cross-platorm API we've got for high performance Kernels is vulcan, a graphics API. That is sad.
Still, this is pretty good for Modular's employees, probably good for Qualcomm. It's just terribly disappointing for anyone who invested time learning mojo in the hope it might actually become cross platform.
Yesterday, LineShine a supercomputer in China emerges as #1 in the Top500 using ARM v9 based chips and no GPUs. Today, Qualcomm a premier designer of ARMv9 licensed chips in the United States acquires Modular, who has been creating a compiler stack that provides an alternative to NVIDIA's CUDA stack.
Are you ready for Qualcomm ARMv9 powered inference running Mojo/MAX written kernels doing low-cost inference at scale for AI?
> I don't get it.
>
> Qualcomm has almost no products in the high-end inference/training market. The industry standard is the NVIDIA Hopper H100/H200.
>
> What could they possibly get from acquiring Modular?
Don't ask what they will gain from owning it, ask what they will gain from others not owning it...
It's now focusing on inferencing, both for data centers and edge. They already have an older AI100 NPU card and have other products in the pipeline including server class CPU that they are targeting for "Agentic" applications.
I honestly think Mojo would be better served if it is just a high-level language for GPU programming that compiles down to PTX with clear Python/Rust interop boundaries instead of trying for the "one language, multiple computational model" thing that they seem to be going for. The programming model between CPU and GPU programming is very different: code that runs best on CPU with heavy branching behaviors should not be written the same way as massively parallel matrix multiplication oriented GPU code, which I think they will be forced to do in the MLIR level anyway.
So, you end up with a language that looks like Python, but doesn't behave like Python, and companies that adopt Mojo early with the promise of Python compatibility may find themselves running into edge cases with difficult to trace compiler error messages that would be nearly impossible to debug, especially with the addition of Zig style `comptime` as their metaprogramming model.
Modular now joins SYCL, OpenCL, and One API on the list of cross platform languages which never really became cross platform.
After so long and so much investment in AI, the best cross-platorm API we've got for high performance Kernels is vulcan, a graphics API. That is sad.
Still, this is pretty good for Modular's employees, probably good for Qualcomm. It's just terribly disappointing for anyone who invested time learning mojo in the hope it might actually become cross platform.
Yesterday, LineShine a supercomputer in China emerges as #1 in the Top500 using ARM v9 based chips and no GPUs. Today, Qualcomm a premier designer of ARMv9 licensed chips in the United States acquires Modular, who has been creating a compiler stack that provides an alternative to NVIDIA's CUDA stack.
Are you ready for Qualcomm ARMv9 powered inference running Mojo/MAX written kernels doing low-cost inference at scale for AI?
I don't get it.
Qualcomm has almost no products in the high-end inference/training market. The industry standard is the NVIDIA Hopper H100/H200.
What could they possibly get from acquiring Modular?
> Qualcomm has almost no products in the high-end inference/training market.
There's actually a lot of ML deployed on phones. Both Google's and Apple's photo software uses it heavily for example.
> The industry standard is the NVIDIA Hopper H100/H200.
B200/B300/GB300 actually...
> I don't get it. > > Qualcomm has almost no products in the high-end inference/training market. The industry standard is the NVIDIA Hopper H100/H200. > > What could they possibly get from acquiring Modular?
Don't ask what they will gain from owning it, ask what they will gain from others not owning it...
Qualcomm is pivoting.
It's now focusing on inferencing, both for data centers and edge. They already have an older AI100 NPU card and have other products in the pipeline including server class CPU that they are targeting for "Agentic" applications.
Are the Qualcomm Dragonfly chips not considered high end?
> I don't get it. Qualcomm has almost no products in the high-end inference/training market
You're allowed to get a new job. Qualcomm is allowed to enter new markets.
You've never heard of an acquihire?
I don't think $4B is reasonable for an acquihire. They must see value in the technology.
Either this was the plan all along (cashing in on the bubble) or it’s an admission of failure.
I honestly think Mojo would be better served if it is just a high-level language for GPU programming that compiles down to PTX with clear Python/Rust interop boundaries instead of trying for the "one language, multiple computational model" thing that they seem to be going for. The programming model between CPU and GPU programming is very different: code that runs best on CPU with heavy branching behaviors should not be written the same way as massively parallel matrix multiplication oriented GPU code, which I think they will be forced to do in the MLIR level anyway.
So, you end up with a language that looks like Python, but doesn't behave like Python, and companies that adopt Mojo early with the promise of Python compatibility may find themselves running into edge cases with difficult to trace compiler error messages that would be nearly impossible to debug, especially with the addition of Zig style `comptime` as their metaprogramming model.
Has anyone used mojo/modular extensively in their work? I installed it as soon as it was available but never went past the toy examples.
Welp, I think I can give up on my hope for Mojo.
latty gotta get his baggy