RISC V making its way into proper server platforms is a huge milestone. The open architecture could eventually disrupt the current vendor lock in we see in cloud data centers.
Which axis of vendor lock in are you seeing possibly being disrupted by this? x86 is rather dominant, though ARM is making a play. RISC V just means AWS offers a different ISA on compute, you'd still be vendor lock-in'd to AWS/whomever. Or I'm totally not getting it.
I wonder how the driver situation is. From Jeff Geerling's work on running "big" GPUs on Raspberry Pis, my impression is that often less-common platforms should work but are littered with papercuts in practice.
AMD GPUs have worked on RISC-V for a couple years at this point. I have a Milk-V Titan that's paired with an AMD GPU running well: https://milkv.io/titan
…and when it comes to the most common use of big-iron GPUs right now, neural network engines, they're all so poorly put together that a papercut is often fatal. If every dependency isn't the exact needed version, the whole thing will collapse, and using all but the most common hardware is sure to be unsupported in some deprecated library version that is absolutely required to get everything running.
I had thought Xilinx ISE and Altera Quartus had the market cornered on the most difficult environments to get running, but then LLMs and stable diffusion came along.
> I had thought Xilinx ISE and Altera Quartus had the market cornered on the most difficult environments to get running, …
These tools were the reason why I gave up on FPGAs years ago and chose to spend my time on GPUs instead. And to this day, I still don't understand why they worked so differently from what I had expected in so many ways.
I wonder how the driver situation is. From Jeff Geerling's work on running "big" GPUs on Raspberry Pis, my impression is that often less-common platforms should work but are littered with papercuts in practice.
While thinking about it, if NVIDIA or SAMSUNG go performant RISC-V CPU micro-architecture, will "probably" change the world.
I had thought Xilinx ISE and Altera Quartus had the market cornered on the most difficult environments to get running, but then LLMs and stable diffusion came along.
These tools were the reason why I gave up on FPGAs years ago and chose to spend my time on GPUs instead. And to this day, I still don't understand why they worked so differently from what I had expected in so many ways.
oh a double wide surprise for the AI industry? just what we needed.
is it opensource or megs of blob?