I wanted to believe but anyone who has spent any time trying to run models locally knows this is not going to be solved by two lines of python running on rocm as the example shows.
ROCm has improved but the reality is you're still fighting the driver stack more than the models. If you're actually doing local inference on AMD you're spending your time on CUDA compatibility layers, not the AI part. Two lines of python is marketing, the gap between demo and working AMD setup is still real.
ROCm is finally getting better due to a few well meaning engineers.
But let’s be honest, AMD has been an extremely bad citizen to non-corporate users.
For my iGPU I have to fake GFX900 and build things from source or staging packages to get that working. Support for GFX90c is finally in the pipeline…
The improvements feel like a bodyguard finally letting you through the door just because NVIDIA is eating their lunch and they don’t want their club to be empty.
They strongarm their customers to using “Enterprise” GPUs to be able to play with ROCm, and are only broadening their offerings for market share purposes.
Debian build their ROCm with support for all possible devices. If you are tired of compiling from source just use a Debian Stable container, install their libraries in your container build, and pass /dev/kfd and /dev/dri to the container. No ROCm or out-of-tree drivers required on the container host, just regular upstream Linux kernel amdgpu and those two devices to the container.
It's also probably worth trying Vulkan inference. It is now faster than ROCm - both tg and pp over 16k ctx - on Strix Halo so maybe you'll see the benefits too.
Nvidia went through a lot of effort to make CUDA operational on their entire lineup, and they did it before deep learning even took off.
You do this thing not because you expect consumers with 5 year old hardware to provide meaningful utilization but as a demo ("let me grab my old gaming machine and do some supercomputing real quick") and a signal that you intend to stay the course. AMD management hasn't realized this even after various Nvidia people said that this was exactly why they did it, at some point the absence of that signal is a signal that the AMD compute ecosystem is an unreliable investment, no?
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I read:
" In addition to that, the update allows these agents to be turned into desktop apps for multiple operating systems. "
This seems like a new way to create app: Create an (AI) app that creates apps.
Requirement Minimum
Processor AMD Ryzen AI 300-series
But let’s be honest, AMD has been an extremely bad citizen to non-corporate users.
For my iGPU I have to fake GFX900 and build things from source or staging packages to get that working. Support for GFX90c is finally in the pipeline…
The improvements feel like a bodyguard finally letting you through the door just because NVIDIA is eating their lunch and they don’t want their club to be empty.
They strongarm their customers to using “Enterprise” GPUs to be able to play with ROCm, and are only broadening their offerings for market share purposes.
Really shouldn’t reward this behavior.
It's also probably worth trying Vulkan inference. It is now faster than ROCm - both tg and pp over 16k ctx - on Strix Halo so maybe you'll see the benefits too.
You do this thing not because you expect consumers with 5 year old hardware to provide meaningful utilization but as a demo ("let me grab my old gaming machine and do some supercomputing real quick") and a signal that you intend to stay the course. AMD management hasn't realized this even after various Nvidia people said that this was exactly why they did it, at some point the absence of that signal is a signal that the AMD compute ecosystem is an unreliable investment, no?
> multiple factual errors
> advertising re-explanation
Found the bot shill account.