(Still cool, still would benefit from better benchmarks)
I really appreciate thriftful & resourceful points of view. Exploring what if, looking for use is such a great virtue.
"I turned a $95 AMD APU into a 16GB VRAM GPU and it can run stable diffusion!"
I have the 4650G APU, and the best way to describe it is: lacking of support. This was even more true 3 yo than now. rocm (is) was absolutely dogshit then, I know this because I tried to do the same when that post was made. You have to compile everything from scratch, get the relevant patches, and even then, xformers which is a library that accelerate diffusion model inferencing was not supported for renoir or rocm back then. Yes, you can generate an image, but it was much slower, and rigged with bugs. You couldn'rt update rocm because it broke compatibility, and it was partly the reason I got into nixos. That being said, those APUs are a power house. Nowadays I can run decent agentic workflows on them (I have 64gb of ddr4 ram, ie APU can suck as much as it needs with the latest linux kernels).
Just note, diffusion models are still second class citizens on AMD apus even GPUs. But then again, nothing close right now on the market except for what apple offers.
But I'm always interested in first hand experiences of how good is it really - I'm pretty cynical about the idea that AMD actually knows what it takes to build good software end-to-end.
> I have an RTX 5070 with 12 GB VRAM and I wanted to run glm-4.7-flash:q8_0, which is a 31.8 GB model. The standard options are:
> Offload layers to CPU — works, but drops token/s by 5–10× because CPU RAM has no CUDA coherence. You end up waiting. Use a smaller quantization — you lose quality. At q4_0 the model is noticeably worse on reasoning tasks.
> Buy a bigger GPU — not realistic for consumer hardware. A 48 GB card costs more than a complete workstation.
> None of those felt right, so I built an alternative: route the overflow memory to DDR4 via DMA-BUF, which gives the GPU direct access to system RAM over PCIe 4.0 without a CPU copy involved.
And then limps home with this caveat on the closest thing to a benchmark:
> The PCIe 4.0 link (~32 GB/s) is the bottleneck when the model overflows VRAM. The best strategy is to shrink the model until it fits — either with EXL3 quantization or ModelOpt PTQ — and use GreenBoost's DDR4 pool for KV cache only.
I think the reason it refers it to DDR4 is because that is how the user explained it to their coding agent. LLMs are great at perpetuating unnecessary specificity.
GRUB_CMDLINE_LINUX_DEFAULT="quiet splash amdttm.pages_limit=5242880 ttm.pages_limit=5242880"
In any case, loading a gigantic model just to use system RAM is absurdly slow (due to mem bandwidth), like 1-5 t/s, so it's not practical. It'd take a whole day to process one 86k token request. Just pay a cloud provider $0.01 to do it in 10 seconds.
Does this make sense? I'd have thought the KV is guaranteed to be used 100% of the time while say in a MoE the same can't be said of the weights.
Though I suppose if you're shooting for huge context then having that allocation go into ram makes sense specially when its allocated but not used yet
tandr•3d ago