this post was submitted on 07 Sep 2026
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No onboard graphics unfortunately, that would have been the easy way out.
Well it works anyway even with a bit of occupied vram, but you could also buy a cheap videocard to use as an output. I have small intel card like that in my server for jellyfin transcoding, I think I paid 60€ for it, it hardly uses any power
I actually did exactly that previously! I had both an RX 6800 XT and an RX 6600 in my system and I used the 6600 for video output. Unfortunately, this cuts my RX 6800 XT from PCIe 4 16x to PCIe 4 8x and severely slows down model loading for llama-swap. Joys of the X570!
And yes, I do have it running right now with a bit of occupied VRAM, but I need to limit my model to 14 GB to leave 2 GB free for GNOME Shell. I really want one of those 64 GB UMA Mac Mini, I heard they work really well because the GPU has direct access to system RAM.
So I have a framework laptop with ryzen ai cpu that uses 48gb of shared ram, and it does run Q4 llms fine enough, but I'm not sure it compares to a real GPU.
On my desktop I have an RX 7900 XTX but I've only dabbled in image generation so far, so right now I couldn't really tell you the difference.
24 GB VRAM. Damn, jealous! My 16 GB seems pitiful in comparison 😅
I do wonder if the Ryzen AI CPUs compare with Apple's UMA. I'm mostly interested in LLM inference for code generation and automation.
Yeah I was lucky to buy a 6900XT when it was near the lowest price, so I sold that and added a couple hundred for the 7900, seemed like a good future-proofing move, given the times we're living in.
I think Apple silicon is faster than my generation of Ryzen, but the newest (Strix something?) with the LPDDRGGFASEWARGH5 memory should be faster yet. Of course buying all that memory right now would be quite painful.