Framework Desktop

I bought a Framework Desktop mainboard only, 128gb AMD 395+ setup, and put it in an existing case with PSU i had on hand. I had a fan for it. It was $2699 +$15 expedited shipping. The case and PSU/etc was probably $200 more (It's an overkill 850w SFX PSU and cheap but great ITX case from Deepcool). I started with Ubuntu but pivoted to Fedora over the summer after a recommendation from a security engineer friend. And it is mostly easier. Generic setup doc here: (not fully updated with GUFO): https://github.com/Selicos/AI_Tools_Public/blob/main/docs/Fe... Key changes for other distros are how to load drivers/apps and get running. Ubuntu has a recent CVE issue but worked fine. CachyOS/etc set up for more Windows like UI and transplants was fine but not focused on the Stricx Halo setup. Although I do see close to RTX 5070 performance in most games I've tested. I was running LM Studio and some other runtimes for ease of setup and model switching. Once you settle on a runtime and model setup you like it is easy to set it up, let it run, and manage as needed. Last week I set up GUFO, as it targets the AMD Strix Halo hardware and is efficient for Qwen Flash Next. I could probably get the work done with Qwen 3.8 27B but I like how QFN thinks and lets me follow along. Starting at about 260k context, I see 88gb of RAM usage when loaded and about 108gb when in use, depending on context overhead. I do have to restart the model every so often as Copilot Chat in VS Code starts failing. I…

I've run 3.8 flash next k4_xl on my Strix halo box (128GB). And in the work I have done so far it was not significantly worse than recent GPT (running default model on pro plan). Admittedly I was not doing complex work (reorganizing a jupyterbook), but I could not see significant difference in the quality of the work. It was a striking difference to Laguna s 2.1 which I had tried just before (much faster and much better quality).

I love my frameworks strix halo box. 16 zen 5 cores with way more memory bandwidth than they should have for a desktop. Only missing the x3d cache

> A GPU in your basement is a depreciating asset All decades prior and up to about a year ago, I would have agreed with you. My Framework Desktop, however has appreciated in value by 75% since I bought it. Will it stay there for a long time? Probably not. But it shows that there are no hard and fast rules about things anymore.

I just bought a Framework Desktop. Would have been nice to get it at the introductory price, or perhaps the new 192gb model refresh they’re now teasing, but I settled and got a 64 gb model. At the time, the 128’s price had already risen again, but the 64’s price was still at a lower price. 64 can still easily do a Qwen 4.8 model, so I’m relatively happy with my purchase… plus, it’s price change has caused it to quickly appreciate in value… so I could sell it if my situation ever turned dire lol

Most games play just fine at 1440 or 4K with upscaling. Cyberpunk ray tracing is a bad experience, but I just finished 007 First Light on ultra settings with an 5k monitor using FSR and it was 45+ fps the whole time. Targeting 4K with fsr was 60fps. I’m not a hardcore gamer or a pro reviewer, but it mostly just works without having to change much. If I were a hardcore gamer I think I would go with a better setup. My big complaint for gaming is that they never enabled HDMI CEC. Apparently the pins are wired but they never shipped the firmware.

I have the Framework Desktop with 395+ 128gb RAM Today I am pretty happy with it. LLMs are finally good enough (fast enough with MTP+MoE, but also just much better in capability) that I can fit local ones into real tasks, and I've used image generation with invokeAI to do some genuinely useful things like rendering concepts for a renovation. I mostly use lemonade-server and invokeAI for my workloads, previously I used llama-swap, but lemonade is just an easier to manage system. ROCm is finally usable. Up until end of Q1 2026 it felt like a total waste of money largely due to AMD. ROCm was unusable all of last yera; there was an entire month where PyTorch crashed just trying to multiply two matrices due to AMD Linux driver issues. kyuz0's toolboxes were the only way to do anything really on the machine. Thankfully things are in a good state now, finally. I probably actually only need ~64gb of ram. There aren't a ton of high parameter count MoE with a small enough active set that it feels nice to use. But it is nice I can have many models or different modalities in memory at the same time, which is what the LMX Omni "models" do. The numbers in the article for gptOSS feel a little irrelevant now. Prompt processing is definitely an issue, and diffusion is very very slow. PP speed hits hard you if you run an agent and try to compact context. Realistically most files are not large enough that it's a huge deal, but it does make large-scale agentic work slow.

I run Qwen 3.6 on my Framework Desktop 128GB, and it's very performant. I know Framework has had to raise the price since I preordered mine, but they're still well under half the cost of that Macbook.

I think people buying laptops for AI use are, sorry, just plain crazy. You overpay for the screen and keyboard and battery and whatever, plus you get much worse thermal performance because of basic physics (area vs volume). My Framework Desktop has a Noctua cooler which works really well. [Tangent: all my life I've been downvoted into a smoking hole in the ground, particularly on reddit r/hardware, for questioning the wisdom of laptops for high performance computing, including gaming. Everyone insists they need the mobility, and then just leave it plugged in the whole time, absolutely refusing to admit it's about aesthetic preference.]

I’ve got the Framework desktop with strix halo. You can reserve memory for the GPU, but it’s straightforward at least on Linux to have the GPU dynamically grab memory as needed. I’ve got my VRAM set to 512MB and regularly use 120GB+for AI stuff.

Not who you asked, but I've got a Framework desktop (strix halo) with 128GB RAM. In linux up to about 112GB can be allocated towards the GPU. I can run Qwen3.5-122B (4-bit quant) quite easily on this box. I find qwen3-coder-next runs quite well at about 36tok/sec. Qwen3.5-27b is a bit slower at about ~24tok/sec but that's a dense model.

The Framework Desktop has a Ryzen 395 chip that is able to allocate memory to either the CPU or GPU. I've been able to allocate 100+gb to the GPU, so even big models can run there. Most recently I used it to develop a script to help me manage email. The implementation included interacting with my provider over JMAP, taking various actions, and implementing an automated unsubscribe flow. It was greenfield, and quite trivial compared to the codebases I normally interact with, but it was definitely useful.

As another data point. Running Qwen3.5 122B at 35t/s as a daily driver using Vulcan llama.cpp on kernel 7.0.0rc5 on a Framework Desktop board (Strix Halo 128). Also a pair of AMD AI Pro r9700 cards as my workhorses for zimageturbo, qwen tts/asr and other accessory functions and experiments. Finally have a Radeon 6900 XT running qwen3.5 32B at 60+t/s for a fast all arounder. If I buy anything nvidia it will be only for compatibility testing. AMD hardware is 100% the best option now for cost, freedom, and security for home users.