Remind me of what are the downsides of possibly getting a framework desktop for christmas.
That you have to wait until Christmas when you can get a HP mini today: https://www.storagereview.com/review/hp-z2-mini-g1a-review-running-gpt-oss-120b-without-a-discrete-gpu
For those interested in the desktop-capable requirements: 16 GB GPU. 9070 XT?
For lighting fast performance with the OpenAI GPT-OSS 20B model, users can use the AMD Radeon™ 9070 XT 16GB graphics card in a desktop system.
Does it require that gen?
Not sure it requires that gen, but it definitely needs the 16GB. I have a 6700 (XT?) with 12 GB and ollama loads up gpt-oss on my CPU. Whereas models like deepseek-r1 with 14b parameters runs solely on the GPU.
~~windows only :(~~ To clarify, if you want to use a Ryzen AI NPU its windows only, but you can run gpt-oss-20b in LLMstudio on the iGPU. The performance is decent for what it is.
It looks like they're running LM Studio on an AI Max 395+. I'm confident that you can do the same on Linux. Here's a post on Framework's blog talking about comparable hardware, the Framework Desktop (also 128GB Ryzen AI Max 395+).
https://frame.work/blog/using-a-framework-desktop-for-local-ai
The simplest application we’ve found to get started with for the text and code generation AI use case is LM Studio. It’s built on top of llama.cpp, which is an extremely powerful and extensible open source inference library that we’ll go into in a future guide. LM Studio packages it up in a user-friendly application that runs on both Windows and Linux. With this guide, we’ll focus on Windows, but the same settings will work on Linux. Note that as of June 2025, inference runs about 20% faster on Fedora 42 than on Windows 11.
EDIT: Also, getting a bit off the point, I'd guess that something like the Framework Desktop (not necessarily specifically a Framework Desktop, but at least something in a more desktop/server-like mini-ITX form factor) is probably preferable to a laptop-like ROG Flow Z13 if the goal is a pure AI box, because it'll be able to dissipate heat better. It looks like Asus capped the Z13's power usage, I imagine because of the heat (and maybe battery, though I can't imagine running LLM stuff on a Z13 on battery would be very practical):
https://old.reddit.com/r/FlowZ13/comments/1ki68oj/a_bit_disappointed_the_new_z13_2025_performance/
Z13 2025 TDP is fixed to 35W-55W quiet mode, 45W~70W performance mode, 60W-86W turbo mode and up-to 93W manual mode
https://frame.work/blog/introducing-the-framework-desktop
In a desktop form factor, we get to unlock every bit of its performance with 120W sustained power and 140W boost while staying quiet and cool.
EDIT2: Also, I don't think that Windows is a great fit with the hardware, if the aim is a dedicated AI box. Like, the reason you'd want to get an AI Max for generative AI purposes in 2025 is because you want something that isn't insanely expensive that can have a whole lot of memory hooked up to a GPU. People noticed that "unified" systems that have shared GPU and CPU memory is a good way to achieve that. Okay...but if you do that, then what you're paying for is a lot of GPU-accessible memory. But then any other software that you're running on the machine is consuming some of that memory. You probably want to be able to run the thing as stripped-down as possible, and it's gonna be rather more practical to do that with a minimal Linux box than with a Windows 11 machine.
Just ran it on my Framework 13 w/ Ryzen AI 9 HX 370. Its not able to use the NPU at all which is as expected in Linux. :(
Still getting 20 tokens/sec is not bad off the GPU/CPU and 2x what I was seeing on Gemma 3 12b.
I have a Radeon RX 9060 XT 16GB which I may try to plug in and bench too.
This is a nice uplift in performance but its still not utilizing dedicated hardware.
Ryzen AI 9 HX 370 w/ 870M + 64GB gpt-oss-20b:
"stats": {
"stopReason": "userStopped",
"tokensPerSecond": 19.52169768309363,
"numGpuLayers": -1,
"timeToFirstTokenSec": 7.363,
"totalTimeSec": 24.127,
"promptTokensCount": 1389,
"predictedTokensCount": 471,
"totalTokensCount": 1860
Ryzen 9 3900x w/ RX 9060 XT 16GB + 128GB gpt-oss-20b
"stats": {
"stopReason": "eosFound",
"tokensPerSecond": 21.621644364467713,
"numGpuLayers": -1,
"timeToFirstTokenSec": 0.365,
"totalTimeSec": 15.124,
"promptTokensCount": 118,
"predictedTokensCount": 327,
"totalTokensCount": 445
Ryzen AI 9 HX 370 w/ 870M + 64GB gemma-3-12b
"stats": {
"stopReason": "eosFound",
"tokensPerSecond": 7.866126447290582,
"numGpuLayers": -1,
"timeToFirstTokenSec": 12.937,
"totalTimeSec": 12.458,
"promptTokensCount": 1366,
"predictedTokensCount": 98,
"totalTokensCount": 1464
Ryzen 9 3900x w/ RX 9060 XT 16GB + 128GB gemma-3-12b
"stats": {
"stopReason": "eosFound",
"tokensPerSecond": 27.62905167372976,
"numGpuLayers": -1,
"timeToFirstTokenSec": 0.31,
"totalTimeSec": 19.219,
"promptTokensCount": 2975,
"predictedTokensCount": 531,
"totalTokensCount": 3506
Thanks for the numbers. Btw, I think a NPU can't run large language models in the first place. They're meant for things like blur the background in video conferences, or help with speech recognition or such very specific smaller tasks. They only have some tens or hundreds of megabytes of memory, so a LLM/chatbot won't fit. The main thing that makes LLM inference faster is memory (RAM) bandwith and speed.
I added a few more numbers. I may pull my old MI25 out of mothballs and bench that to.
lightning fast tokens per second
lightning fast sounds fast, tokens per second sounds slow…
I just benched it on my system and i was getting around 20 tokens/sec which feels as fast as remote AI and 2x faster than gemma-3-12b.
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