AI PC · desktop
Framework Desktop (Ryzen AI Max+ 395) review
128GB of unified memory in a 4.5L desktop built from standard parts.
- Memory
- 128 GB
- Bandwidth
- 256 GB/s
- AI compute
- 50 TOPS
- £ / GB
- £27
What it runs
With 128 GB of unified memory, leaving the system its share and room for context, you can load at 4-bit a model up to roughly
~141B params
Verdict
At $3,449 at the time of writing, the DIY build (no SSD, operating system or fan) undercuts the GMKtec EVO-X2, the Beelink GTR9 Pro, the DGX Spark and a 128GB Mac Studio, but it's out of stock. A dense 70B at Q4 runs at about 5 tok/s on it.
BEST FOR
- + A DIY 128GB box for less than the GMKtec or the Spark at the time of writing
- + Windows or Linux on standard PC parts
- + People who repair their own machines and fit their own SSDs
NOT FOR
- - Fast answers from big dense models
- - Anyone whose tools need CUDA
If you’re pricing a 128GB box to run models at home, the Framework Desktop will be on your list, and it’s built from parts you’d recognise from any desktop PC. It uses a standard Mini-ITX board, a FlexATX power supply and a 120mm fan mount. The board carries AMD’s Ryzen AI Max+ 395 (the chip codenamed Strix Halo) with 128GB of LPDDR5X soldered on, which means the memory can’t be upgraded later.
The take
You can’t order the 128GB Ryzen AI Max+ 395 system, which is out of stock, but Framework lists it at $3,449 at the time of writing (12 November 2026). That’s the DIY build, which comes without an SSD, an operating system or a fan. The smaller configurations are out of stock as well, a 32GB at $1,269 and a 64GB at $1,959.
In its November 2025 review, ServeTheHome said that a few weeks earlier, many would have called $1,999 a high base price for a 128GB system. By Framework’s own account in January 2026, the price of the memory chips in its 128GB build had spiked the most.
Even so, at the time of writing the 128GB Framework is about $50 under the GMKtec EVO-X2 at about $3,500, although the GMKtec’s price includes a 1TB SSD. The Beelink GTR9 Pro is $4,199 on pre-order. The DGX Spark is $6,950, so the Framework is half the money. A 128GB Mac Studio with the M5 Max is $5,099 with a 512GB SSD or $5,399 with 1TB, which puts the Framework at 64 to 68% of it. The Spark’s memory is a little quicker on paper, 273 GB/s against the Framework’s 256 GB/s.
Framework now leads its desktop range with a 192GB model on the Ryzen AI Max+ PRO 495. It’s a 16-core, 32-thread chip with 192GB of LPDDR5X-8533 on the same 256-bit bus, and an NPU rated at up to 55 TOPS. That memory speed works out at about 273 GB/s, which matches the Spark. The 495’s PCIe x4 slot is open-ended and supplies up to 25W, though the default case doesn’t expose it. The DIY version is $6,799 on pre-order at the time of writing, with the November batches sold out and Batch 3 shipping in December. The pre-built starts at $7,449.
What it’ll run
A model on the Framework isn’t held to the VRAM on a graphics card, because the memory is unified (one pool that the processor and the graphics core share). AMD says the graphics core can reach 112GB of the 128GB. Framework’s own figure is 96GB of VRAM in Windows, with Linux able to go higher, although Jeff Geerling hit errors above about 108GB.
A 70B model at Q4 is 42.5GB, less than half of the 96GB Windows allows. The model Framework suggests for the 128GB box, DeepSeek V4 Flash 0731 at UD-IQ2_XXS, is 90.9GB. On pre-production units in August 2025, Geerling had gpt-oss-120b at about 33 tok/s, gpt-oss-20b at about 45 and Qwen2.5 14B at about 23.
On a dense model, the Framework’s token speed comes down to memory bandwidth (how fast the chip can read its memory), since every token means reading all of the weights. Strix Halo’s memory bandwidth is 256 GB/s in theory, and lhl on the Level1Techs forum measured about 215 GB/s. Divide that by the 42.5GB of a 70B at Q4 and the ceiling is about 5 tok/s, which is almost exactly the 4.97 Geerling measured. Double figures at that size takes a mixture-of-experts (MoE) model like gpt-oss-120b, which only works through part of its weights for each token. At the other end, a 3B ran at 88 tok/s in those tests.
The 50 TOPS NPU doesn’t help llama.cpp, because AMD’s route to it is ONNX Runtime GenAI. Vulkan was ahead of ROCm in Geerling’s August 2025 tests, but kyuz0’s Strix Halo toolboxes now recommend ROCm for performance. In the same tests, Ollama seemed to use only the CPU cores on its defaults, and an owner reported in a GitHub issue that Ollama’s ROCm provider saw just the 512MB set aside in the BIOS.
Who should buy it
The Framework makes sense if you want 128GB for less than the GMKtec, the Beelink, the Spark or a Mac Studio, going by list prices at the time of writing, and you can wait for stock. You’ll need to budget on top for an SSD, a fan and an operating system, which can be Windows or Linux. If you’d rather fix a machine than replace it, the board, power supply and fan are all standard sizes, and Framework sells spares through its Marketplace. There are two M.2 2280 sockets that take up to 8TB each. The warranty is a year, with three years as a paid option.
It’s the wrong machine if you want fast answers from a big dense model, with a 70B at Q4 managing only about 5 tok/s. And there’s no CUDA on AMD graphics, which rules it out if your tools depend on it. The 128GB build is still out of stock, even though the configurator says it ships within 5 business days. The UK store also warns that deliveries to Europe are delayed by up to 7 days.
Starting settings
Starting configurations, each with where its figure comes from. A sensible starting point, not gospel - tune to your own kit.
Big MoE model
gpt-oss-120b (MoE, ~65GB); graphics memory split set high
gpt-oss-120b is about 65GB, well inside the 112GB the graphics core can reach. Geerling ran it at about 33 tok/s.
Dense large model
Llama 70B Q4 (~42.5GB)
A 70B at Q4 is 42.5GB, so it fits with plenty left over for context, but memory bandwidth caps it at about 5 tok/s.
Everyday use
Qwen2.5 14B Q4
Qwen2.5 14B at Q4 ran at about 23 tok/s in Geerling's tests.
Backend tip
llama.cpp on ROCm or Vulkan
Try both. kyuz0, who maintains the Strix Halo toolboxes on GitHub, now recommends ROCm, but Vulkan was the quicker of the two in Geerling's August 2025 run. In one owner's GitHub issue, Ollama saw only the 512MB set aside in the BIOS, so check Ollama is using the graphics core and not just the CPU.
Notes from people who've run one
Each note below names its source underneath.
- “
In Geerling's August 2025 tests, a single pre-production board drew around 97W running gpt-oss-20b at about 45 tok/s, and around 140W on Qwen2.5 14B at about 23.
- “
On the Level1Techs forum, lhl measured memory bandwidth topping out at about 215 GB/s at the graphics core, out of a theoretical 256 GB/s.
- “
In August 2025, Geerling couldn't find a way to use the 50 TOPS NPU.
1 claim(s) we couldn't fully verify
- · Ollama's current behaviour on Windows - GitHub issues show it misreading GPU memory; Windows support for llama.cpp and Ollama on this box was not checked.
Sources we drew on
The spec sheets, vendor pages and reviews behind this page.
- Benchmark Framework Desktop Mainboard and 4-node clusterJeff Geerling (GitHub) · review
- AMD Ryzen AI Max+ 395 product pageAMD · primary source
- Framework Desktop configuratorFramework · primary source
- DGX Spark hardware overviewNVIDIA · primary source
- Strix Halo (Ryzen AI Max+ 395) LLM benchmark resultslhl, Level1Techs forum · forum
- Framework Desktop Review: A Solid AMD Strix HaloServeTheHome · review
- Framework Desktop overview and specsFramework · primary source
- Framework Desktop configurator (UK)Framework · primary source
- Framework Desktop PRO 400 configuratorFramework · primary source
- Framework Desktop MainboardFramework · primary source
- Updates on memory pricing and navigating the volatile memory marketFramework · primary source
- AMD Variable Graphics Memory FAQAMD · primary source
- Ryzen AI Software: LLM overviewAMD · primary source
- ROCm on Radeon and Ryzen: Windows compatibilityAMD · primary source
- NVIDIA Marketplace: DGX SparkNVIDIA · primary source
- GeForce RTX 3090 product pageNVIDIA · primary source
- Mac Studio tech specsApple · primary source
- Mac Studio (M5 Max) configuratorApple · primary source
- Beelink GTR9 Pro product pageBeelink · primary source
- Increasing VRAM allocation on AMD AI APUs under LinuxJeff Geerling · review
- Ryzen AI Max - not so much? (dsfd)Framework community forum · forum
- AMD Strix Halo toolboxeskyuz0 (GitHub) · forum
- GFX1151 TTM setting is not detected (#13681)Ollama issue (GitHub) · forum
Common questions
How much memory can the Framework Desktop give to models?+
It has 128GB of LPDDR5X, soldered and not upgradeable. AMD says the graphics core can reach 112GB of that, with 96GB set aside as graphics memory and the rest shared. Framework quotes 96GB of VRAM in Windows and says Linux can be set higher. On Linux, Jeff Geerling got errors above about 108GB.
Is the Framework Desktop fast for local LLMs?+
Not on big dense models. On pre-production units in August 2025, Jeff Geerling had a 70B at Q4 running at about 5 tok/s, while the mixture-of-experts gpt-oss-120b ran at about 33. A dense model is held back by memory bandwidth, which is 256 GB/s in theory and about 215 GB/s as lhl measured it on the Level1Techs forum. The DGX Spark is a little ahead on paper at 273 GB/s.
Does it need CUDA to run AI models?+
No, and it can't use CUDA at all, as the Radeon 8060S is AMD graphics. On Linux, llama.cpp runs on the graphics core through both Vulkan and ROCm. In Jeff Geerling's August 2025 tests, Ollama ran on its defaults but seemed to use only the CPU cores. On Windows, AMD's ROCm compatibility matrix lists the Ryzen AI Max+ 395, though a footnote says PyTorch includes ROCm components there while the full stack isn't supported yet.
