Framework Desktop (Ryzen AI Max+ 395) 64GB
Key facts
The Framework Desktop (Ryzen AI Max+ 395) 64GB has 64 GB shared memory. 29 of 42 models we track fit fully at standard quality (estimate).
No checked euro price yet. Checked . Framework (Germany).
Framework's small AMD desktop, sold as a kit. The graphics get a fixed part of its shared memory.
Price
No price yet
We have no checked euro price yet. Price details below say what is known.
Memory
64 GB shared memory
48 GB of it for the graphics
Good for
Up to Qwen2.5 32B
The largest well-known model it runs well: a mid-size model, at standard quality.
Comfortable~9.4 tok/sWhich models run on it
Each AI model needs a certain amount of memory. This is what fits at standard quality with a normal-length conversation (about 12 pages of text), and how fast the answers would come.
| AI model | Runs? | Speed |
|---|---|---|
| LFM3-1.7Bsmall | Runs | Fast~164 tok/s |
| Llama 3.2 3Bsmall | Runs | Fast~70 tok/s |
| Spark-X2.6-4Bsmall | Runs | Fast~68 tok/s |
| Mistral 7B v0.3small | Runs | Fast~37 tok/s |
| NVIDIA Nemotron 3.5 Bolt E4Bsmall | Runs | Fast~32 tok/s |
| Llama 3.1 8Bsmall | Runs | Fast~35 tok/s |
| Gemma 2 9Bsmall | Runs | Fast~27 tok/s |
| Qwen2.5 14Bmid-size | Runs | Comfortable~19.6 tok/s |
| Phi-4 14Bmid-size | Runs | Comfortable~19.4 tok/s |
| DeepSeek R1 Distill Qwen 14Bmid-size | Runs | Comfortable~19.2 tok/s |
| gpt-oss-20bmid-size | Runs | Fast~88 tok/s |
| Mistral Small 3.2 24Bmid-size | Runs | Comfortable~13.0 tok/s |
| Gemma 4 26B A4Bmid-size | Runs | Not estimated |
| OrcaCyber Nano 1.0mid-size | Runs | Comfortable~12.6 tok/s |
| Gemma 2 27Bmid-size | Runs | Comfortable~11.0 tok/s |
| Gemma 3 27Bmid-size | Runs | Comfortable~11.6 tok/s |
| Qwen3.8-27Bmid-size | Runs | Comfortable~12.0 tok/s |
| Muse Glimmer 30Bmid-size | Runs | Comfortable~12.1 tok/s |
| NVIDIA Nemotron 3.5 Lightningmid-size | Runs | Not estimated |
| Qwen3 Coder 30B A3Bmid-size | Runs | Not estimated |
| Gemma 4 31Bmid-size | Runs | Comfortable~10.3 tok/s |
| Qwen3 32Bmid-size | Runs | Comfortable~9.3 tok/s |
| Qwen2.5 32Bmid-size | Runs | Comfortable~9.4 tok/s |
| Nanbeige4.3-33Bmid-size | Runs | Comfortable~9.1 tok/s |
| Qwen3.6-35B-A3Bmid-size | Runs | Not estimated |
| Recursive Flintmid-size | Runs | Not estimated |
| Llama 3.3 70Blarge | Runs | Slow~4.5 tok/s |
| DeepSeek-R1-Distill 70Blarge | Runs | Slow~4.5 tok/s |
| Qwen2.5 72Blarge | Doesn't fit | – |
| NVIDIA Nemotron 3.5 Bolt 74B-A9Blarge | Runs | Not estimated |
| Kolibri-1large | Doesn't fit | – |
| Llama 4 Scoutvery large | Doesn't fit | – |
| gpt-oss-120bvery large | Doesn't fit | – |
| Laguna S 2.1very large | Doesn't fit | – |
| NVIDIA Nemotron 3.5 Super VLvery large | Doesn't fit | – |
| Ornith-1.5-122Bvery large | Doesn't fit | – |
| Qwen3.8-Flash-Nextvery large | Doesn't fit | – |
| MiniMax M3.1 Flashvery large | Doesn't fit | – |
| Reflection Beam 1.0very large | Doesn't fit | – |
| DeepSeek V4.1 Flash 1017very large | Doesn't fit | – |
| Meta Muse Spark Openvery large | Doesn't fit | – |
| Kimi K3.1 Provery large | Doesn't fit | – |
Not estimated: these models use only part of themselves for each word. Our speed estimate can't model that yet, so we leave it out instead of guessing.
Other quality levels
Higher quality needs more memory. Standard quality is what most people run at home.
- Q4 · standard (smallest that keeps quality)
- Q5 · a bit sharper, a bit bigger
- Q8 · near full quality, almost twice Q4
- FP16 · full quality, over three times Q4
Swipe the table sideways to see every quality level.
| AI model | Q4 | Q5 | Q8 | FP16 |
|---|---|---|---|---|
| LFM3-1.7B | Runs | Runs | Runs | Runs |
| Llama 3.2 3B | Runs | Runs | Runs | Runs |
| Spark-X2.6-4B | Runs | Runs | Runs | Runs |
| Mistral 7B v0.3 | Runs | Runs | Runs | Runs |
| NVIDIA Nemotron 3.5 Bolt E4B | Runs | Runs | Runs | Runs |
| Llama 3.1 8B | Runs | Runs | Runs | Runs |
| Gemma 2 9B | Runs | Runs | Runs | Runs |
| Qwen2.5 14B | Runs | Runs | Runs | Runs |
| Phi-4 14B | Runs | Runs | Runs | Runs |
| DeepSeek R1 Distill Qwen 14B | Runs | Runs | Runs | Runs |
| gpt-oss-20b | Runs | Runs | Runs | Runs |
| Mistral Small 3.2 24B | Runs | Runs | Runs | Doesn't fit |
| Gemma 4 26B A4B | Runs | Runs | Runs | Doesn't fit |
| OrcaCyber Nano 1.0 | Runs | Runs | Runs | Doesn't fit |
| Gemma 2 27B | Runs | Runs | Runs | Doesn't fit |
| Gemma 3 27B | Runs | Runs | Runs | Doesn't fit |
| Qwen3.8-27B | Runs | Runs | Runs | Doesn't fit |
| Muse Glimmer 30B | Runs | Runs | Runs | Doesn't fit |
| NVIDIA Nemotron 3.5 Lightning | Runs | Runs | Runs | Doesn't fit |
| Qwen3 Coder 30B A3B | Runs | Runs | Runs | Doesn't fit |
| Gemma 4 31B | Runs | Runs | Runs | Doesn't fit |
| Qwen3 32B | Runs | Runs | Runs | Doesn't fit |
| Qwen2.5 32B | Runs | Runs | Runs | Doesn't fit |
| Nanbeige4.3-33B | Runs | Runs | Runs | Doesn't fit |
| Qwen3.6-35B-A3B | Runs | Runs | Runs | Doesn't fit |
| Recursive Flint | Runs | Runs | Runs | Doesn't fit |
| Llama 3.3 70B | Runs | Doesn't fit | Doesn't fit | Doesn't fit |
| DeepSeek-R1-Distill 70B | Runs | Doesn't fit | Doesn't fit | Doesn't fit |
| Qwen2.5 72B | Doesn't fit | Doesn't fit | Doesn't fit | Doesn't fit |
| NVIDIA Nemotron 3.5 Bolt 74B-A9B | Runs | Doesn't fit | Doesn't fit | Doesn't fit |
| Kolibri-1 | Doesn't fit | Doesn't fit | Doesn't fit | Doesn't fit |
| Llama 4 Scout | Doesn't fit | Doesn't fit | Doesn't fit | Doesn't fit |
| gpt-oss-120b | Doesn't fit | Doesn't fit | Doesn't fit | Doesn't fit |
| Laguna S 2.1 | Doesn't fit | Doesn't fit | Doesn't fit | Doesn't fit |
| NVIDIA Nemotron 3.5 Super VL | Doesn't fit | Doesn't fit | Doesn't fit | Doesn't fit |
| Ornith-1.5-122B | Doesn't fit | Doesn't fit | Doesn't fit | Doesn't fit |
| Qwen3.8-Flash-Next | Doesn't fit | Doesn't fit | Doesn't fit | Doesn't fit |
| MiniMax M3.1 Flash | Doesn't fit | Doesn't fit | Doesn't fit | Doesn't fit |
| Reflection Beam 1.0 | Doesn't fit | Doesn't fit | Doesn't fit | Doesn't fit |
| DeepSeek V4.1 Flash 1017 | Doesn't fit | Doesn't fit | Doesn't fit | Doesn't fit |
| Meta Muse Spark Open | Doesn't fit | Doesn't fit | Doesn't fit | Doesn't fit |
| Kimi K3.1 Pro | Doesn't fit | Doesn't fit | Doesn't fit | Doesn't fit |
Ran a model on this machine? Send us your llama-bench result.
Speeds and memory are our estimate from the hardware specs, not a measured test. Prices are dated and may change. How we calculate
Sources and method
Fit and speed are our estimate from memory size and memory speed, not a measured test. Checked . Framework (Germany).
Price details and parts
No price we use: out of stock at Framework's web shop for Germany on 21 Oct 2026, listed there at €2,209 for the Max+ 395 64 GB DIY Edition, VAT included, without SSD or operating system. AMD's Variable Graphics Memory (VGM) FAQ: up to 48 GB of a 64 GB Ryzen AI Max+ system for the graphics, the memory we count.
Source: Framework (Germany), checked 21 Oct 2026.
What this is
- Framework Desktop DIY Edition, Ryzen AI Max+ 395, 64 GB LPDDR5x (soldered), as listed on Framework's web shop for Germany
- No SSD or operating system included in the DIY Edition price
Prices exclude shipping. Euro prices include VAT. Dollar prices are converted from our euro prices without EU VAT and before US sales tax, so they are an estimate.
Technical specs
- Shared memory
- 64 GB shared memory (48 GB of it for the graphics)
- Usable for AI models (our estimate)
- About 48 GB. The system keeps the rest for itself.
- Memory speed (bandwidth)
- 256 GB/s. How fast the chip can read the model; higher means faster answers. Source
- Power (maximum)
- 120 W. Source
- In short
- 64 GB of shared memory. AMD lets the graphics use up to 48 GB of it, and that is what we count.
Common questions
- Can the Framework Desktop (Ryzen AI Max+ 395) 64GB run Llama 3.3 70B?
- It fits, but slowly: about 4.5 tokens per second (tok/s) by our estimate (Slow). A bit slower than you read. Fine for short answers. At standard quality and with a normal-length conversation, Llama 3.3 70B needs about 46 GB of memory, and the Framework Desktop (Ryzen AI Max+ 395) 64GB has about 48 GB it can use.
- Which AI models run fully on the Framework Desktop (Ryzen AI Max+ 395) 64GB?
- By our estimate, 29 of the 42 models we track run fully in its memory at standard quality: LFM3-1.7B, Llama 3.2 3B, Spark-X2.6-4B, Mistral 7B v0.3, NVIDIA Nemotron 3.5 Bolt E4B, Llama 3.1 8B, Gemma 2 9B, Qwen2.5 14B, Phi-4 14B, DeepSeek R1 Distill Qwen 14B, gpt-oss-20b, Mistral Small 3.2 24B, Gemma 4 26B A4B, OrcaCyber Nano 1.0, Gemma 2 27B, Gemma 3 27B, Qwen3.8-27B, Muse Glimmer 30B, NVIDIA Nemotron 3.5 Lightning, Qwen3 Coder 30B A3B, Gemma 4 31B, Qwen3 32B, Qwen2.5 32B, Nanbeige4.3-33B, Qwen3.6-35B-A3B, Recursive Flint, Llama 3.3 70B, DeepSeek-R1-Distill 70B, NVIDIA Nemotron 3.5 Bolt 74B-A9B.
- How fast is the Framework Desktop (Ryzen AI Max+ 395) 64GB with AI models?
- With Qwen2.5 32B, the largest well-known model it runs well, about 9.4 tokens per second (tok/s) at standard quality (our estimate, not a measured test). Comfortable: About as fast as you read. Fine for chat.