Framework Desktop Ryzen AI Max+ 395 64GB vs AMD Radeon AI PRO R9700
In short
The Framework Desktop Ryzen AI Max+ 395 64GB has more memory for models: 64 GB against 32 GB. At 64k context the Framework Desktop Ryzen AI Max+ 395 64GB runs 59 of the models we track fully in memory and the AMD Radeon AI PRO R9700 51; only the Framework Desktop Ryzen AI Max+ 395 64GB holds Qwen 3.5 122B-A10B. On the models both run fully in memory, the AMD Radeon AI PRO R9700 is about 2.5× as fast as the Framework Desktop Ryzen AI Max+ 395 64GB (estimated, median across models). In the US the Framework Desktop Ryzen AI Max+ 395 64GB costs $1,899 at eBay (used) and the AMD Radeon AI PRO R9700 $1,700 at Amazon: the AMD Radeon AI PRO R9700 is $199 cheaper.
Updated . Speeds are estimates; how they are worked out.
Specifications
| Spec | Framework Desktop Ryzen AI Max+ 395 64GB | AMD Radeon AI PRO R9700 |
|---|---|---|
| Chip | Ryzen AI Max+ 395 (Radeon 8060S) | – |
| Memory bus | 256-bit | 256-bit |
| Memory bandwidth | 256 GB/s | 640 GB/s |
| FP16 compute | 59.4 TFLOPS | 191 TFLOPS |
| Software | ROCm | ROCm |
| Unified memory | 64 GB | – |
| GPU can use by default | 48 GB | – |
| GPU can use, tuned | 56 GB | – |
| Operating system | linux | – |
| Processor | Ryzen AI Max+ 395 | – |
| Storage | 1000 GB | – |
| GPU | – | Radeon AI PRO R9700 |
| Memory | – | 32 GB GDDR6 |
| Board power | – | 300 W |
| PCIe generation | – | Gen 5 |
| PCIe lanes | – | x16 |
What each runs
Estimated decode speed with the most precise quant that fits each one est.
| Model | Framework Desktop Ryzen AI Max+ 395 64GB | AMD Radeon AI PRO R9700 |
|---|---|---|
| Qwen 3.8 27B | 4tok/sQ8_0 | 12tok/sQ6_K |
| Qwen 3.5 122B-A10B | 17tok/sUD-Q2_K_XL | offload |
| Qwen 3.6 27B | 4tok/sQ8_0 | 13tok/sQ6_K |
| Qwen3 Coder Next | 21tok/sQ4_K_M | offload |
| Gemma 4 31B | 4tok/sQ8_0 | 11tok/sQ6_K |
| Mistral Small 4 119B | 22tok/sUD-Q2_K_XL | offload |
| Qwen 3.6 35B-A3B | 19tok/sQ8_0 | 55tok/sUD-Q6_K |
| Qwen 3.5 27B | 4tok/sQ8_0 | 13tok/sQ6_K |
| Gemma 4 26B-A4B | 15tok/sQ8_0 | 37tok/sQ8_0 |
| Qwen3 Next 80B-A3B | 24tok/sQ4_K_M | offload |
| Qwen 3.5 35B-A3B | 19tok/sQ8_0 | 55tok/sQ6_K |
| GLM 4.7 Flash | 10tok/sQ8_0 | 28tok/sQ6_K |
| Devstral Small 2 24B | 4tok/sQ8_0 | 12tok/sQ6_K |
| Nemotron 3.5 Lightning 30B-A3B | 22tok/sUD-Q6_K_XL | offload |
| Kimi Linear 48B-A3B | 28tok/sQ6_K | 88tok/sQ4_K_M |
| gpt-oss 20B | 21tok/sMXFP4 | 52tok/sMXFP4 |
| Qwen3 Coder 30B-A3B | 9tok/sQ8_0 | 24tok/sQ6_K |
| Qwen3 32B | 3tok/sQ6_K | offload |
| Gemma 4 12B | 10tok/sQ8_0 | 25tok/sQ8_0 |
| Qwen 3.5 9B | 12tok/sQ8_0 | 30tok/sQ8_0 |
| Gemma 3 27B | 4tok/sQ8_0 | 13tok/sQ6_K |
| Mistral Small 3.2 24B | 4tok/sQ8_0 | 12tok/sQ6_K |
| DeepSeek R1 Distill Qwen 32B | 3tok/sQ6_K | offload |
| Ministral 3 14B | 6tok/sQ8_0 | 14tok/sQ8_0 |
| Qwen3 14B | 5tok/sQ8_0 | 13tok/sQ8_0 |
| DeepSeek R1 0528 Qwen3 8B | 8tok/sQ8_0 | 19tok/sQ8_0 |
| Qwen3 8B | 8tok/sQ8_0 | 19tok/sQ8_0 |
| Phi-4 14B | 5tok/sQ8_0 | 12tok/sQ8_0 |
| Qwen 3.5 4B | 21tok/sQ8_0 | 53tok/sQ8_0 |
| Ministral 3 8B | 8tok/sQ8_0 | 19tok/sQ8_0 |
| Granite 4.2 8B | 7tok/sQ8_0 | 18tok/sQ8_0 |
| Gemma 4 E4B | 14tok/sQ8_0 | 35tok/sQ8_0 |
| LFM2.5 8B-A1B | 20tok/sQ8_0 | 49tok/sQ8_0 |
| Llama 3.1 8B | 9tok/sQ6_K | 23tok/sQ6_K |
| Qwen2.5 0.5B | 105tok/sQ8_0 | 263tok/sQ8_0 |
| Qwen3.5 0.8B | 86tok/sQ8_0 | 215tok/sQ8_0 |
| Qwen3.5 2B | 62tok/sQ5_K_M | 156tok/sQ5_K_M |
| DeepSeek R1 Distill Qwen 1.5B | 37tok/sQ8_0 | 93tok/sQ8_0 |
| MiniCPM5 2B | 26tok/sQ8_0 | 64tok/sQ8_0 |
| Mellum2.1 12B A2.5B Thinking | 23tok/sQ8_0 | 58tok/sQ8_0 |
| LFM2.5 2.6B | 20tok/sQ8_0 | 51tok/sQ8_0 |
| LFM2.5 VL 3B | 20tok/sQ8_0 | 51tok/sQ8_0 |
| Kimi VL A3B Thinking 2506 | 16tok/sQ8_0 | 41tok/sQ8_0 |
| Kimi VL A3B | 16tok/smlx-8bit | 40tok/smlx-8bit |
| granite vision 4.1 4b | 16tok/sQ8_0 | 39tok/sQ8_0 |
| granite 4.2 3b | 15tok/sQ8_0 | 38tok/sQ8_0 |
| GLM 4 9B 0414 | 13tok/smlx-5bit | 32tok/smlx-5bit |
| DeepSeek R1 Distill Qwen 7B | 12tok/sQ8_0 | 30tok/sQ8_0 |
| LFM2.5 24B A2B | 11tok/sQ8_0 | 28tok/sQ8_0 |
| GLM 4.6V Flash | 11tok/sQ8_0 | 28tok/sQ8_0 |
| GLM 4.1V 9B Thinking | 10tok/smlx-8bit | 24tok/smlx-8bit |
| DeepSeek R1 Distill Llama 8B | 8tok/sQ8_0 | 21tok/sQ8_0 |
| Ministral 3 8B Reasoning 2512 | 8tok/sQ8_0 | 19tok/sQ8_0 |
| DeepSeek R1 Distill Qwen 14B | 5tok/sQ8_0 | 12tok/sQ8_0 |
| Devstral Small 2507 | 4tok/sQ8_0 | 13tok/sQ5_K_M |
| Magistral Small 2506 | 4tok/sQ8_0 | 12tok/sQ6_K |
| Magistral Small 2507 | 4tok/sQ8_0 | 13tok/sQ5_K_M |
| Magistral Small 2509 | 4tok/sQ8_0 | 13tok/sQ5_K_M |
| granite 4.2 30b | 3tok/sQ8_0 | offload |
| Nemotron 3 Super 120B-A12B | won't run | offload |
| GLM 4.5 Air | won't run | offload |
| DeepSeek V4 Flash | won't run | won't run |
| GLM 5.3 Flash | won't run | won't run |
| Qwen 3.8 Flash Next | won't run | won't run |
| MiniMax M2.7 | won't run | won't run |
| gpt-oss 120B | won't run | won't run |
| Llama 3.3 70B | won't run | won't run |
| DeepSeek R1 | won't run | won't run |
| DeepSeek R1 0528 | won't run | won't run |
| DeepSeek R1 Distill Llama 70B | won't run | won't run |
| DeepSeek R1 Zero | won't run | won't run |
| DeepSeek V3 0324 | won't run | won't run |
| DeepSeek V3.1 | won't run | won't run |
| DeepSeek V3.1 Terminus | won't run | won't run |
| DeepSeek V3.2 | won't run | won't run |
| DeepSeek V4 Flash 0731 | won't run | won't run |
| DeepSeek V4 Flash Vision Exp | won't run | won't run |
| Devstral 2 123B Instruct 2512 | won't run | won't run |
| GLM 4.5V | won't run | won't run |
| GLM 4.6 | won't run | won't run |
| GLM 4.6V | won't run | won't run |
| GLM 4.7 | won't run | won't run |
| GLM 5.1 | won't run | won't run |
| GLM 5.2 | won't run | won't run |
| GLM 5.3 | won't run | won't run |
| Kimi Dev 72B | won't run | won't run |
| Kimi K2 | won't run | won't run |
| Kimi K2 Instruct 0905 | won't run | won't run |
| Kimi K2 Thinking | won't run | won't run |
| Kimi K2.5 | won't run | won't run |
| MiniMax M2 | won't run | won't run |
| MiniMax M2.1 | won't run | won't run |
| MiniMax M2.5 | won't run | won't run |
| MiniMax M3 | won't run | won't run |
| Mistral Medium 3.5 128B | won't run | won't run |
Questions
- Which is better for local AI, the Framework Desktop Ryzen AI Max+ 395 64GB or the AMD Radeon AI PRO R9700?
- Choose the Framework Desktop Ryzen AI Max+ 395 64GB to run bigger models. Choose the AMD Radeon AI PRO R9700 for speed. Choose the AMD Radeon AI PRO R9700 to spend less.
- Which can run bigger models, the Framework Desktop Ryzen AI Max+ 395 64GB or the AMD Radeon AI PRO R9700?
- At 64k context the Framework Desktop Ryzen AI Max+ 395 64GB runs 59 of the models we track fully in memory and the AMD Radeon AI PRO R9700 51; only the Framework Desktop Ryzen AI Max+ 395 64GB holds Qwen 3.5 122B-A10B.
- Which is faster for local AI, the Framework Desktop Ryzen AI Max+ 395 64GB or the AMD Radeon AI PRO R9700?
- On the models both run fully in memory, the AMD Radeon AI PRO R9700 is about 2.5× as fast as the Framework Desktop Ryzen AI Max+ 395 64GB (estimated, median across models).
- What is the price difference between the Framework Desktop Ryzen AI Max+ 395 64GB and the AMD Radeon AI PRO R9700?
- In the US the Framework Desktop Ryzen AI Max+ 395 64GB costs $1,899 at eBay (used) and the AMD Radeon AI PRO R9700 $1,700 at Amazon: the AMD Radeon AI PRO R9700 is $199 cheaper.
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