AMD Radeon PRO W7900
48 GB · 864 GB/s · 295 W
In short
The AMD Radeon PRO W7900 has 48 GB of GDDR6 memory with 864 GB/s of bandwidth, which sets how fast it generates text. At 64k context it runs 77 of the 125 open models we track fully in memory; the most capable is Qwen 3.8 Flash Next at UD-Q3_K_XL, at about 9 tok/s (estimated). We have no current price for the AMD Radeon PRO W7900 in Germany.
Updated . Speeds are estimates; how they are worked out.
Prices
Every retailer we track in Germany, cheapest first. Buy links may earn us a commission.
No retailer in this market lists it right now.
What two of them run
Every model in the catalogue, with the best quant for this hardware. Estimates, not measurements.
| Model | Quant | Fits | Memory | Max context | Decode @64k | Prefill | Good for |
|---|---|---|---|---|---|---|---|
| Qwen 3.8 Flash Next Alibaba Qwen | UD-Q3_K_XL | fits | 87.1 GB | 256k | 9tok/sest. | 323 tok/s | – |
| Qwen 3.8 27B Alibaba Qwen | Q8_0 | fits | 32.9 GB | 256k | 14tok/sest. | 627 tok/s | chat |
| MiniMax M2.7 MiniMax | UD-Q2_K_XL | fits | 87.3 GB | 92k170k with q8 cache | 14tok/sest. | 596 tok/s | chat, coding |
| Qwen 3.5 122B-A10B Alibaba Qwen | Q5_K_M | fits | 88.5 GB | 256k | 31tok/sest. | 1305 tok/s | chat, coding, agents, long documents |
| Qwen 3.6 27B Alibaba Qwen | Q8_0 | fits | 32.4 GB | 256k | 14tok/sest. | 627 tok/s | chat |
| gpt-oss 120B OpenAI | MXFP4 | fits | 63.0 GB | 128k | 52tok/sest. | 2024 tok/s | chat, coding, agents, long documents |
| Qwen3 Coder Next Alibaba Qwen | Q8_0 | fits | 82.2 GB | 256k | 49tok/sest. | 3024 tok/s | chat, coding, agents, long documents |
| Nemotron 3 Super 120B-A12B NVIDIA | Q5_K_M | fits | 92.3 GB | 217k256k with q8 cache | 28tok/sest. | 1517 tok/s | chat, coding, long documents |
| Gemma 4 31B | Q8_0 | fits | 37.9 GB | 256k | 12tok/sest. | 572 tok/s | – |
| Mistral Small 4 119B Mistral AI | Q6_K | fits | 94.0 GB | 76k122k with q8 cache | 39tok/sest. | 1283 tok/s | chat, coding, agents, long documents |
| Qwen 3.6 35B-A3B Alibaba Qwen | Q8_0 | fits | 37.4 GB | 256k | 64tok/sest. | 3774 tok/s | chat, coding, agents, long documents |
| Qwen 3.5 27B Alibaba Qwen | Q8_0 | fits | 32.4 GB | 256k | 14tok/sest. | 627 tok/s | chat |
| Gemma 4 26B-A4B | Q8_0 | fits | 28.1 GB | 256k | 50tok/sest. | 3864 tok/s | chat, coding, agents, long documents |
| GLM 4.5 Air Z.ai | Q5_K_M | fits | 91.0 GB | 82k128k with q8 cache | 13tok/sest. | 425 tok/s | chat |
| Qwen 3.5 35B-A3B Alibaba Qwen | Q8_0 | fits | 37.4 GB | 256k | 64tok/sest. | 3774 tok/s | chat, coding, agents, long documents |
| Qwen3 Next 80B-A3B Alibaba Qwen | Q8_0 | fits | 82.2 GB | 256k | 59tok/sest. | 3449 tok/s | chat, coding, agents, long documents |
| GLM 4.7 Flash Z.ai | Q8_0 | fits | 34.6 GB | 198k | 32tok/sest. | 1872 tok/s | chat, coding, agents, long documents |
| Devstral Small 2 24B Mistral AI | Q8_0 | fits | 35.0 GB | 384k | 13tok/sest. | 617 tok/s | chat |
| Nemotron 3.5 Lightning 30B-A3B NVIDIA | UD-Q6_K_XL | fits | 34.7 GB | 256k | 76tok/sest. | 4653 tok/s | chat, coding, agents, long documents |
| Kimi Linear 48B-A3B Moonshot AI | Q8_0 | fits | 50.9 GB | 1024k | 76tok/sest. | 3032 tok/s | chat, coding, long documents |
| gpt-oss 20B OpenAI | MXFP4 | fits | 14.5 GB | 128k | 71tok/sest. | 2889 tok/s | chat, coding, agents, long documents |
| Qwen3 Coder 30B-A3B Alibaba Qwen | Q8_0 | fits | 37.9 GB | 256k | 30tok/sest. | 1351 tok/s | chat, coding, agents, long documents |
| Qwen3 32B Alibaba Qwen | Q8_0 | fits | 50.1 GB | 40k | 9tok/sest. | 320 tok/s | – |
| Gemma 4 12B | Q8_0 | fits | 14.8 GB | 256k | 34tok/sest. | 1486 tok/s | chat, coding, long documents |
| Qwen 3.5 9B Alibaba Qwen | Q8_0 | fits | 12.6 GB | 256k | 41tok/sest. | 1818 tok/s | chat, coding, long documents |
| Gemma 3 27B | Q8_0 | fits | 33.8 GB | 128k | 14tok/sest. | 702 tok/s | chat |
| Mistral Small 3.2 24B Mistral AI | Q8_0 | fits | 35.0 GB | 128k | 13tok/sest. | 617 tok/s | chat |
| DeepSeek R1 Distill Qwen 32B DeepSeek | Q8_0 | fits | 50.1 GB | 128k | 9tok/sest. | 396 tok/s | – |
| Llama 3.3 70B Meta | Q8_0 | fits | 91.5 GB | 73k128k with q8 cache | 5tok/sest. | 189 tok/s | – |
| Ministral 3 14B Mistral AI | Q8_0 | fits | 25.0 GB | 256k | 19tok/sest. | 869 tok/s | chat, coding |
| Qwen3 14B Alibaba Qwen | Q8_0 | fits | 26.3 GB | 40k | 18tok/sest. | 761 tok/s | chat, coding |
| DeepSeek R1 0528 Qwen3 8B DeepSeek | Q8_0 | fits | 18.8 GB | 128k | 26tok/sest. | 1202 tok/s | chat, coding, long documents |
| Qwen3 8B Alibaba Qwen | Q8_0 | fits | 18.8 GB | 40k | 26tok/sest. | 1202 tok/s | chat, coding, long documents |
| Phi-4 14B Microsoft | Q8_0 | fits | 28.7 GB | 16k | 16tok/sest. | 764 tok/s | chat, coding |
| Qwen 3.5 4B Alibaba Qwen | Q8_0 | fits | 7.9 GB | 256k | 71tok/sest. | 3152 tok/s | chat, coding, long documents |
| Ministral 3 8B Mistral AI | Q8_0 | fits | 18.6 GB | 256k | 26tok/sest. | 1189 tok/s | chat, coding, long documents |
| Granite 4.2 8B IBM | Q8_0 | fits | 20.4 GB | 128k | 24tok/sest. | 1099 tok/s | chat, coding, agents, long documents |
| Gemma 4 E4B | Q8_0 | fits | 11.1 GB | 128k | 47tok/sest. | 2382 tok/s | chat, coding, long documents |
| LFM2.5 8B-A1B Liquid AI | Q8_0 | fits | 13.1 GB | 125k | 67tok/sest. | 5083 tok/s | chat, coding, long documents |
| Llama 3.1 8B Meta | Q6_K | fits | 15.8 GB | 128k | 31tok/sest. | 1291 tok/s | chat, coding, long documents |
| Qwen2.5 0.5B Alibaba Qwen | Q8_0 | fits | 2.9 GB | 32k | 356tok/sest. | 11272 tok/s | chat, coding, long documents |
| Qwen3.5 0.8B Alibaba Qwen | Q8_0 | fits | 3.2 GB | 256k | 291tok/sest. | 12780 tok/s | chat, coding, agents, long documents |
| Qwen3.5 2B Alibaba Qwen | Q5_K_M | fits | 3.8 GB | 256k | 210tok/sest. | 6967 tok/s | chat, coding, agents, long documents |
| DeepSeek R1 Distill Qwen 1.5B DeepSeek | Q8_0 | fits | 5.2 GB | 128k | 126tok/sest. | 4669 tok/s | chat, coding, long documents |
| MiniCPM5 2B openbmb | Q8_0 | fits | 6.8 GB | 128k | 86tok/sest. | 2631 tok/s | chat, coding, agents, long documents |
| Spark X2.5 4B XHToken | mlx-4bit | fits | 6.8 GB | 1024k | 86tok/sest. | 3231 tok/s | chat, coding, agents, long documents |
| Mellum2.1 12B A2.5B Thinking JetBrains | Q8_0 | fits | 14.6 GB | 128k | 78tok/sest. | 4710 tok/s | chat, coding, agents, long documents |
| FrogNano 4B 2609 Microsoft | Q8_0 | fits | 8.0 GB | 256k | 70tok/sest. | 3152 tok/s | chat, coding, agents, long documents |
| LFM2.5 2.6B Liquid AI | Q8_0 | fits | 8.1 GB | 128k | 69tok/sest. | 3191 tok/s | chat, coding, agents, long documents |
| LFM2.5 VL 3B Liquid AI | Q8_0 | fits | 8.1 GB | 32k | 69tok/sest. | 3001 tok/s | chat, coding, agents, long documents |
| Kimi VL A3B Thinking 2506 Moonshot AI | Q8_0 | fits | 19.4 GB | 128k | 55tok/sest. | 3239 tok/s | chat, coding, long documents |
| Kimi VL A3B Moonshot AI | mlx-8bit | fits | 20.2 GB | 128k | 54tok/sest. | 3239 tok/s | chat, coding, agents, long documents |
| granite vision 4.1 4b IBM | Q8_0 | fits | 10.0 GB | 128k | 53tok/sest. | 2004 tok/s | chat, coding, long documents |
| granite 4.2 3b IBM | Q8_0 | fits | 10.3 GB | 128k | 51tok/sest. | 2069 tok/s | chat, coding, agents, long documents |
| Ministral 3 3B Instruct 2512 Mistral AI | Q8_0 | fits | 11.6 GB | 256k | 45tok/sest. | 1981 tok/s | chat, coding, agents, long documents |
| GLM 4 9B 0414 Z.ai | mlx-5bit | fits | 11.9 GB | 32k | 44tok/sest. | 1065 tok/s | chat, coding, agents, long documents |
| DeepSeek R1 Distill Qwen 7B DeepSeek | Q8_0 | fits | 12.7 GB | 128k | 40tok/sest. | 1512 tok/s | chat, coding, long documents |
| LFM2.5 24B A2B Liquid AI | Q8_0 | fits | 30.4 GB | 128k | 38tok/sest. | 2912 tok/s | chat, coding, agents, long documents |
| GLM 4.6V Flash Z.ai | Q8_0 | fits | 13.5 GB | 128k | 38tok/sest. | 1020 tok/s | chat, coding, agents, long documents |
| GLM 4.1V 9B Thinking Z.ai | mlx-8bit | fits | 15.1 GB | 64k | 33tok/sest. | 1020 tok/s | chat, coding, long documents |
| DeepSeek R1 Distill Llama 8B DeepSeek | Q8_0 | fits | 17.6 GB | 128k | 28tok/sest. | 1291 tok/s | chat, coding, long documents |
| Ministral 3 8B Reasoning 2512 Mistral AI | Q8_0 | fits | 18.6 GB | 256k | 26tok/sest. | 1189 tok/s | chat, coding, agents, long documents |
| Ministral 3 14B Reasoning 2512 Mistral AI | Q8_0 | fits | 25.0 GB | 256k | 19tok/sest. | 869 tok/s | chat, coding |
| Qwen3.8 27B OBLITERATED OBLITERATUS | mlx-4bit | fits | 25.1 GB | 256k | 19tok/sest. | 627 tok/s | chat, coding, long documents |
| DeepSeek R1 Distill Qwen 14B DeepSeek | Q8_0 | fits | 28.3 GB | 128k | 17tok/sest. | 695 tok/s | chat, coding |
| Muse Glimmer 30B meta-models | Q8_0 | fits | 32.7 GB | 128k | 14tok/sest. | 633 tok/s | – |
| GLM 4.5V Z.ai | Q4_K_M | fits | 72.4 GB | 64k | 14tok/sest. | 418 tok/s | chat |
| Devstral Small 2507 Mistral AI | Q8_0 | fits | 35.0 GB | 128k | 13tok/sest. | 625 tok/s | chat |
| Magistral Small 2506 Mistral AI | Q8_0 | fits | 35.0 GB | 40k | 13tok/sest. | 625 tok/s | chat |
| Magistral Small 2507 Mistral AI | Q8_0 | fits | 35.0 GB | 40k | 13tok/sest. | 625 tok/s | chat |
| Magistral Small 2509 Mistral AI | Q8_0 | fits | 35.0 GB | 128k | 13tok/sest. | 617 tok/s | chat |
| GLM 4.6V Z.ai | Q4_K_M | fits | 78.8 GB | 128k | 13tok/sest. | 418 tok/s | chat |
| granite 4.2 30b IBM | Q8_0 | fits | 46.7 GB | 128k | 10tok/sest. | 462 tok/s | – |
| DeepSeek R1 Distill Llama 70B DeepSeek | Q8_0 | fits | 91.5 GB | 73k128k with q8 cache | 5tok/sest. | 189 tok/s | – |
| Devstral 2 123B Instruct 2512 Mistral AI | Q4_K_M | fits | 93.4 GB | 66k123k with q8 cache | 5tok/sest. | 110 tok/s | – |
| Kimi Dev 72B Moonshot AI | Q8_0 | fits | 93.6 GB | 66k123k with q8 cache | 5tok/sest. | 186 tok/s | – |
| Mistral Medium 3.5 128B Mistral AI | Q4_K_M | fits | 93.4 GB | 66k123k with q8 cache | 5tok/sest. | 108 tok/s | – |
| DeepSeek V4 Flash DeepSeek | UD-Q2_K_XL | offload | 97.2 GB | 32k57k with q8 cache | 18tok/sest. | 187 tok/s | chat, coding |
| GLM 5.3 Flash Z.ai | UD-Q2_K_XL | offload | 103.7 GB | – | 18tok/sest. | 275 tok/s | chat |
| DeepSeek R1 DeepSeek | Q4_K_M | won't run | 382.6 GB | – | – | – | – |
| DeepSeek R1 0528 DeepSeek | Q4_K_M | won't run | 383.1 GB | – | – | – | – |
| DeepSeek R1 Zero DeepSeek | Q4_K_M | won't run | 382.6 GB | – | – | – | – |
| DeepSeek V3 DeepSeek | Q4_K_M | won't run | 382.6 GB | – | – | – | – |
| DeepSeek V3 0324 DeepSeek | Q4_K_M | won't run | 382.6 GB | – | – | – | – |
| DeepSeek V3.1 DeepSeek | Q4_K_M | won't run | 383.5 GB | – | – | – | – |
| DeepSeek V3.1 Terminus DeepSeek | Q4_K_M | won't run | 383.5 GB | – | – | – | – |
| DeepSeek V3.2 DeepSeek | Q4_K_M | won't run | 383.5 GB | – | – | – | – |
| DeepSeek V4 Flash 0731 DeepSeek | MXFP4 | won't run | 152.7 GB | – | – | – | – |
| DeepSeek V4 Flash Vision Exp DeepSeek | MXFP4 | won't run | 161.5 GB | – | – | – | – |
| GLM 4.5 Z.ai | Q4_K_M | won't run | 226.3 GB | – | – | – | – |
| GLM 4.6 Z.ai | Q4_K_M | won't run | 225.5 GB | – | – | – | – |
| GLM 4.7 Z.ai | Q4_K_M | won't run | 226.3 GB | – | – | – | – |
| GLM 5 Z.ai | MXFP4 | won't run | 389.5 GB | – | – | – | – |
| GLM 5.1 Z.ai | MXFP4 | won't run | 427.1 GB | – | – | – | – |
| GLM 5.2 Z.ai | UD-Q4_K_M | won't run | 441.0 GB | – | – | – | – |
| GLM 5.3 Z.ai | Q8_0 | won't run | 753.5 GB | – | – | – | – |
| Kimi K2 Moonshot AI | Q4_K_M | won't run | 584.1 GB | – | – | – | – |
| Kimi K2 Instruct 0905 Moonshot AI | Q4_K_M | won't run | 584.5 GB | – | – | – | – |
| Kimi K2 Thinking Moonshot AI | Q4_K_M | won't run | 584.5 GB | – | – | – | – |
| Kimi K2.5 Moonshot AI | Q4_K_M | won't run | 584.5 GB | – | – | – | – |
| MiniMax M2 MiniMax | Q4_K_M | won't run | 146.0 GB | – | – | – | – |
| MiniMax M2.1 MiniMax | Q4_K_M | won't run | 146.0 GB | – | – | – | – |
| MiniMax M2.5 MiniMax | MXFP4 | won't run | 132.4 GB | – | – | – | – |
| MiniMax M3 MiniMax | MXFP4 | won't run | 248.9 GB | – | – | – | – |
Specifications
From the manufacturer's published specifications; sources are linked where we have them.
- GPU
- Radeon PRO W7900
- Memory
- 48 GB GDDR6
- Memory bus
- 384-bit
- Memory bandwidth
- 864 GB/s
- FP16 compute
- 122.6 TFLOPS
- Software
- ROCm
- Board power
- 295 W
- PCIe generation
- Gen 4
- PCIe lanes
- x16
Compare with
Questions
- How much memory does the AMD Radeon PRO W7900 have for AI models?
- The AMD Radeon PRO W7900 has 48 GB of GDDR6 memory with 864 GB/s of bandwidth, which sets how fast it generates text.
- What is the best model the AMD Radeon PRO W7900 can run locally?
- Qwen 3.8 Flash Next (UD-Q3_K_XL) fits fully in its memory at 64k context and generates at about 9 tok/s, an estimate from its memory bandwidth. 77 of the 125 models we track fit at that context.
- Can the AMD Radeon PRO W7900 run DeepSeek V4 Flash?
- Not fully in memory: at UD-Q2_K_XL and 64k context DeepSeek V4 Flash needs about 97 GB, so part of it runs from system RAM and it slows to about 18 tok/s (estimated).
- How much does the AMD Radeon PRO W7900 cost in Germany?
- We have no current price for the AMD Radeon PRO W7900 in Germany.
Speeds on this page are for 64k context. See the best hardware for local AI at every budget.