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 32k context it runs 78 of the 125 open models we track fully in memory; the most capable is DeepSeek V4 Flash at UD-Q2_K_XL, at about 30 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 @32k | Prefill | Good for |
|---|---|---|---|---|---|---|---|
| DeepSeek V4 Flash DeepSeek | UD-Q2_K_XL | fits | 94.4 GB | 32k57k with q8 cache | 30tok/sest. | 322 tok/s | chat, coding |
| Qwen 3.8 Flash Next Alibaba Qwen | UD-Q3_K_XL | fits | 86.1 GB | 256k | 9tok/sest. | 335 tok/s | – |
| MiniMax M2.7 MiniMax | UD-Q2_K_XL | fits | 79.4 GB | 92k170k with q8 cache | 24tok/sest. | 913 tok/s | chat, coding |
| Qwen 3.8 27B Alibaba Qwen | Q8_0 | fits | 30.7 GB | 256k | 15tok/sest. | 692 tok/s | chat |
| Qwen 3.5 122B-A10B Alibaba Qwen | Q5_K_M | fits | 87.6 GB | 256k | 35tok/sest. | 1623 tok/s | chat, coding, agents, long documents |
| Qwen 3.6 27B Alibaba Qwen | Q8_0 | fits | 30.3 GB | 256k | 15tok/sest. | 692 tok/s | chat |
| gpt-oss 120B OpenAI | MXFP4 | fits | 61.7 GB | 128k | 66tok/sest. | 2621 tok/s | chat, coding, agents, long documents |
| Qwen3 Coder Next Alibaba Qwen | Q8_0 | fits | 81.3 GB | 256k | 57tok/sest. | 3911 tok/s | chat, coding, agents, long documents |
| Nemotron 3 Super 120B-A12B NVIDIA | Q5_K_M | fits | 91.9 GB | 217k256k with q8 cache | 29tok/sest. | 1641 tok/s | chat, coding, long documents |
| Gemma 4 31B | Q8_0 | fits | 35.2 GB | 256k | 13tok/sest. | 617 tok/s | – |
| Mistral Small 4 119B Mistral AI | Q6_K | fits | 93.2 GB | 76k122k with q8 cache | 44tok/sest. | 1804 tok/s | chat, coding, agents, long documents |
| Qwen 3.6 35B-A3B Alibaba Qwen | Q8_0 | fits | 36.5 GB | 256k | 75tok/sest. | 4941 tok/s | chat, coding, agents, long documents |
| Qwen 3.5 27B Alibaba Qwen | Q8_0 | fits | 30.3 GB | 256k | 15tok/sest. | 692 tok/s | chat |
| Gemma 4 26B-A4B | Q8_0 | fits | 27.3 GB | 256k | 57tok/sest. | 4396 tok/s | chat, coding, agents, long documents |
| GLM 4.5 Air Z.ai | Q5_K_M | fits | 85.0 GB | 82k128k with q8 cache | 17tok/sest. | 670 tok/s | chat |
| Qwen 3.5 35B-A3B Alibaba Qwen | Q8_0 | fits | 36.5 GB | 256k | 75tok/sest. | 4941 tok/s | chat, coding, agents, long documents |
| Qwen3 Next 80B-A3B Alibaba Qwen | Q8_0 | fits | 81.3 GB | 256k | 71tok/sest. | 4653 tok/s | chat, coding, agents, long documents |
| GLM 4.7 Flash Z.ai | Q8_0 | fits | 32.8 GB | 198k | 40tok/sest. | 2580 tok/s | chat, coding, agents, long documents |
| Devstral Small 2 24B Mistral AI | Q8_0 | fits | 29.8 GB | 384k | 16tok/sest. | 730 tok/s | chat |
| Nemotron 3.5 Lightning 30B-A3B NVIDIA | UD-Q6_K_XL | fits | 34.3 GB | 256k | 80tok/sest. | 5638 tok/s | chat, coding, agents, long documents |
| gpt-oss 20B OpenAI | MXFP4 | fits | 13.5 GB | 128k | 88tok/sest. | 3689 tok/s | chat, coding, agents, long documents |
| Kimi Linear 48B-A3B Moonshot AI | Q8_0 | fits | 50.4 GB | 1024k | 82tok/sest. | 4258 tok/s | chat, coding, long documents |
| Qwen3 Coder 30B-A3B Alibaba Qwen | Q8_0 | fits | 34.7 GB | 256k | 45tok/sest. | 2272 tok/s | chat, coding, agents, long documents |
| Qwen3 32B Alibaba Qwen | Q8_0 | fits | 41.9 GB | 40k | 11tok/sest. | 430 tok/s | – |
| Gemma 4 12B | Q8_0 | fits | 14.1 GB | 256k | 35tok/sest. | 1605 tok/s | chat, coding, long documents |
| Qwen 3.5 9B Alibaba Qwen | Q8_0 | fits | 11.4 GB | 256k | 45tok/sest. | 2000 tok/s | chat, coding, long documents |
| Mistral Small 3.2 24B Mistral AI | Q8_0 | fits | 29.8 GB | 128k | 16tok/sest. | 730 tok/s | chat |
| Gemma 3 27B | Q8_0 | fits | 31.1 GB | 128k | 15tok/sest. | 735 tok/s | chat |
| DeepSeek R1 Distill Qwen 32B DeepSeek | Q8_0 | fits | 41.9 GB | 128k | 11tok/sest. | 493 tok/s | – |
| Llama 3.3 70B Meta | Q8_0 | fits | 81.3 GB | 73k128k with q8 cache | 6tok/sest. | 233 tok/s | – |
| Ministral 3 14B Mistral AI | Q8_0 | fits | 19.9 GB | 256k | 24tok/sest. | 1111 tok/s | chat, coding |
| Qwen3 14B Alibaba Qwen | Q8_0 | fits | 21.1 GB | 40k | 23tok/sest. | 999 tok/s | chat, coding |
| DeepSeek R1 0528 Qwen3 8B DeepSeek | Q8_0 | fits | 14.1 GB | 128k | 35tok/sest. | 1648 tok/s | chat, coding, long documents |
| Qwen3 8B Alibaba Qwen | Q8_0 | fits | 14.1 GB | 40k | 35tok/sest. | 1648 tok/s | chat, coding, long documents |
| Phi-4 14B Microsoft | Q8_0 | fits | 22.2 GB | 16k | 21tok/sest. | 1004 tok/s | chat, coding |
| Qwen 3.5 4B Alibaba Qwen | Q8_0 | fits | 6.7 GB | 256k | 85tok/sest. | 3742 tok/s | chat, coding, long documents |
| Ministral 3 8B Mistral AI | Q8_0 | fits | 14.1 GB | 256k | 35tok/sest. | 1591 tok/s | chat, coding, long documents |
| Granite 4.2 8B IBM | Q8_0 | fits | 15.2 GB | 128k | 32tok/sest. | 1515 tok/s | chat, coding, agents, long documents |
| Gemma 4 E4B | Q8_0 | fits | 10.0 GB | 128k | 52tok/sest. | 2513 tok/s | chat, coding, long documents |
| LFM2.5 8B-A1B Liquid AI | Q8_0 | fits | 11.4 GB | 125k | 107tok/sest. | 8218 tok/s | chat, coding, long documents |
| Llama 3.1 8B Meta | Q6_K | fits | 11.6 GB | 128k | 44tok/sest. | 1741 tok/s | chat, coding, long documents |
| Qwen2.5 0.5B Alibaba Qwen | Q8_0 | fits | 2.4 GB | 32k | 509tok/sest. | 17899 tok/s | chat, coding, long documents |
| Qwen3.5 0.8B Alibaba Qwen | Q8_0 | fits | 2.6 GB | 256k | 385tok/sest. | 16813 tok/s | chat, coding, agents, long documents |
| Qwen3.5 2B Alibaba Qwen | Q5_K_M | fits | 3.2 GB | 256k | 256tok/sest. | 8015 tok/s | chat, coding, agents, long documents |
| DeepSeek R1 Distill Qwen 1.5B DeepSeek | Q8_0 | fits | 4.1 GB | 128k | 168tok/sest. | 6733 tok/s | chat, coding, long documents |
| MiniCPM5 2B openbmb | Q8_0 | fits | 5.3 GB | 128k | 116tok/sest. | 4021 tok/s | chat, coding, agents, long documents |
| Spark X2.5 4B XHToken | mlx-4bit | fits | 5.5 GB | 1024k | 110tok/sest. | 3949 tok/s | chat, coding, agents, long documents |
| LFM2.5 2.6B Liquid AI | Q8_0 | fits | 6.0 GB | 128k | 97tok/sest. | 4555 tok/s | chat, coding, agents, long documents |
| LFM2.5 VL 3B Liquid AI | Q8_0 | fits | 6.0 GB | 32k | 97tok/sest. | 4177 tok/s | chat, coding, agents, long documents |
| Mellum2.1 12B A2.5B Thinking JetBrains | Q8_0 | fits | 14.0 GB | 128k | 90tok/sest. | 5934 tok/s | chat, coding, agents, long documents |
| FrogNano 4B 2609 Microsoft | Q8_0 | fits | 6.8 GB | 256k | 83tok/sest. | 3742 tok/s | chat, coding, agents, long documents |
| granite vision 4.1 4b IBM | Q8_0 | fits | 7.4 GB | 128k | 75tok/sest. | 2918 tok/s | chat, coding, long documents |
| granite 4.2 3b IBM | Q8_0 | fits | 7.6 GB | 128k | 72tok/sest. | 3058 tok/s | chat, coding, agents, long documents |
| Kimi VL A3B Thinking 2506 Moonshot AI | Q8_0 | fits | 18.2 GB | 128k | 69tok/sest. | 4459 tok/s | chat, coding, long documents |
| Kimi VL A3B Moonshot AI | mlx-8bit | fits | 19.0 GB | 128k | 67tok/sest. | 4459 tok/s | chat, coding, agents, long documents |
| Ministral 3 3B Instruct 2512 Mistral AI | Q8_0 | fits | 8.1 GB | 256k | 67tok/sest. | 2924 tok/s | chat, coding, agents, long documents |
| LFM2.5 24B A2B Liquid AI | Q8_0 | fits | 27.7 GB | 128k | 59tok/sest. | 4580 tok/s | chat, coding, agents, long documents |
| GLM 4 9B 0414 Z.ai | mlx-5bit | fits | 10.4 GB | 32k | 50tok/sest. | 1453 tok/s | chat, coding, agents, long documents |
| DeepSeek R1 Distill Qwen 7B DeepSeek | Q8_0 | fits | 10.8 GB | 128k | 48tok/sest. | 1968 tok/s | chat, coding, long documents |
| GLM 4.6V Flash Z.ai | Q8_0 | fits | 12.1 GB | 128k | 42tok/sest. | 1370 tok/s | chat, coding, agents, long documents |
| DeepSeek R1 Distill Llama 8B DeepSeek | Q8_0 | fits | 13.4 GB | 128k | 37tok/sest. | 1741 tok/s | chat, coding, long documents |
| GLM 4.1V 9B Thinking Z.ai | mlx-8bit | fits | 13.7 GB | 64k | 36tok/sest. | 1370 tok/s | chat, coding, long documents |
| Ministral 3 8B Reasoning 2512 Mistral AI | Q8_0 | fits | 14.1 GB | 256k | 35tok/sest. | 1591 tok/s | chat, coding, agents, long documents |
| Ministral 3 14B Reasoning 2512 Mistral AI | Q8_0 | fits | 19.9 GB | 256k | 24tok/sest. | 1111 tok/s | chat, coding |
| DeepSeek R1 Distill Qwen 14B DeepSeek | Q8_0 | fits | 22.1 GB | 128k | 22tok/sest. | 940 tok/s | chat, coding |
| Qwen3.8 27B OBLITERATED OBLITERATUS | mlx-4bit | fits | 22.9 GB | 256k | 21tok/sest. | 692 tok/s | chat, coding, long documents |
| GLM 4.5V Z.ai | Q4_K_M | fits | 66.5 GB | 64k | 20tok/sest. | 654 tok/s | chat |
| GLM 4.6V Z.ai | Q4_K_M | fits | 72.8 GB | 128k | 18tok/sest. | 654 tok/s | chat |
| Devstral Small 2507 Mistral AI | Q8_0 | fits | 29.8 GB | 128k | 16tok/sest. | 741 tok/s | chat |
| Magistral Small 2506 Mistral AI | Q8_0 | fits | 29.8 GB | 40k | 16tok/sest. | 741 tok/s | chat |
| Magistral Small 2507 Mistral AI | Q8_0 | fits | 29.8 GB | 40k | 16tok/sest. | 741 tok/s | chat |
| Magistral Small 2509 Mistral AI | Q8_0 | fits | 29.8 GB | 128k | 16tok/sest. | 730 tok/s | chat |
| Muse Glimmer 30B meta-models | Q8_0 | fits | 32.1 GB | 128k | 15tok/sest. | 667 tok/s | – |
| granite 4.2 30b IBM | Q8_0 | fits | 38.5 GB | 128k | 12tok/sest. | 567 tok/s | – |
| DeepSeek R1 Distill Llama 70B DeepSeek | Q8_0 | fits | 81.3 GB | 73k128k with q8 cache | 6tok/sest. | 233 tok/s | – |
| Devstral 2 123B Instruct 2512 Mistral AI | Q4_K_M | fits | 82.2 GB | 66k123k with q8 cache | 6tok/sest. | 134 tok/s | – |
| Mistral Medium 3.5 128B Mistral AI | Q4_K_M | fits | 82.2 GB | 66k123k with q8 cache | 6tok/sest. | 132 tok/s | – |
| Kimi Dev 72B Moonshot AI | Q8_0 | fits | 83.4 GB | 66k123k with q8 cache | 5tok/sest. | 228 tok/s | – |
| GLM 5.3 Flash Z.ai | UD-Q2_K_XL | offload | 103.2 GB | – | 19tok/sest. | 411 tok/s | chat |
| DeepSeek R1 DeepSeek | Q4_K_M | won't run | 380.3 GB | – | – | – | – |
| DeepSeek R1 0528 DeepSeek | Q4_K_M | won't run | 380.8 GB | – | – | – | – |
| DeepSeek R1 Zero DeepSeek | Q4_K_M | won't run | 380.3 GB | – | – | – | – |
| DeepSeek V3 DeepSeek | Q4_K_M | won't run | 380.3 GB | – | – | – | – |
| DeepSeek V3 0324 DeepSeek | Q4_K_M | won't run | 380.3 GB | – | – | – | – |
| DeepSeek V3.1 DeepSeek | Q4_K_M | won't run | 381.2 GB | – | – | – | – |
| DeepSeek V3.1 Terminus DeepSeek | Q4_K_M | won't run | 381.2 GB | – | – | – | – |
| DeepSeek V3.2 DeepSeek | Q4_K_M | won't run | 381.2 GB | – | – | – | – |
| DeepSeek V4 Flash 0731 DeepSeek | MXFP4 | won't run | 149.8 GB | – | – | – | – |
| DeepSeek V4 Flash Vision Exp DeepSeek | MXFP4 | won't run | 158.6 GB | – | – | – | – |
| GLM 4.5 Z.ai | Q4_K_M | won't run | 214.6 GB | – | – | – | – |
| GLM 4.6 Z.ai | Q4_K_M | won't run | 213.8 GB | – | – | – | – |
| GLM 4.7 Z.ai | Q4_K_M | won't run | 214.6 GB | – | – | – | – |
| GLM 5 Z.ai | MXFP4 | won't run | 386.6 GB | – | – | – | – |
| GLM 5.1 Z.ai | MXFP4 | won't run | 424.2 GB | – | – | – | – |
| GLM 5.2 Z.ai | UD-Q4_K_M | won't run | 438.1 GB | – | – | – | – |
| GLM 5.3 Z.ai | Q8_0 | won't run | 750.6 GB | – | – | – | – |
| Kimi K2 Moonshot AI | Q4_K_M | won't run | 581.8 GB | – | – | – | – |
| Kimi K2 Instruct 0905 Moonshot AI | Q4_K_M | won't run | 582.2 GB | – | – | – | – |
| Kimi K2 Thinking Moonshot AI | Q4_K_M | won't run | 582.2 GB | – | – | – | – |
| Kimi K2.5 Moonshot AI | Q4_K_M | won't run | 582.2 GB | – | – | – | – |
| MiniMax M2 MiniMax | Q4_K_M | won't run | 138.1 GB | – | – | – | – |
| MiniMax M2.1 MiniMax | Q4_K_M | won't run | 138.1 GB | – | – | – | – |
| MiniMax M2.5 MiniMax | MXFP4 | won't run | 124.5 GB | – | – | – | – |
| MiniMax M3 MiniMax | MXFP4 | won't run | 244.9 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?
- DeepSeek V4 Flash (UD-Q2_K_XL) fits fully in its memory at 32k context and generates at about 30 tok/s, an estimate from its memory bandwidth. 78 of the 125 models we track fit at that context.
- Can the AMD Radeon PRO W7900 run GLM 5.3 Flash?
- Not fully in memory: at UD-Q2_K_XL and 32k context GLM 5.3 Flash needs about 103 GB, so part of it runs from system RAM and it slows to about 19 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 32k context. See the best hardware for local AI at every budget.