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 65 of the 125 open models we track fully in memory; the most capable is Qwen 3.8 27B at Q8_0, at about 15 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 it runs
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 |
|---|---|---|---|---|---|---|---|
| Qwen 3.8 27B Alibaba Qwen | Q8_0 | fits | 30.2 GB | 256k | 15tok/sest. | 692 tok/s | chat |
| Qwen 3.5 122B-A10B Alibaba Qwen | UD-Q2_K_XL | fits | 40.8 GB | 248k256k with q8 cache | 66tok/sest. | 1623 tok/s | chat, coding, agents, long documents |
| Qwen 3.6 27B Alibaba Qwen | Q8_0 | fits | 29.8 GB | 256k | 15tok/sest. | 692 tok/s | chat |
| Qwen3 Coder Next Alibaba Qwen | Q4_K_M | fits | 47.0 GB | 38k61k with q8 cache | 88tok/sest. | 3911 tok/s | chat, coding, agents, long documents |
| Gemma 4 31B | Q8_0 | fits | 34.7 GB | 181k256k with q8 cache | 13tok/sest. | 617 tok/s | – |
| Mistral Small 4 119B Mistral AI | UD-Q2_K_XL | fits | 39.1 GB | 321k511k with q8 cache | 91tok/sest. | 1804 tok/s | chat, coding, agents, long documents |
| Qwen 3.6 35B-A3B Alibaba Qwen | Q8_0 | fits | 36.0 GB | 256k | 75tok/sest. | 4941 tok/s | chat, coding, agents, long documents |
| Qwen 3.5 27B Alibaba Qwen | Q8_0 | fits | 29.8 GB | 256k | 15tok/sest. | 692 tok/s | chat |
| Gemma 4 26B-A4B | Q8_0 | fits | 26.8 GB | 256k | 57tok/sest. | 4396 tok/s | chat, coding, agents, long documents |
| Qwen3 Next 80B-A3B Alibaba Qwen | Q4_K_M | fits | 47.0 GB | 39k63k with q8 cache | 107tok/sest. | 4653 tok/s | chat, coding, agents, long documents |
| Qwen 3.5 35B-A3B Alibaba Qwen | Q8_0 | fits | 36.0 GB | 256k | 75tok/sest. | 4941 tok/s | chat, coding, agents, long documents |
| GLM 4.7 Flash Z.ai | Q8_0 | fits | 32.3 GB | 198k | 40tok/sest. | 2580 tok/s | chat, coding, agents, long documents |
| Devstral Small 2 24B Mistral AI | Q8_0 | fits | 29.3 GB | 142k259k with q8 cache | 16tok/sest. | 730 tok/s | chat |
| Nemotron 3.5 Lightning 30B-A3B NVIDIA | UD-Q6_K_XL | fits | 33.8 GB | 256k | 80tok/sest. | 5638 tok/s | chat, coding, agents, long documents |
| Kimi Linear 48B-A3B Moonshot AI | Q6_K | fits | 39.0 GB | 640k872k with q8 cache | 103tok/sest. | 4258 tok/s | chat, coding, long documents |
| gpt-oss 20B OpenAI | MXFP4 | fits | 13.0 GB | 128k | 88tok/sest. | 3689 tok/s | chat, coding, agents, long documents |
| Qwen3 Coder 30B-A3B Alibaba Qwen | Q8_0 | fits | 34.2 GB | 162k256k with q8 cache | 45tok/sest. | 2272 tok/s | chat, coding, agents, long documents |
| Qwen3 32B Alibaba Qwen | Q8_0 | fits | 41.4 GB | 40k | 11tok/sest. | 430 tok/s | – |
| Gemma 4 12B | Q8_0 | fits | 13.6 GB | 256k | 35tok/sest. | 1605 tok/s | chat, coding, long documents |
| Qwen 3.5 9B Alibaba Qwen | Q8_0 | fits | 10.9 GB | 256k | 45tok/sest. | 2000 tok/s | chat, coding, long documents |
| Mistral Small 3.2 24B Mistral AI | Q8_0 | fits | 29.3 GB | 128k | 16tok/sest. | 730 tok/s | chat |
| Gemma 3 27B | Q8_0 | fits | 30.6 GB | 128k | 15tok/sest. | 735 tok/s | chat |
| DeepSeek R1 Distill Qwen 32B DeepSeek | Q8_0 | fits | 41.4 GB | 54k100k with q8 cache | 11tok/sest. | 493 tok/s | – |
| Ministral 3 14B Mistral AI | Q8_0 | fits | 19.4 GB | 203k256k with q8 cache | 24tok/sest. | 1111 tok/s | chat, coding |
| Qwen3 14B Alibaba Qwen | Q8_0 | fits | 20.6 GB | 40k | 23tok/sest. | 999 tok/s | chat, coding |
| DeepSeek R1 0528 Qwen3 8B DeepSeek | Q8_0 | fits | 13.6 GB | 128k | 35tok/sest. | 1648 tok/s | chat, coding, long documents |
| Qwen3 8B Alibaba Qwen | Q8_0 | fits | 13.6 GB | 40k | 35tok/sest. | 1648 tok/s | chat, coding, long documents |
| Phi-4 14B Microsoft | Q8_0 | fits | 21.7 GB | 16k | 21tok/sest. | 1004 tok/s | chat, coding |
| Qwen 3.5 4B Alibaba Qwen | Q8_0 | fits | 6.2 GB | 256k | 85tok/sest. | 3742 tok/s | chat, coding, long documents |
| Ministral 3 8B Mistral AI | Q8_0 | fits | 13.6 GB | 256k | 35tok/sest. | 1591 tok/s | chat, coding, long documents |
| Granite 4.2 8B IBM | Q8_0 | fits | 14.7 GB | 128k | 32tok/sest. | 1515 tok/s | chat, coding, agents, long documents |
| Gemma 4 E4B | Q8_0 | fits | 9.5 GB | 128k | 52tok/sest. | 2513 tok/s | chat, coding, long documents |
| LFM2.5 8B-A1B Liquid AI | Q8_0 | fits | 10.9 GB | 125k | 107tok/sest. | 8218 tok/s | chat, coding, long documents |
| Llama 3.1 8B Meta | Q6_K | fits | 11.1 GB | 128k | 44tok/sest. | 1741 tok/s | chat, coding, long documents |
| Qwen2.5 0.5B Alibaba Qwen | Q8_0 | fits | 1.9 GB | 32k | 509tok/sest. | 17899 tok/s | chat, coding, long documents |
| Qwen3.5 0.8B Alibaba Qwen | Q8_0 | fits | 2.1 GB | 256k | 385tok/sest. | 16813 tok/s | chat, coding, agents, long documents |
| Qwen3.5 2B Alibaba Qwen | Q5_K_M | fits | 2.7 GB | 256k | 256tok/sest. | 8015 tok/s | chat, coding, agents, long documents |
| DeepSeek R1 Distill Qwen 1.5B DeepSeek | Q8_0 | fits | 3.6 GB | 128k | 168tok/sest. | 6733 tok/s | chat, coding, long documents |
| MiniCPM5 2B openbmb | Q8_0 | fits | 4.8 GB | 128k | 116tok/sest. | 4021 tok/s | chat, coding, agents, long documents |
| Spark X2.5 4B XHToken | mlx-4bit | fits | 5.0 GB | 1024k | 110tok/sest. | 3949 tok/s | chat, coding, agents, long documents |
| LFM2.5 2.6B Liquid AI | Q8_0 | fits | 5.5 GB | 128k | 97tok/sest. | 4555 tok/s | chat, coding, agents, long documents |
| LFM2.5 VL 3B Liquid AI | Q8_0 | fits | 5.5 GB | 32k | 97tok/sest. | 4177 tok/s | chat, coding, agents, long documents |
| Mellum2.1 12B A2.5B Thinking JetBrains | Q8_0 | fits | 13.5 GB | 128k | 90tok/sest. | 5934 tok/s | chat, coding, agents, long documents |
| FrogNano 4B 2609 Microsoft | Q8_0 | fits | 6.3 GB | 256k | 83tok/sest. | 3742 tok/s | chat, coding, agents, long documents |
| granite vision 4.1 4b IBM | Q8_0 | fits | 6.9 GB | 128k | 75tok/sest. | 2918 tok/s | chat, coding, long documents |
| granite 4.2 3b IBM | Q8_0 | fits | 7.1 GB | 128k | 72tok/sest. | 3058 tok/s | chat, coding, agents, long documents |
| Kimi VL A3B Thinking 2506 Moonshot AI | Q8_0 | fits | 17.7 GB | 128k | 69tok/sest. | 4459 tok/s | chat, coding, long documents |
| Kimi VL A3B Moonshot AI | mlx-8bit | fits | 18.5 GB | 128k | 67tok/sest. | 4459 tok/s | chat, coding, agents, long documents |
| Ministral 3 3B Instruct 2512 Mistral AI | Q8_0 | fits | 7.6 GB | 256k | 67tok/sest. | 2924 tok/s | chat, coding, agents, long documents |
| LFM2.5 24B A2B Liquid AI | Q8_0 | fits | 27.2 GB | 128k | 59tok/sest. | 4580 tok/s | chat, coding, agents, long documents |
| GLM 4 9B 0414 Z.ai | mlx-5bit | fits | 9.9 GB | 32k | 50tok/sest. | 1453 tok/s | chat, coding, agents, long documents |
| DeepSeek R1 Distill Qwen 7B DeepSeek | Q8_0 | fits | 10.3 GB | 128k | 48tok/sest. | 1968 tok/s | chat, coding, long documents |
| GLM 4.6V Flash Z.ai | Q8_0 | fits | 11.6 GB | 128k | 42tok/sest. | 1370 tok/s | chat, coding, agents, long documents |
| DeepSeek R1 Distill Llama 8B DeepSeek | Q8_0 | fits | 12.9 GB | 128k | 37tok/sest. | 1741 tok/s | chat, coding, long documents |
| GLM 4.1V 9B Thinking Z.ai | mlx-8bit | fits | 13.2 GB | 64k | 36tok/sest. | 1370 tok/s | chat, coding, long documents |
| Ministral 3 8B Reasoning 2512 Mistral AI | Q8_0 | fits | 13.6 GB | 256k | 35tok/sest. | 1591 tok/s | chat, coding, agents, long documents |
| Ministral 3 14B Reasoning 2512 Mistral AI | Q8_0 | fits | 19.4 GB | 203k256k with q8 cache | 24tok/sest. | 1111 tok/s | chat, coding |
| DeepSeek R1 Distill Qwen 14B DeepSeek | Q8_0 | fits | 21.6 GB | 128k | 22tok/sest. | 940 tok/s | chat, coding |
| Qwen3.8 27B OBLITERATED OBLITERATUS | mlx-4bit | fits | 22.4 GB | 256k | 21tok/sest. | 692 tok/s | chat, coding, long documents |
| Devstral Small 2507 Mistral AI | Q8_0 | fits | 29.3 GB | 128k | 16tok/sest. | 741 tok/s | chat |
| Magistral Small 2506 Mistral AI | Q8_0 | fits | 29.3 GB | 40k | 16tok/sest. | 741 tok/s | chat |
| Magistral Small 2507 Mistral AI | Q8_0 | fits | 29.3 GB | 40k | 16tok/sest. | 741 tok/s | chat |
| Magistral Small 2509 Mistral AI | Q8_0 | fits | 29.3 GB | 128k | 16tok/sest. | 730 tok/s | chat |
| Muse Glimmer 30B meta-models | Q8_0 | fits | 31.6 GB | 128k | 15tok/sest. | 667 tok/s | – |
| granite 4.2 30b IBM | Q8_0 | fits | 38.0 GB | 68k125k with q8 cache | 12tok/sest. | 567 tok/s | – |
| Nemotron 3 Super 120B-A12B NVIDIA | UD-Q2_K_XL | offload | 52.3 GB | – | 28tok/sest. | 1543 tok/s | chat, coding, long documents |
| gpt-oss 120B OpenAI | Q4_K_M | offload | 60.6 GB | – | 24tok/sest. | 2268 tok/s | chat, coding |
| GLM 4.5 Air Z.ai | Q2_K | offload | 48.9 GB | 22k41k with q8 cache | 19tok/sest. | 655 tok/s | chat |
| Llama 3.3 70B Meta | Q4_K_M | offload | 50.6 GB | 21k39k with q8 cache | 6tok/sest. | 224 tok/s | – |
| GLM 4.5V Z.ai | Q4_K_M | offload | 66.0 GB | – | 6tok/sest. | 541 tok/s | – |
| DeepSeek R1 Distill Llama 70B DeepSeek | Q4_K_M | offload | 50.6 GB | 21k39k with q8 cache | 6tok/sest. | 224 tok/s | – |
| GLM 4.6V Z.ai | Q4_K_M | offload | 72.3 GB | – | 5tok/sest. | 516 tok/s | – |
| Kimi Dev 72B Moonshot AI | Q6_K | offload | 70.9 GB | – | 2tok/sest. | 182 tok/s | – |
| DeepSeek V4 Flash DeepSeek | UD-Q2_K_XL | won't run | 93.9 GB | – | – | – | – |
| GLM 5.3 Flash Z.ai | UD-Q2_K_XL | won't run | 102.7 GB | – | – | – | – |
| Qwen 3.8 Flash Next Alibaba Qwen | UD-Q2_K_XL | won't run | 75.3 GB | – | – | – | – |
| MiniMax M2.7 MiniMax | UD-Q2_K_XL | won't run | 78.9 GB | – | – | – | – |
| DeepSeek R1 DeepSeek | Q4_K_M | won't run | 379.8 GB | – | – | – | – |
| DeepSeek R1 0528 DeepSeek | Q4_K_M | won't run | 380.3 GB | – | – | – | – |
| DeepSeek R1 Zero DeepSeek | Q4_K_M | won't run | 379.8 GB | – | – | – | – |
| DeepSeek V3 DeepSeek | Q4_K_M | won't run | 379.8 GB | – | – | – | – |
| DeepSeek V3 0324 DeepSeek | Q4_K_M | won't run | 379.8 GB | – | – | – | – |
| DeepSeek V3.1 DeepSeek | Q4_K_M | won't run | 380.7 GB | – | – | – | – |
| DeepSeek V3.1 Terminus DeepSeek | Q4_K_M | won't run | 380.7 GB | – | – | – | – |
| DeepSeek V3.2 DeepSeek | Q4_K_M | won't run | 380.7 GB | – | – | – | – |
| DeepSeek V4 Flash 0731 DeepSeek | MXFP4 | won't run | 149.3 GB | – | – | – | – |
| DeepSeek V4 Flash Vision Exp DeepSeek | MXFP4 | won't run | 158.1 GB | – | – | – | – |
| Devstral 2 123B Instruct 2512 Mistral AI | Q4_K_M | won't run | 81.7 GB | – | – | – | – |
| GLM 4.5 Z.ai | Q4_K_M | won't run | 214.1 GB | – | – | – | – |
| GLM 4.6 Z.ai | Q4_K_M | won't run | 213.3 GB | – | – | – | – |
| GLM 4.7 Z.ai | Q4_K_M | won't run | 214.1 GB | – | – | – | – |
| GLM 5 Z.ai | MXFP4 | won't run | 386.1 GB | – | – | – | – |
| GLM 5.1 Z.ai | MXFP4 | won't run | 423.7 GB | – | – | – | – |
| GLM 5.2 Z.ai | UD-Q4_K_M | won't run | 437.6 GB | – | – | – | – |
| GLM 5.3 Z.ai | Q8_0 | won't run | 750.1 GB | – | – | – | – |
| Kimi K2 Moonshot AI | Q4_K_M | won't run | 581.3 GB | – | – | – | – |
| Kimi K2 Instruct 0905 Moonshot AI | Q4_K_M | won't run | 581.7 GB | – | – | – | – |
| Kimi K2 Thinking Moonshot AI | Q4_K_M | won't run | 581.7 GB | – | – | – | – |
| Kimi K2.5 Moonshot AI | Q4_K_M | won't run | 581.7 GB | – | – | – | – |
| MiniMax M2 MiniMax | Q4_K_M | won't run | 137.6 GB | – | – | – | – |
| MiniMax M2.1 MiniMax | Q4_K_M | won't run | 137.6 GB | – | – | – | – |
| MiniMax M2.5 MiniMax | MXFP4 | won't run | 124.0 GB | – | – | – | – |
| MiniMax M3 MiniMax | MXFP4 | won't run | 244.4 GB | – | – | – | – |
| Mistral Medium 3.5 128B Mistral AI | Q4_K_M | won't run | 81.7 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 27B (Q8_0) fits fully in its memory at 32k context and generates at about 15 tok/s, an estimate from its memory bandwidth. 65 of the 125 models we track fit at that context.
- Can the AMD Radeon PRO W7900 run DeepSeek V4 Flash?
- No: even at UD-Q2_K_XL, DeepSeek V4 Flash needs about 94 GB at 32k context, more than the AMD Radeon PRO W7900 and system RAM can hold.
- 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.