NVIDIA RTX PRO 2000 Blackwell
16 GB · 288 GB/s · 70 W
In short
The NVIDIA RTX PRO 2000 Blackwell has 16 GB of GDDR7 memory with 288 GB/s of bandwidth, which sets how fast it generates text. At 8k context it runs 62 of the 129 open models we track fully in memory; the most capable is Qwen 3.8 27B at Q8_0, at about 6 tok/s (estimated). The best new price for the NVIDIA RTX PRO 2000 Blackwell in Germany is 1.000 € at Amazon.
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.
| Retailer | Condition | Price | Stock | Checked | Buy |
|---|---|---|---|---|---|
| AmazonPNY NVIDIA RTX 2000 Ada Professional Graphics Card 16GB GDDR7 | New | 1.000 € | In stock | 5 h ago | Buy |
| eBayLenovo NVIDIA Grafikkarte RTX PRO 2000 Blackwell 16 GB GDDR7 PCIe 5.0 x8 4 x | New | 1.123 € | In stock | 49 min ago | Buy |
| eBayNVIDIA | New | 1.214 € | In stock | 49 min ago | Buy |
| eBayNVIDIA | New | 1.254 € | In stock | 49 min ago | Buy |
| AmazonPNY Quadro RTX PRO 2000 Blackwell 16GB GDDR7 4xmDP | New | 1.470 € | In stock | 5 h ago | Buy |
| AmazonPNY NVIDIA RTX Pro 2000 Blackwell | New | 1.470 € | In stock | 5 h ago | Buy |
| AmazonNvidia Rtx Pro 2000 Blackwell 16 GB Gddr7 | New | 1.470 € | In stock | 5 h ago | Buy |
| eBayGrafikkarte NVIDIA RTX PRO 2000 Blackwell 16GB PCIe 5.0 x8 Retail | New | 1.484 €+ 6 € delivery | In stock | 49 min ago | Buy |
| eBayPNY RTX Pro 2000 Blackwell | New | 1.916 €+ 5 € delivery | In stock | 49 min ago | Buy |
| eBayPNY RTX PRO 2000 Blackwell | New | 2.071 €+ 5 € delivery | In stock | 49 min ago | Buy |
Price history
Lowest price shown each day over the last 90 days.
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 @8k | Prefill | Good for |
|---|---|---|---|---|---|---|---|
| Qwen 3.8 27B Alibaba Qwen | Q8_0 | fits | 28.8 GB | 36k64k with q8 cache | 6tok/sest. | 134 tok/s | – |
| Qwen 3.6 27B Alibaba Qwen | Q8_0 | fits | 28.4 GB | 42k74k with q8 cache | 6tok/sest. | 134 tok/s | – |
| Gemma 4 31B | Q6_K | fits | 26.0 GB | 64k122k with q8 cache | 7tok/sest. | 117 tok/s | – |
| Qwen 3.6 35B-A3B Alibaba Qwen | Q6_K | fits | 30.2 GB | 32k50k with q8 cache | 38tok/sest. | 1149 tok/s | chat, coding, agents, long documents |
| Qwen 3.5 27B Alibaba Qwen | Q8_0 | fits | 28.4 GB | 42k74k with q8 cache | 6tok/sest. | 134 tok/s | – |
| Gemma 4 26B-A4B | Q8_0 | fits | 26.5 GB | 176k256k with q8 cache | 24tok/sest. | 875 tok/s | chat, coding, long documents |
| Qwen 3.5 35B-A3B Alibaba Qwen | Q6_K | fits | 28.2 GB | 109k170k with q8 cache | 41tok/sest. | 1149 tok/s | chat, coding, agents, long documents |
| GLM 4.7 Flash Z.ai | Q6_K | fits | 24.6 GB | 116k198k with q8 cache | 24tok/sest. | 643 tok/s | chat |
| Devstral Small 2 24B Mistral AI | Q8_0 | fits | 25.7 GB | 39k71k with q8 cache | 7tok/sest. | 151 tok/s | – |
| Nemotron 3.5 Lightning 30B-A3B NVIDIA | UD-Q5_K_M | fits | 29.4 GB | 129k168k with q8 cache | 36tok/sest. | 1197 tok/s | chat, coding, agents, long documents |
| Kimi Linear 48B-A3B Moonshot AI | Q4_K_M | fits | 29.2 GB | 122k167k with q8 cache | 55tok/sest. | 1092 tok/s | chat, coding, long documents |
| gpt-oss 20B OpenAI | MXFP4 | fits | 12.6 GB | 128k | 41tok/sest. | 832 tok/s | chat, coding, agents, long documents |
| Qwen3 Coder 30B-A3B Alibaba Qwen | Q6_K | fits | 25.3 GB | 63k113k with q8 cache | 33tok/sest. | 831 tok/s | chat |
| Qwen3 32B Alibaba Qwen | Q6_K | fits | 28.2 GB | 18k33k with q8 cache | 6tok/sest. | 103 tok/s | – |
| Gemma 4 12B | Q8_0 | fits | 13.4 GB | 256k | 14tok/sest. | 305 tok/s | – |
| Qwen 3.5 9B Alibaba Qwen | Q8_0 | fits | 10.3 GB | 256k | 18tok/sest. | 386 tok/s | chat |
| Mistral Small 3.2 24B Mistral AI | Q8_0 | fits | 25.7 GB | 39k71k with q8 cache | 7tok/sest. | 151 tok/s | – |
| DeepSeek R1 Distill Qwen 32B DeepSeek | Q6_K | fits | 28.2 GB | 18k33k with q8 cache | 6tok/sest. | 108 tok/s | – |
| Gemma 3 27B | Q8_0 | fits | 28.9 GB | 30k58k with q8 cache | 6tok/sest. | 136 tok/s | – |
| Ministral 3 14B Mistral AI | Q8_0 | fits | 15.8 GB | 100k183k with q8 cache | 11tok/sest. | 251 tok/s | – |
| Qwen3 14B Alibaba Qwen | Q8_0 | fits | 17.0 GB | 40k | 11tok/sest. | 233 tok/s | – |
| DeepSeek R1 0528 Qwen3 8B DeepSeek | Q8_0 | fits | 10.4 GB | 128k | 18tok/sest. | 408 tok/s | chat |
| Qwen3 8B Alibaba Qwen | Q8_0 | fits | 10.4 GB | 40k | 18tok/sest. | 408 tok/s | chat |
| Phi-4 14B Microsoft | Q8_0 | fits | 17.2 GB | 16k | 10tok/sest. | 235 tok/s | – |
| Qwen 3.5 4B Alibaba Qwen | Q8_0 | fits | 5.6 GB | 256k | 37tok/sest. | 778 tok/s | chat, coding, long documents |
| Ministral 3 8B Mistral AI | Q8_0 | fits | 10.6 GB | 153k256k with q8 cache | 18tok/sest. | 381 tok/s | chat |
| Granite 4.2 8B IBM | Q8_0 | fits | 11.1 GB | 128k | 17tok/sest. | 378 tok/s | chat |
| Gemma 4 E4B | Q8_0 | fits | 9.0 GB | 128k | 21tok/sest. | 468 tok/s | chat |
| LFM2.5 8B-A1B Liquid AI | Q8_0 | fits | 9.9 GB | 125k | 73tok/sest. | 2732 tok/s | chat, coding |
| Llama 3.1 8B Meta | Q6_K | fits | 8.3 GB | 128k | 23tok/sest. | 421 tok/s | chat |
| Qwen2.5 0.5B Alibaba Qwen | Q8_0 | fits | 1.7 GB | 32k | 283tok/sest. | 5717 tok/s | chat, coding, long documents |
| Qwen3.5 0.8B Alibaba Qwen | Q8_0 | fits | 2.0 GB | 256k | 192tok/sest. | 3934 tok/s | chat, coding, agents, long documents |
| Qwen3.5 2B Alibaba Qwen | Q5_K_M | fits | 2.6 GB | 256k | 115tok/sest. | 1614 tok/s | chat, coding, agents, long documents |
| LFM3 1.7B Liquid AI | Q8_0 | fits | 3.0 GB | 256k | 91tok/sest. | 2073 tok/s | chat, coding, agents, long documents |
| DeepSeek R1 Distill Qwen 1.5B DeepSeek | Q8_0 | fits | 3.1 GB | 128k | 84tok/sest. | 1799 tok/s | chat, coding, long documents |
| MiniCPM5 2B openbmb | Q8_0 | fits | 4.0 GB | 128k | 59tok/sest. | 1190 tok/s | chat, coding, agents |
| LFM2.5 2.6B Liquid AI | Q8_0 | fits | 4.3 GB | 128k | 53tok/sest. | 1197 tok/s | chat, coding, agents, long documents |
| LFM2.5 VL 3B Liquid AI | Q8_0 | fits | 4.3 GB | 32k | 53tok/sest. | 1057 tok/s | chat, coding, agents, long documents |
| Spark X2.5 4B XHToken | mlx-4bit | fits | 4.3 GB | 653k1024k with q8 cache | 53tok/sest. | 846 tok/s | chat, coding, agents, long documents |
| granite vision 4.1 4b IBM | Q8_0 | fits | 5.1 GB | 128k | 42tok/sest. | 792 tok/s | chat, coding |
| Ministral 3 3B Instruct 2512 Mistral AI | Q8_0 | fits | 5.4 GB | 244k256k with q8 cache | 40tok/sest. | 812 tok/s | chat, coding |
| granite 4.2 3b IBM | Q8_0 | fits | 5.4 GB | 128k | 39tok/sest. | 852 tok/s | chat, coding |
| LFM2.5 24B A2B Liquid AI | Q8_0 | fits | 25.5 GB | 70k125k with q8 cache | 38tok/sest. | 1435 tok/s | chat, coding |
| Mellum2.1 12B A2.5B Thinking JetBrains | Q8_0 | fits | 13.3 GB | 128k | 38tok/sest. | 1316 tok/s | chat, coding, agents, long documents |
| FrogNano 4B 2609 Microsoft | Q8_0 | fits | 5.7 GB | 256k | 36tok/sest. | 778 tok/s | chat, coding, agents, long documents |
| Kimi VL A3B Thinking 2506 Moonshot AI | Q8_0 | fits | 17.2 GB | 128k | 32tok/sest. | 1110 tok/s | chat, coding |
| Kimi VL A3B Moonshot AI | mlx-8bit | fits | 18.0 GB | 128k | 31tok/sest. | 1041 tok/s | chat, coding |
| DeepSeek R1 Distill Qwen 7B DeepSeek | Q8_0 | fits | 9.1 GB | 128k | 21tok/sest. | 454 tok/s | chat |
| GLM 4 9B 0414 Z.ai | mlx-5bit | fits | 9.1 GB | 32k | 21tok/sest. | 357 tok/s | chat |
| DeepSeek R1 Distill Llama 8B DeepSeek | Q8_0 | fits | 10.1 GB | 128k | 19tok/sest. | 421 tok/s | chat |
| Ministral 3 8B Reasoning 2512 Mistral AI | Q8_0 | fits | 10.6 GB | 153k256k with q8 cache | 18tok/sest. | 381 tok/s | chat |
| GLM 4.6V Flash Z.ai | Q8_0 | fits | 10.8 GB | 128k | 17tok/sest. | 329 tok/s | chat |
| GLM 4.1V 9B Thinking Z.ai | mlx-8bit | fits | 12.4 GB | 64k | 15tok/sest. | 329 tok/s | – |
| Ministral 3 14B Reasoning 2512 Mistral AI | Q8_0 | fits | 15.8 GB | 100k183k with q8 cache | 11tok/sest. | 251 tok/s | – |
| DeepSeek R1 Distill Qwen 14B DeepSeek | Q8_0 | fits | 17.3 GB | 77k128k with q8 cache | 10tok/sest. | 228 tok/s | – |
| Qwen3.8 27B OBLITERATED OBLITERATUS | mlx-4bit | fits | 21.0 GB | 150k256k with q8 cache | 8tok/sest. | 134 tok/s | – |
| Muse Glimmer 30B meta-models | Q6_K | fits | 23.1 GB | 128k | 8tok/sest. | 124 tok/s | – |
| Devstral Small 2507 Mistral AI | Q8_0 | fits | 25.7 GB | 39k71k with q8 cache | 7tok/sest. | 154 tok/s | – |
| Magistral Small 2506 Mistral AI | Q8_0 | fits | 25.7 GB | 39k40k with q8 cache | 7tok/sest. | 154 tok/s | – |
| Magistral Small 2507 Mistral AI | Q8_0 | fits | 25.7 GB | 39k40k with q8 cache | 7tok/sest. | 154 tok/s | – |
| Magistral Small 2509 Mistral AI | Q8_0 | fits | 25.7 GB | 39k71k with q8 cache | 7tok/sest. | 151 tok/s | – |
| granite 4.2 30b IBM | Q6_K | fits | 26.0 GB | 26k49k with q8 cache | 7tok/sest. | 122 tok/s | – |
| Qwen 3.5 122B-A10B Alibaba Qwen | UD-Q2_K_XL | offload | 40.4 GB | – | 19tok/sest. | 302 tok/s | chat |
| Qwen3 Coder Next Alibaba Qwen | Q4_K_M | offload | 46.6 GB | – | 23tok/sest. | 709 tok/s | chat |
| Nemotron 3 Super 120B-A12B NVIDIA | UD-Q2_K_XL | offload | 52.3 GB | – | 10tok/sest. | 234 tok/s | – |
| Mistral Small 4 119B Mistral AI | UD-Q2_K_XL | offload | 38.8 GB | – | 28tok/sest. | 405 tok/s | chat, coding |
| GLM 4.5 Air Z.ai | Q2_K | offload | 44.8 GB | – | 9tok/sest. | 171 tok/s | – |
| Qwen3 Next 80B-A3B Alibaba Qwen | Q4_K_M | offload | 46.6 GB | – | 29tok/sest. | 891 tok/s | chat, coding |
| Llama 3.3 70B Meta | Q4_K_M | offload | 43.2 GB | – | 2tok/sest. | 42 tok/s | – |
| DeepSeek R1 Distill Llama 70B DeepSeek | Q4_K_M | offload | 43.2 GB | – | 2tok/sest. | 42 tok/s | – |
| DeepSeek V4 Flash DeepSeek | UD-Q2_K_XL | won't run | 92.0 GB | – | – | – | – |
| GLM 5.3 Flash Z.ai | UD-Q2_K_XL | won't run | 102.6 GB | – | – | – | – |
| Qwen 3.8 Flash Next Alibaba Qwen | UD-Q2_K_XL | won't run | 74.9 GB | – | – | – | – |
| MiniMax M2.7 MiniMax | UD-Q2_K_XL | won't run | 73.2 GB | – | – | – | – |
| gpt-oss 120B OpenAI | Q4_K_M | won't run | 59.9 GB | – | – | – | – |
| DeepSeek R1 DeepSeek | Q4_K_M | won't run | 378.3 GB | – | – | – | – |
| DeepSeek R1 0528 DeepSeek | Q4_K_M | won't run | 378.8 GB | – | – | – | – |
| DeepSeek R1 Zero DeepSeek | Q4_K_M | won't run | 378.3 GB | – | – | – | – |
| DeepSeek V3 DeepSeek | Q4_K_M | won't run | 378.3 GB | – | – | – | – |
| DeepSeek V3 0324 DeepSeek | Q4_K_M | won't run | 378.3 GB | – | – | – | – |
| DeepSeek V3.1 DeepSeek | Q4_K_M | won't run | 379.2 GB | – | – | – | – |
| DeepSeek V3.1 Terminus DeepSeek | Q4_K_M | won't run | 379.2 GB | – | – | – | – |
| DeepSeek V3.2 DeepSeek | Q4_K_M | won't run | 379.2 GB | – | – | – | – |
| DeepSeek V4 Flash 0731 DeepSeek | MXFP4 | won't run | 147.5 GB | – | – | – | – |
| DeepSeek V4 Flash Vision Exp DeepSeek | MXFP4 | won't run | 156.2 GB | – | – | – | – |
| Devstral 2 123B Instruct 2512 Mistral AI | Q4_K_M | won't run | 73.7 GB | – | – | – | – |
| GLM 4.5 Z.ai | Q4_K_M | won't run | 205.6 GB | – | – | – | – |
| GLM 4.5V Z.ai | Q4_K_M | won't run | 61.8 GB | – | – | – | – |
| GLM 4.6 Z.ai | Q4_K_M | won't run | 204.8 GB | – | – | – | – |
| GLM 4.6V Z.ai | Q4_K_M | won't run | 68.2 GB | – | – | – | – |
| GLM 4.7 Z.ai | Q4_K_M | won't run | 205.6 GB | – | – | – | – |
| GLM 5 Z.ai | MXFP4 | won't run | 384.2 GB | – | – | – | – |
| GLM 5.1 Z.ai | MXFP4 | won't run | 421.8 GB | – | – | – | – |
| GLM 5.2 Z.ai | UD-Q4_K_M | won't run | 435.7 GB | – | – | – | – |
| GLM 5.3 Z.ai | Q8_0 | won't run | 748.2 GB | – | – | – | – |
| Kimi Dev 72B Moonshot AI | Q6_K | won't run | 63.6 GB | – | – | – | – |
| Kimi K2 Moonshot AI | Q4_K_M | won't run | 579.8 GB | – | – | – | – |
| Kimi K2 Instruct 0905 Moonshot AI | Q4_K_M | won't run | 580.3 GB | – | – | – | – |
| Kimi K2 Thinking Moonshot AI | Q4_K_M | won't run | 580.3 GB | – | – | – | – |
| Kimi K2.5 Moonshot AI | Q4_K_M | won't run | 580.3 GB | – | – | – | – |
| MiniMax M2 MiniMax | Q4_K_M | won't run | 131.9 GB | – | – | – | – |
| MiniMax M2.1 MiniMax | Q4_K_M | won't run | 131.9 GB | – | – | – | – |
| MiniMax M2.5 MiniMax | MXFP4 | won't run | 118.4 GB | – | – | – | – |
| MiniMax M3 MiniMax | MXFP4 | won't run | 241.8 GB | – | – | – | – |
| Mistral Medium 3.5 128B Mistral AI | Q4_K_M | won't run | 73.7 GB | – | – | – | – |
Specifications
From the manufacturer's published specifications; sources are linked where we have them.
- GPU
- RTX PRO 2000 Blackwell
- Memory
- 16 GB GDDR7
- Memory bus
- 128-bit
- Memory bandwidth
- 288 GB/s
- FP16 compute
- 17.03 TFLOPS
- Software
- CUDA
- Board power
- 70 W
- PCIe generation
- Gen 5
- PCIe lanes
- x8
Compare with
Questions
- How much memory does the NVIDIA RTX PRO 2000 Blackwell have for AI models?
- The NVIDIA RTX PRO 2000 Blackwell has 16 GB of GDDR7 memory with 288 GB/s of bandwidth, which sets how fast it generates text.
- What is the best model the NVIDIA RTX PRO 2000 Blackwell can run locally?
- Qwen 3.8 27B (Q8_0) fits fully in its memory at 8k context and generates at about 6 tok/s, an estimate from its memory bandwidth. 62 of the 129 models we track fit at that context.
- Can the NVIDIA RTX PRO 2000 Blackwell run DeepSeek V4 Flash?
- No: even at UD-Q2_K_XL, DeepSeek V4 Flash needs about 92 GB at 8k context, more than the NVIDIA RTX PRO 2000 Blackwell and system RAM can hold.
- How much does the NVIDIA RTX PRO 2000 Blackwell cost in Germany?
- The best new price for the NVIDIA RTX PRO 2000 Blackwell in Germany is 1.000 € at Amazon.
Speeds on this page are for 8k context. See the best hardware for local AI at every budget.