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 32k context it runs 37 of the 129 open models we track fully in memory; the most capable is gpt-oss 20B at MXFP4, at about 33 tok/s (estimated). The best new price for the NVIDIA RTX PRO 2000 Blackwell in the US is $1,580 at eBay; used, it starts at $1,439 (asking price).
Updated . Speeds are estimates; how they are worked out.
Prices
Every retailer we track in the US, cheapest first. Buy links may earn us a commission.
| Retailer | Condition | Price | Stock | Checked | Buy |
|---|---|---|---|---|---|
| eBayPNY NVIDIA RTX PRO 2000 Blackwell Single Fan 16GB GDDR7 PCIe 5.0 Grap | used, asking price | $1,439 | In stock | 4 h ago | Buy |
| eBayNEW NVIDIA RTX PRO 2000 BLACKWELL 16GB Graphics Card | New | $1,580 | In stock | 4 h ago | Buy |
| eBayPNY VCNRTXPRO2000B-PB NVIDIA RTX PRO 2000 Blackwell 16GB GDDR7 GPU Graphic Card | New | $1,600+ $4 delivery | In stock | 4 h ago | Buy |
| eBayPNY VCNRTXPRO2000B-B NVIDIA RTX PRO 2000 Blackwell 16GB GDDR7 Workstation GPU | New | $2,013 | In stock | 4 h ago | Buy |
| eBayPNY VCNRTXPRO2000B-PB NVIDIA RTX PRO 2000 Blackwell 16GB GDDR7 Workstation GPU | New | $2,025 | In stock | 4 h ago | Buy |
| eBayPNY VCNRTXPRO2000B-PB NVIDIA RTX PRO 2000 Blackwell Graphic Card | New | $2,210 | In stock | 4 h ago | Buy |
| eBayPNY VCNRTXPRO2000B-PB NVIDIA RTX PRO 2000 Blackwell 16GB GDDR7 GPU Graphic Card | New | $2,233 | In stock | 4 h ago | Buy |
Price history
Lowest price shown each day over the last 90 days.
Not enough price history yet.
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 |
|---|---|---|---|---|---|---|---|
| gpt-oss 20B OpenAI | MXFP4 | fits | 12.9 GB | 116k128k with q8 cache | 33tok/sest. | 659 tok/s | chat, coding, agents, long documents |
| Gemma 4 12B | Q8_0 | fits | 13.5 GB | 120k193k with q8 cache | 13tok/sest. | 287 tok/s | – |
| Qwen 3.5 9B Alibaba Qwen | Q8_0 | fits | 10.8 GB | 155k256k with q8 cache | 17tok/sest. | 357 tok/s | chat |
| Qwen3 14B Alibaba Qwen | Q4_K_M | fits | 14.3 GB | 38k40k with q8 cache | 12tok/sest. | 178 tok/s | chat |
| Ministral 3 14B Mistral AI | Q5_K_M | fits | 14.8 GB | 35k64k with q8 cache | 12tok/sest. | 198 tok/s | chat |
| DeepSeek R1 0528 Qwen3 8B DeepSeek | Q8_0 | fits | 13.5 GB | 44k81k with q8 cache | 13tok/sest. | 294 tok/s | chat |
| Qwen3 8B Alibaba Qwen | Q8_0 | fits | 13.5 GB | 40k | 13tok/sest. | 294 tok/s | chat |
| Qwen 3.5 4B Alibaba Qwen | Q8_0 | fits | 6.1 GB | 256k | 32tok/sest. | 668 tok/s | chat, coding, long documents |
| Ministral 3 8B Mistral AI | Q8_0 | fits | 13.5 GB | 45k82k with q8 cache | 13tok/sest. | 284 tok/s | chat |
| Granite 4.2 8B IBM | Q8_0 | fits | 14.6 GB | 36k67k with q8 cache | 12tok/sest. | 271 tok/s | chat |
| Gemma 4 E4B | Q8_0 | fits | 9.4 GB | 128k | 20tok/sest. | 449 tok/s | chat |
| LFM2.5 8B-A1B Liquid AI | Q8_0 | fits | 10.8 GB | 119k125k with q8 cache | 40tok/sest. | 1468 tok/s | chat, coding |
| Llama 3.1 8B Meta | Q6_K | fits | 11.0 GB | 65k118k with q8 cache | 16tok/sest. | 311 tok/s | chat |
| Qwen2.5 0.5B Alibaba Qwen | Q8_0 | fits | 1.8 GB | 32k | 191tok/sest. | 3197 tok/s | chat, coding, long documents |
| Qwen3.5 0.8B Alibaba Qwen | Q8_0 | fits | 2.0 GB | 256k | 145tok/sest. | 3003 tok/s | chat, coding, agents, long documents |
| Qwen3.5 2B Alibaba Qwen | Q5_K_M | fits | 2.6 GB | 256k | 96tok/sest. | 1432 tok/s | chat, coding, agents, long documents |
| LFM3 1.7B Liquid AI | Q8_0 | fits | 3.0 GB | 256k | 79tok/sest. | 1782 tok/s | chat, coding, agents, long documents |
| DeepSeek R1 Distill Qwen 1.5B DeepSeek | Q8_0 | fits | 3.5 GB | 128k | 63tok/sest. | 1203 tok/s | chat, coding, long documents |
| MiniCPM5 2B openbmb | Q8_0 | fits | 4.7 GB | 128k | 44tok/sest. | 718 tok/s | chat, coding, agents |
| Spark X2.5 4B XHToken | mlx-4bit | fits | 4.9 GB | 288k482k with q8 cache | 42tok/sest. | 705 tok/s | chat, coding, agents, long documents |
| LFM2.5 2.6B Liquid AI | Q8_0 | fits | 5.4 GB | 128k | 37tok/sest. | 814 tok/s | chat, coding, agents, long documents |
| LFM2.5 VL 3B Liquid AI | Q8_0 | fits | 5.4 GB | 32k | 37tok/sest. | 746 tok/s | chat, coding, agents, long documents |
| Kimi VL A3B Thinking 2506 Moonshot AI | Q4_K_M | fits | 11.7 GB | 128k | 36tok/sest. | 796 tok/s | chat, coding, long documents |
| Mellum2.1 12B A2.5B Thinking JetBrains | Q8_0 | fits | 13.4 GB | 128k | 34tok/sest. | 1060 tok/s | chat, coding, agents, long documents |
| FrogNano 4B 2609 Microsoft | Q8_0 | fits | 6.2 GB | 256k | 31tok/sest. | 668 tok/s | chat, coding, agents, long documents |
| Kimi VL A3B Moonshot AI | mlx-6bit | fits | 14.7 GB | 51k84k with q8 cache | 30tok/sest. | 670 tok/s | chat, coding, agents |
| granite vision 4.1 4b IBM | Q8_0 | fits | 6.8 GB | 128k | 28tok/sest. | 521 tok/s | chat, coding |
| granite 4.2 3b IBM | Q8_0 | fits | 7.0 GB | 128k | 27tok/sest. | 546 tok/s | chat, coding |
| Ministral 3 3B Instruct 2512 Mistral AI | Q8_0 | fits | 7.5 GB | 105k188k with q8 cache | 25tok/sest. | 522 tok/s | chat, coding |
| GLM 4 9B 0414 Z.ai | mlx-5bit | fits | 9.8 GB | 32k | 19tok/sest. | 259 tok/s | chat |
| DeepSeek R1 Distill Qwen 7B DeepSeek | Q8_0 | fits | 10.2 GB | 118k128k with q8 cache | 18tok/sest. | 351 tok/s | chat |
| GLM 4.6V Flash Z.ai | Q8_0 | fits | 11.5 GB | 119k128k with q8 cache | 16tok/sest. | 245 tok/s | chat |
| DeepSeek R1 Distill Llama 8B DeepSeek | Q8_0 | fits | 12.8 GB | 51k93k with q8 cache | 14tok/sest. | 311 tok/s | chat |
| GLM 4.1V 9B Thinking Z.ai | mlx-8bit | fits | 13.1 GB | 64k | 14tok/sest. | 245 tok/s | – |
| Ministral 3 8B Reasoning 2512 Mistral AI | Q8_0 | fits | 13.5 GB | 45k82k with q8 cache | 13tok/sest. | 284 tok/s | chat |
| Ministral 3 14B Reasoning 2512 Mistral AI | Q5_K_M | fits | 14.8 GB | 35k64k with q8 cache | 12tok/sest. | 198 tok/s | chat |
| DeepSeek R1 Distill Qwen 14B DeepSeek | Q4_K_M | fits | 15.3 GB | 32k60k with q8 cache | 12tok/sest. | 168 tok/s | chat |
| Qwen 3.8 27B Alibaba Qwen | UD-Q4_K_M | offload | 18.4 GB | – | 7tok/sest. | 111 tok/s | – |
| Qwen 3.5 122B-A10B Alibaba Qwen | UD-Q2_K_XL | offload | 40.7 GB | – | 10tok/sest. | 179 tok/s | – |
| Qwen 3.6 27B Alibaba Qwen | Q4_K_M | offload | 18.7 GB | – | 7tok/sest. | 110 tok/s | – |
| Gemma 4 31B | Q4_K_M | offload | 21.2 GB | – | 5tok/sest. | 91 tok/s | – |
| Mistral Small 4 119B Mistral AI | UD-Q2_K_XL | offload | 39.0 GB | – | 14tok/sest. | 202 tok/s | chat |
| Qwen 3.6 35B-A3B Alibaba Qwen | MXFP4 | offload | 21.8 GB | – | 25tok/sest. | 721 tok/s | chat, coding |
| Qwen 3.5 27B Alibaba Qwen | Q4_K_M | offload | 18.6 GB | – | 7tok/sest. | 110 tok/s | – |
| Gemma 4 26B-A4B | MXFP4 | offload | 17.1 GB | – | 25tok/sest. | 735 tok/s | chat, coding |
| Qwen 3.5 35B-A3B Alibaba Qwen | Q4_K_M | offload | 22.1 GB | – | 24tok/sest. | 716 tok/s | chat, coding |
| GLM 4.7 Flash Z.ai | Q4_K_M | offload | 19.6 GB | – | 15tok/sest. | 399 tok/s | chat |
| Devstral Small 2 24B Mistral AI | Q4_K_M | offload | 19.2 GB | 8k15k with q8 cache | 6tok/sest. | 114 tok/s | – |
| Nemotron 3.5 Lightning 30B-A3B NVIDIA | MXFP4 | offload | 22.7 GB | – | 25tok/sest. | 804 tok/s | chat, coding, long documents |
| Kimi Linear 48B-A3B Moonshot AI | Q4_K_M | offload | 29.2 GB | – | 24tok/sest. | 538 tok/s | chat, coding |
| Qwen3 Coder 30B-A3B Alibaba Qwen | Q4_K_M | offload | 21.2 GB | – | 13tok/sest. | 337 tok/s | chat |
| Qwen3 32B Alibaba Qwen | Q4_K_M | offload | 27.3 GB | – | 3tok/sest. | 56 tok/s | – |
| Gemma 3 27B | Q4_K_M | offload | 19.2 GB | – | 6tok/sest. | 115 tok/s | – |
| Mistral Small 3.2 24B Mistral AI | Q4_K_M | offload | 19.2 GB | 8k15k with q8 cache | 6tok/sest. | 114 tok/s | – |
| DeepSeek R1 Distill Qwen 32B DeepSeek | Q4_K_M | offload | 27.4 GB | – | 3tok/sest. | 64 tok/s | – |
| Phi-4 14B Microsoft | Q4_K_M | offload | 15.4 GB | 16k | 11tok/sest. | 179 tok/s | chat |
| LFM2.5 24B A2B Liquid AI | Q4_K_M | offload | 17.7 GB | 4k8k with q8 cache | 21tok/sest. | 751 tok/s | chat, coding |
| Muse Glimmer 30B meta-models | Q4_K_M | offload | 17.5 GB | – | 8tok/sest. | 110 tok/s | – |
| Devstral Small 2507 Mistral AI | Q4_K_M | offload | 19.2 GB | 8k15k with q8 cache | 6tok/sest. | 116 tok/s | – |
| Magistral Small 2506 Mistral AI | Q4_K_M | offload | 19.2 GB | 8k15k with q8 cache | 6tok/sest. | 116 tok/s | – |
| Magistral Small 2507 Mistral AI | Q4_K_M | offload | 19.2 GB | 8k15k with q8 cache | 6tok/sest. | 116 tok/s | – |
| Magistral Small 2509 Mistral AI | Q4_K_M | offload | 19.2 GB | 8k15k with q8 cache | 6tok/sest. | 114 tok/s | – |
| Qwen3.8 27B OBLITERATED OBLITERATUS | mlx-4bit | offload | 22.3 GB | – | 5tok/sest. | 100 tok/s | – |
| granite 4.2 30b IBM | Q4_K_M | offload | 25.7 GB | – | 3tok/sest. | 76 tok/s | – |
| DeepSeek V4 Flash DeepSeek | UD-Q2_K_XL | won't run | 93.8 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 | 75.2 GB | – | – | – | – |
| MiniMax M2.7 MiniMax | UD-Q2_K_XL | won't run | 78.8 GB | – | – | – | – |
| Nemotron 3 Super 120B-A12B NVIDIA | UD-Q2_K_XL | won't run | 52.2 GB | – | – | – | – |
| Qwen3 Coder Next Alibaba Qwen | Q4_K_M | won't run | 46.9 GB | – | – | – | – |
| gpt-oss 120B OpenAI | Q4_K_M | won't run | 60.5 GB | – | – | – | – |
| GLM 4.5 Air Z.ai | Q2_K | won't run | 48.8 GB | – | – | – | – |
| Qwen3 Next 80B-A3B Alibaba Qwen | Q4_K_M | won't run | 46.9 GB | – | – | – | – |
| Llama 3.3 70B Meta | Q4_K_M | won't run | 50.5 GB | – | – | – | – |
| DeepSeek R1 DeepSeek | Q4_K_M | won't run | 379.7 GB | – | – | – | – |
| DeepSeek R1 0528 DeepSeek | Q4_K_M | won't run | 380.2 GB | – | – | – | – |
| DeepSeek R1 Distill Llama 70B DeepSeek | Q4_K_M | won't run | 50.5 GB | – | – | – | – |
| DeepSeek R1 Zero DeepSeek | Q4_K_M | won't run | 379.7 GB | – | – | – | – |
| DeepSeek V3 DeepSeek | Q4_K_M | won't run | 379.7 GB | – | – | – | – |
| DeepSeek V3 0324 DeepSeek | Q4_K_M | won't run | 379.7 GB | – | – | – | – |
| DeepSeek V3.1 DeepSeek | Q4_K_M | won't run | 380.6 GB | – | – | – | – |
| DeepSeek V3.1 Terminus DeepSeek | Q4_K_M | won't run | 380.6 GB | – | – | – | – |
| DeepSeek V3.2 DeepSeek | Q4_K_M | won't run | 380.6 GB | – | – | – | – |
| DeepSeek V4 Flash 0731 DeepSeek | MXFP4 | won't run | 149.2 GB | – | – | – | – |
| DeepSeek V4 Flash Vision Exp DeepSeek | MXFP4 | won't run | 158.0 GB | – | – | – | – |
| Devstral 2 123B Instruct 2512 Mistral AI | Q4_K_M | won't run | 81.6 GB | – | – | – | – |
| GLM 4.5 Z.ai | Q4_K_M | won't run | 214.0 GB | – | – | – | – |
| GLM 4.5V Z.ai | Q4_K_M | won't run | 65.9 GB | – | – | – | – |
| GLM 4.6 Z.ai | Q4_K_M | won't run | 213.2 GB | – | – | – | – |
| GLM 4.6V Z.ai | Q4_K_M | won't run | 72.2 GB | – | – | – | – |
| GLM 4.7 Z.ai | Q4_K_M | won't run | 214.0 GB | – | – | – | – |
| GLM 5 Z.ai | MXFP4 | won't run | 386.0 GB | – | – | – | – |
| GLM 5.1 Z.ai | MXFP4 | won't run | 423.6 GB | – | – | – | – |
| GLM 5.2 Z.ai | UD-Q4_K_M | won't run | 437.5 GB | – | – | – | – |
| GLM 5.3 Z.ai | Q8_0 | won't run | 750.0 GB | – | – | – | – |
| Kimi Dev 72B Moonshot AI | Q6_K | won't run | 70.8 GB | – | – | – | – |
| Kimi K2 Moonshot AI | Q4_K_M | won't run | 581.2 GB | – | – | – | – |
| Kimi K2 Instruct 0905 Moonshot AI | Q4_K_M | won't run | 581.6 GB | – | – | – | – |
| Kimi K2 Thinking Moonshot AI | Q4_K_M | won't run | 581.6 GB | – | – | – | – |
| Kimi K2.5 Moonshot AI | Q4_K_M | won't run | 581.6 GB | – | – | – | – |
| MiniMax M2 MiniMax | Q4_K_M | won't run | 137.5 GB | – | – | – | – |
| MiniMax M2.1 MiniMax | Q4_K_M | won't run | 137.5 GB | – | – | – | – |
| MiniMax M2.5 MiniMax | MXFP4 | won't run | 123.9 GB | – | – | – | – |
| MiniMax M3 MiniMax | MXFP4 | won't run | 244.3 GB | – | – | – | – |
| Mistral Medium 3.5 128B Mistral AI | Q4_K_M | won't run | 81.6 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?
- gpt-oss 20B (MXFP4) fits fully in its memory at 32k context and generates at about 33 tok/s, an estimate from its memory bandwidth. 37 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 94 GB at 32k 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 the US?
- The best new price for the NVIDIA RTX PRO 2000 Blackwell in the US is $1,580 at eBay; used, it starts at $1,439 (asking price).
Speeds on this page are for 32k context. See the best hardware for local AI at every budget.