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 64k context it runs 32 of the 129 open models we track fully in memory; the most capable is gpt-oss 20B at MXFP4, at about 27 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 | 5 h ago | Buy |
| eBayNEW NVIDIA RTX PRO 2000 BLACKWELL 16GB Graphics Card | New | $1,580 | In stock | 5 h ago | Buy |
| eBayPNY VCNRTXPRO2000B-PB NVIDIA RTX PRO 2000 Blackwell 16GB GDDR7 GPU Graphic Card | New | $1,600+ $4 delivery | In stock | 5 h ago | Buy |
| eBayPNY VCNRTXPRO2000B-B NVIDIA RTX PRO 2000 Blackwell 16GB GDDR7 Workstation GPU | New | $2,013 | In stock | 5 h ago | Buy |
| eBayPNY VCNRTXPRO2000B-PB NVIDIA RTX PRO 2000 Blackwell 16GB GDDR7 Workstation GPU | New | $2,025 | In stock | 5 h ago | Buy |
| eBayPNY VCNRTXPRO2000B-PB NVIDIA RTX PRO 2000 Blackwell Graphic Card | New | $2,210 | In stock | 5 h ago | Buy |
| eBayPNY VCNRTXPRO2000B-PB NVIDIA RTX PRO 2000 Blackwell 16GB GDDR7 GPU Graphic Card | New | $2,233 | In stock | 5 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 @64k | Prefill | Good for |
|---|---|---|---|---|---|---|---|
| gpt-oss 20B OpenAI | MXFP4 | fits | 13.9 GB | 116k128k with q8 cache | 27tok/sest. | 516 tok/s | chat, coding, agents, long documents |
| Gemma 4 12B | Q8_0 | fits | 14.2 GB | 120k193k with q8 cache | 13tok/sest. | 265 tok/s | – |
| Qwen 3.5 9B Alibaba Qwen | Q8_0 | fits | 12.0 GB | 155k256k with q8 cache | 15tok/sest. | 325 tok/s | chat |
| DeepSeek R1 0528 Qwen3 8B DeepSeek | Q4_K_M | fits | 14.8 GB | 68k124k with q8 cache | 12tok/sest. | 215 tok/s | chat |
| Qwen3 8B Alibaba Qwen | Q4_K_M | fits | 14.8 GB | 40k | 12tok/sest. | 215 tok/s | chat |
| Qwen 3.5 4B Alibaba Qwen | Q8_0 | fits | 7.3 GB | 256k | 27tok/sest. | 563 tok/s | chat, coding, long documents |
| Ministral 3 8B Mistral AI | Q5_K_M | fits | 15.2 GB | 65k118k with q8 cache | 12tok/sest. | 212 tok/s | chat |
| Gemma 4 E4B | Q8_0 | fits | 10.5 GB | 128k | 18tok/sest. | 425 tok/s | chat |
| LFM2.5 8B-A1B Liquid AI | Q8_0 | fits | 12.5 GB | 119k125k with q8 cache | 25tok/sest. | 908 tok/s | chat, coding |
| Llama 3.1 8B Meta | Q6_K | fits | 15.2 GB | 65k118k with q8 cache | 12tok/sest. | 231 tok/s | chat |
| Qwen2.5 0.5B Alibaba Qwen | Q8_0 | fits | 2.3 GB | 32k | 134tok/sest. | 2013 tok/s | chat, coding, long documents |
| Qwen3.5 0.8B Alibaba Qwen | Q8_0 | fits | 2.6 GB | 256k | 109tok/sest. | 2283 tok/s | chat, coding, agents, long documents |
| Qwen3.5 2B Alibaba Qwen | Q5_K_M | fits | 3.2 GB | 256k | 79tok/sest. | 1244 tok/s | chat, coding, agents, long documents |
| LFM3 1.7B Liquid AI | Q8_0 | fits | 3.6 GB | 256k | 67tok/sest. | 1501 tok/s | chat, coding, agents, long documents |
| DeepSeek R1 Distill Qwen 1.5B DeepSeek | Q8_0 | fits | 4.6 GB | 128k | 47tok/sest. | 834 tok/s | chat, coding, long documents |
| MiniCPM5 2B openbmb | Q8_0 | fits | 6.2 GB | 128k | 33tok/sest. | 470 tok/s | chat, coding, agents |
| Spark X2.5 4B XHToken | mlx-4bit | fits | 6.2 GB | 288k482k with q8 cache | 32tok/sest. | 577 tok/s | chat, coding, agents, long documents |
| Mellum2.1 12B A2.5B Thinking JetBrains | Q8_0 | fits | 14.0 GB | 128k | 29tok/sest. | 841 tok/s | chat, coding, agents, long documents |
| Kimi VL A3B Thinking 2506 Moonshot AI | Q4_K_M | fits | 12.8 GB | 128k | 27tok/sest. | 579 tok/s | chat, coding, long documents |
| FrogNano 4B 2609 Microsoft | Q8_0 | fits | 7.4 GB | 256k | 26tok/sest. | 563 tok/s | chat, coding, agents, long documents |
| LFM2.5 2.6B Liquid AI | Q8_0 | fits | 7.5 GB | 128k | 26tok/sest. | 570 tok/s | chat, coding, agents, long documents |
| LFM2.5 VL 3B Liquid AI | Q8_0 | fits | 7.5 GB | 32k | 26tok/sest. | 536 tok/s | chat, coding, agents, long documents |
| Kimi VL A3B Moonshot AI | mlx-5bit | fits | 14.0 GB | 103k128k with q8 cache | 26tok/sest. | 454 tok/s | chat, coding, agents |
| granite vision 4.1 4b IBM | Q8_0 | fits | 9.4 GB | 128k | 20tok/sest. | 358 tok/s | chat, coding |
| granite 4.2 3b IBM | Q8_0 | fits | 9.7 GB | 128k | 19tok/sest. | 370 tok/s | chat, coding |
| Ministral 3 3B Instruct 2512 Mistral AI | Q8_0 | fits | 11.0 GB | 105k188k with q8 cache | 17tok/sest. | 354 tok/s | chat, coding |
| GLM 4 9B 0414 Z.ai | mlx-5bit | fits | 11.3 GB | 32k | 16tok/sest. | 190 tok/s | chat |
| DeepSeek R1 Distill Qwen 7B DeepSeek | Q8_0 | fits | 12.1 GB | 118k128k with q8 cache | 15tok/sest. | 270 tok/s | chat |
| GLM 4.6V Flash Z.ai | Q8_0 | fits | 12.9 GB | 119k128k with q8 cache | 14tok/sest. | 182 tok/s | chat |
| GLM 4.1V 9B Thinking Z.ai | mlx-8bit | fits | 14.5 GB | 64k | 12tok/sest. | 182 tok/s | – |
| DeepSeek R1 Distill Llama 8B DeepSeek | Q6_K | fits | 15.2 GB | 65k118k with q8 cache | 12tok/sest. | 231 tok/s | chat |
| Ministral 3 8B Reasoning 2512 Mistral AI | Q5_K_M | fits | 15.2 GB | 65k118k with q8 cache | 12tok/sest. | 212 tok/s | chat |
| Qwen 3.8 27B Alibaba Qwen | UD-Q4_K_M | offload | 20.5 GB | – | 5tok/sest. | 94 tok/s | – |
| Qwen 3.6 27B Alibaba Qwen | Q4_K_M | offload | 20.9 GB | – | 5tok/sest. | 93 tok/s | – |
| Gemma 4 31B | Q4_K_M | offload | 23.9 GB | – | 4tok/sest. | 79 tok/s | – |
| Mistral Small 4 119B Mistral AI | UD-Q2_K_XL | offload | 39.9 GB | – | 11tok/sest. | 142 tok/s | chat |
| Qwen 3.6 35B-A3B Alibaba Qwen | MXFP4 | offload | 22.6 GB | – | 19tok/sest. | 539 tok/s | chat, coding |
| Qwen 3.5 27B Alibaba Qwen | Q4_K_M | offload | 20.8 GB | – | 5tok/sest. | 94 tok/s | – |
| Gemma 4 26B-A4B | MXFP4 | offload | 17.9 GB | – | 19tok/sest. | 628 tok/s | chat, coding |
| Qwen 3.5 35B-A3B Alibaba Qwen | Q4_K_M | offload | 22.9 GB | – | 18tok/sest. | 535 tok/s | chat, coding |
| GLM 4.7 Flash Z.ai | Q4_K_M | offload | 21.4 GB | – | 10tok/sest. | 275 tok/s | chat |
| Devstral Small 2 24B Mistral AI | Q4_K_M | offload | 24.4 GB | 8k15k with q8 cache | 4tok/sest. | 85 tok/s | – |
| Nemotron 3.5 Lightning 30B-A3B NVIDIA | MXFP4 | offload | 23.1 GB | – | 22tok/sest. | 657 tok/s | chat, coding, long documents |
| Kimi Linear 48B-A3B Moonshot AI | Q4_K_M | offload | 29.6 GB | – | 21tok/sest. | 380 tok/s | chat, coding |
| Qwen3 Coder 30B-A3B Alibaba Qwen | Q4_K_M | offload | 24.4 GB | – | 7tok/sest. | 186 tok/s | chat |
| Qwen3 32B Alibaba Qwen | Q4_K_M | offload | 35.5 GB | – | 2tok/sest. | 37 tok/s | – |
| Gemma 3 27B | Q4_K_M | offload | 21.9 GB | – | 5tok/sest. | 102 tok/s | – |
| Mistral Small 3.2 24B Mistral AI | Q4_K_M | offload | 24.4 GB | 8k15k with q8 cache | 4tok/sest. | 85 tok/s | – |
| DeepSeek R1 Distill Qwen 32B DeepSeek | Q4_K_M | offload | 35.6 GB | – | 2tok/sest. | 46 tok/s | – |
| Ministral 3 14B Mistral AI | Q4_K_M | offload | 18.7 GB | 43k79k with q8 cache | 7tok/sest. | 138 tok/s | chat |
| Qwen3 14B Alibaba Qwen | Q4_K_M | offload | 19.5 GB | 38k40k with q8 cache | 6tok/sest. | 118 tok/s | chat |
| Phi-4 14B Microsoft | Q4_K_M | offload | 21.9 GB | 16k | 5tok/sest. | 111 tok/s | chat |
| Granite 4.2 8B IBM | Q4_K_M | offload | 16.2 GB | 58k107k with q8 cache | 10tok/sest. | 190 tok/s | chat |
| LFM2.5 24B A2B Liquid AI | Q4_K_M | offload | 20.4 GB | 4k8k with q8 cache | 10tok/sest. | 440 tok/s | chat, coding |
| Muse Glimmer 30B meta-models | Q4_K_M | offload | 18.1 GB | – | 7tok/sest. | 102 tok/s | – |
| Ministral 3 14B Reasoning 2512 Mistral AI | Q4_K_M | offload | 18.7 GB | 43k79k with q8 cache | 7tok/sest. | 138 tok/s | chat |
| DeepSeek R1 Distill Qwen 14B DeepSeek | Q4_K_M | offload | 21.4 GB | 32k60k with q8 cache | 5tok/sest. | 102 tok/s | chat |
| Devstral Small 2507 Mistral AI | Q4_K_M | offload | 24.4 GB | 8k15k with q8 cache | 4tok/sest. | 86 tok/s | – |
| Magistral Small 2506 Mistral AI | Q4_K_M | offload | 24.4 GB | 8k15k with q8 cache | 4tok/sest. | 86 tok/s | – |
| Magistral Small 2507 Mistral AI | Q4_K_M | offload | 24.4 GB | 8k15k with q8 cache | 4tok/sest. | 86 tok/s | – |
| Magistral Small 2509 Mistral AI | Q4_K_M | offload | 24.4 GB | 8k15k with q8 cache | 4tok/sest. | 85 tok/s | – |
| Qwen3.8 27B OBLITERATED OBLITERATUS | mlx-4bit | offload | 24.5 GB | – | 4tok/sest. | 86 tok/s | – |
| granite 4.2 30b IBM | Q4_K_M | offload | 33.9 GB | – | 2tok/sest. | 55 tok/s | – |
| DeepSeek V4 Flash DeepSeek | UD-Q2_K_XL | won't run | 96.6 GB | – | – | – | – |
| GLM 5.3 Flash Z.ai | UD-Q2_K_XL | won't run | 103.1 GB | – | – | – | – |
| Qwen 3.8 Flash Next Alibaba Qwen | UD-Q2_K_XL | won't run | 76.1 GB | – | – | – | – |
| MiniMax M2.7 MiniMax | UD-Q2_K_XL | won't run | 86.7 GB | – | – | – | – |
| Qwen 3.5 122B-A10B Alibaba Qwen | UD-Q2_K_XL | won't run | 41.7 GB | – | – | – | – |
| Nemotron 3 Super 120B-A12B NVIDIA | UD-Q2_K_XL | won't run | 52.6 GB | – | – | – | – |
| Qwen3 Coder Next Alibaba Qwen | Q4_K_M | won't run | 47.8 GB | – | – | – | – |
| gpt-oss 120B OpenAI | Q4_K_M | won't run | 61.8 GB | – | – | – | – |
| GLM 4.5 Air Z.ai | Q2_K | won't run | 54.8 GB | – | – | – | – |
| Qwen3 Next 80B-A3B Alibaba Qwen | Q4_K_M | won't run | 47.8 GB | – | – | – | – |
| Llama 3.3 70B Meta | Q4_K_M | won't run | 60.7 GB | – | – | – | – |
| DeepSeek R1 DeepSeek | Q4_K_M | won't run | 382.0 GB | – | – | – | – |
| DeepSeek R1 0528 DeepSeek | Q4_K_M | won't run | 382.5 GB | – | – | – | – |
| DeepSeek R1 Distill Llama 70B DeepSeek | Q4_K_M | won't run | 60.7 GB | – | – | – | – |
| DeepSeek R1 Zero DeepSeek | Q4_K_M | won't run | 382.0 GB | – | – | – | – |
| DeepSeek V3 DeepSeek | Q4_K_M | won't run | 382.0 GB | – | – | – | – |
| DeepSeek V3 0324 DeepSeek | Q4_K_M | won't run | 382.0 GB | – | – | – | – |
| DeepSeek V3.1 DeepSeek | Q4_K_M | won't run | 382.9 GB | – | – | – | – |
| DeepSeek V3.1 Terminus DeepSeek | Q4_K_M | won't run | 382.9 GB | – | – | – | – |
| DeepSeek V3.2 DeepSeek | Q4_K_M | won't run | 382.9 GB | – | – | – | – |
| DeepSeek V4 Flash 0731 DeepSeek | MXFP4 | won't run | 152.1 GB | – | – | – | – |
| DeepSeek V4 Flash Vision Exp DeepSeek | MXFP4 | won't run | 160.9 GB | – | – | – | – |
| Devstral 2 123B Instruct 2512 Mistral AI | Q4_K_M | won't run | 92.8 GB | – | – | – | – |
| GLM 4.5 Z.ai | Q4_K_M | won't run | 225.7 GB | – | – | – | – |
| GLM 4.5V Z.ai | Q4_K_M | won't run | 71.8 GB | – | – | – | – |
| GLM 4.6 Z.ai | Q4_K_M | won't run | 224.9 GB | – | – | – | – |
| GLM 4.6V Z.ai | Q4_K_M | won't run | 78.2 GB | – | – | – | – |
| GLM 4.7 Z.ai | Q4_K_M | won't run | 225.7 GB | – | – | – | – |
| GLM 5 Z.ai | MXFP4 | won't run | 388.9 GB | – | – | – | – |
| GLM 5.1 Z.ai | MXFP4 | won't run | 426.5 GB | – | – | – | – |
| GLM 5.2 Z.ai | UD-Q4_K_M | won't run | 440.4 GB | – | – | – | – |
| GLM 5.3 Z.ai | Q8_0 | won't run | 752.9 GB | – | – | – | – |
| Kimi Dev 72B Moonshot AI | Q6_K | won't run | 81.0 GB | – | – | – | – |
| Kimi K2 Moonshot AI | Q4_K_M | won't run | 583.5 GB | – | – | – | – |
| Kimi K2 Instruct 0905 Moonshot AI | Q4_K_M | won't run | 583.9 GB | – | – | – | – |
| Kimi K2 Thinking Moonshot AI | Q4_K_M | won't run | 583.9 GB | – | – | – | – |
| Kimi K2.5 Moonshot AI | Q4_K_M | won't run | 583.9 GB | – | – | – | – |
| MiniMax M2 MiniMax | Q4_K_M | won't run | 145.4 GB | – | – | – | – |
| MiniMax M2.1 MiniMax | Q4_K_M | won't run | 145.4 GB | – | – | – | – |
| MiniMax M2.5 MiniMax | MXFP4 | won't run | 131.8 GB | – | – | – | – |
| MiniMax M3 MiniMax | MXFP4 | won't run | 248.3 GB | – | – | – | – |
| Mistral Medium 3.5 128B Mistral AI | Q4_K_M | won't run | 92.8 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 64k context and generates at about 27 tok/s, an estimate from its memory bandwidth. 32 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 97 GB at 64k 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 64k context. See the best hardware for local AI at every budget.