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 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 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 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 |
|---|---|---|---|---|---|---|---|
| Qwen 3.8 27B Alibaba Qwen | Q8_0 | fits | 30.5 GB | 36k64k with q8 cache | 6tok/sest. | 124 tok/s | – |
| Qwen 3.6 27B Alibaba Qwen | Q8_0 | fits | 30.1 GB | 42k74k with q8 cache | 6tok/sest. | 124 tok/s | – |
| Gemma 4 31B | Q6_K | fits | 28.0 GB | 64k122k with q8 cache | 6tok/sest. | 110 tok/s | – |
| Qwen 3.6 35B-A3B Alibaba Qwen | Q6_K | fits | 30.8 GB | 32k50k with q8 cache | 32tok/sest. | 882 tok/s | chat, coding, agents, long documents |
| Qwen 3.5 27B Alibaba Qwen | Q8_0 | fits | 30.1 GB | 42k74k with q8 cache | 6tok/sest. | 124 tok/s | – |
| Gemma 4 26B-A4B | Q8_0 | fits | 27.1 GB | 176k256k with q8 cache | 21tok/sest. | 785 tok/s | chat, coding, long documents |
| Qwen 3.5 35B-A3B Alibaba Qwen | Q6_K | fits | 28.8 GB | 109k170k with q8 cache | 34tok/sest. | 882 tok/s | chat, coding, agents, long documents |
| GLM 4.7 Flash Z.ai | Q6_K | fits | 25.9 GB | 116k198k with q8 cache | 18tok/sest. | 461 tok/s | chat |
| Devstral Small 2 24B Mistral AI | Q8_0 | fits | 29.6 GB | 39k71k with q8 cache | 6tok/sest. | 130 tok/s | – |
| Nemotron 3.5 Lightning 30B-A3B NVIDIA | UD-Q5_K_M | fits | 29.7 GB | 129k168k with q8 cache | 34tok/sest. | 1007 tok/s | chat, coding, agents, long documents |
| Kimi Linear 48B-A3B Moonshot AI | Q4_K_M | fits | 29.6 GB | 122k167k with q8 cache | 50tok/sest. | 761 tok/s | chat, coding, long documents |
| gpt-oss 20B OpenAI | MXFP4 | fits | 13.3 GB | 128k | 33tok/sest. | 659 tok/s | chat, coding, agents, long documents |
| Qwen3 Coder 30B-A3B Alibaba Qwen | Q6_K | fits | 27.7 GB | 63k113k with q8 cache | 19tok/sest. | 406 tok/s | chat |
| Qwen3 32B Alibaba Qwen | Q4_K_M | fits | 27.7 GB | 40k | 6tok/sest. | 77 tok/s | – |
| Gemma 4 12B | Q8_0 | fits | 13.9 GB | 256k | 13tok/sest. | 287 tok/s | – |
| Qwen 3.5 9B Alibaba Qwen | Q8_0 | fits | 11.2 GB | 256k | 17tok/sest. | 357 tok/s | chat |
| Gemma 3 27B | Q6_K | fits | 24.8 GB | 102k128k with q8 cache | 7tok/sest. | 131 tok/s | – |
| DeepSeek R1 Distill Qwen 32B DeepSeek | Q4_K_M | fits | 27.8 GB | 43k80k with q8 cache | 6tok/sest. | 88 tok/s | – |
| Mistral Small 3.2 24B Mistral AI | Q8_0 | fits | 29.6 GB | 39k71k with q8 cache | 6tok/sest. | 130 tok/s | – |
| Ministral 3 14B Mistral AI | Q8_0 | fits | 19.7 GB | 100k183k with q8 cache | 9tok/sest. | 198 tok/s | – |
| Qwen3 14B Alibaba Qwen | Q8_0 | fits | 20.9 GB | 40k | 9tok/sest. | 178 tok/s | – |
| DeepSeek R1 0528 Qwen3 8B DeepSeek | Q8_0 | fits | 13.9 GB | 128k | 13tok/sest. | 294 tok/s | chat |
| Qwen3 8B Alibaba Qwen | Q8_0 | fits | 13.9 GB | 40k | 13tok/sest. | 294 tok/s | chat |
| Phi-4 14B Microsoft | Q8_0 | fits | 22.0 GB | 16k | 8tok/sest. | 179 tok/s | – |
| Qwen 3.5 4B Alibaba Qwen | Q8_0 | fits | 6.5 GB | 256k | 32tok/sest. | 668 tok/s | chat, coding, long documents |
| Ministral 3 8B Mistral AI | Q8_0 | fits | 13.9 GB | 153k256k with q8 cache | 13tok/sest. | 284 tok/s | chat |
| Granite 4.2 8B IBM | Q8_0 | fits | 15.0 GB | 128k | 12tok/sest. | 271 tok/s | chat |
| Gemma 4 E4B | Q8_0 | fits | 9.8 GB | 128k | 20tok/sest. | 449 tok/s | chat |
| LFM2.5 8B-A1B Liquid AI | Q8_0 | fits | 11.2 GB | 125k | 40tok/sest. | 1468 tok/s | chat, coding |
| Llama 3.1 8B Meta | Q6_K | fits | 11.4 GB | 128k | 16tok/sest. | 311 tok/s | chat |
| Qwen2.5 0.5B Alibaba Qwen | Q8_0 | fits | 2.2 GB | 32k | 191tok/sest. | 3197 tok/s | chat, coding, long documents |
| Qwen3.5 0.8B Alibaba Qwen | Q8_0 | fits | 2.4 GB | 256k | 145tok/sest. | 3003 tok/s | chat, coding, agents, long documents |
| Qwen3.5 2B Alibaba Qwen | Q5_K_M | fits | 3.0 GB | 256k | 96tok/sest. | 1432 tok/s | chat, coding, agents, long documents |
| LFM3 1.7B Liquid AI | Q8_0 | fits | 3.4 GB | 256k | 79tok/sest. | 1782 tok/s | chat, coding, agents, long documents |
| DeepSeek R1 Distill Qwen 1.5B DeepSeek | Q8_0 | fits | 3.9 GB | 128k | 63tok/sest. | 1203 tok/s | chat, coding, long documents |
| MiniCPM5 2B openbmb | Q8_0 | fits | 5.1 GB | 128k | 44tok/sest. | 718 tok/s | chat, coding, agents |
| Spark X2.5 4B XHToken | mlx-4bit | fits | 5.3 GB | 653k1024k with q8 cache | 42tok/sest. | 705 tok/s | chat, coding, agents, long documents |
| LFM2.5 2.6B Liquid AI | Q8_0 | fits | 5.8 GB | 128k | 37tok/sest. | 814 tok/s | chat, coding, agents, long documents |
| LFM2.5 VL 3B Liquid AI | Q8_0 | fits | 5.8 GB | 32k | 37tok/sest. | 746 tok/s | chat, coding, agents, long documents |
| Mellum2.1 12B A2.5B Thinking JetBrains | Q8_0 | fits | 13.8 GB | 128k | 34tok/sest. | 1060 tok/s | chat, coding, agents, long documents |
| FrogNano 4B 2609 Microsoft | Q8_0 | fits | 6.6 GB | 256k | 31tok/sest. | 668 tok/s | chat, coding, agents, long documents |
| granite vision 4.1 4b IBM | Q8_0 | fits | 7.2 GB | 128k | 28tok/sest. | 521 tok/s | chat, coding |
| granite 4.2 3b IBM | Q8_0 | fits | 7.4 GB | 128k | 27tok/sest. | 546 tok/s | chat, coding |
| Kimi VL A3B Thinking 2506 Moonshot AI | Q8_0 | fits | 18.0 GB | 128k | 26tok/sest. | 796 tok/s | chat, coding |
| Kimi VL A3B Moonshot AI | mlx-8bit | fits | 18.8 GB | 128k | 25tok/sest. | 670 tok/s | chat, coding |
| Ministral 3 3B Instruct 2512 Mistral AI | Q8_0 | fits | 7.9 GB | 244k256k with q8 cache | 25tok/sest. | 522 tok/s | chat, coding |
| LFM2.5 24B A2B Liquid AI | Q8_0 | fits | 27.5 GB | 70k125k with q8 cache | 22tok/sest. | 818 tok/s | chat, coding |
| GLM 4 9B 0414 Z.ai | mlx-5bit | fits | 10.2 GB | 32k | 19tok/sest. | 259 tok/s | chat |
| DeepSeek R1 Distill Qwen 7B DeepSeek | Q8_0 | fits | 10.6 GB | 128k | 18tok/sest. | 351 tok/s | chat |
| GLM 4.6V Flash Z.ai | Q8_0 | fits | 11.9 GB | 128k | 16tok/sest. | 245 tok/s | chat |
| DeepSeek R1 Distill Llama 8B DeepSeek | Q8_0 | fits | 13.2 GB | 128k | 14tok/sest. | 311 tok/s | chat |
| GLM 4.1V 9B Thinking Z.ai | mlx-8bit | fits | 13.5 GB | 64k | 14tok/sest. | 245 tok/s | – |
| Ministral 3 8B Reasoning 2512 Mistral AI | Q8_0 | fits | 13.9 GB | 153k256k with q8 cache | 13tok/sest. | 284 tok/s | chat |
| Ministral 3 14B Reasoning 2512 Mistral AI | Q8_0 | fits | 19.7 GB | 100k183k with q8 cache | 9tok/sest. | 198 tok/s | – |
| DeepSeek R1 Distill Qwen 14B DeepSeek | Q8_0 | fits | 21.9 GB | 77k128k with q8 cache | 8tok/sest. | 168 tok/s | – |
| Qwen3.8 27B OBLITERATED OBLITERATUS | mlx-4bit | fits | 22.7 GB | 150k256k with q8 cache | 8tok/sest. | 124 tok/s | – |
| Muse Glimmer 30B meta-models | Q6_K | fits | 23.6 GB | 128k | 8tok/sest. | 119 tok/s | – |
| granite 4.2 30b IBM | Q5_K_M | fits | 28.9 GB | 39k72k with q8 cache | 6tok/sest. | 101 tok/s | – |
| Devstral Small 2507 Mistral AI | Q8_0 | fits | 29.6 GB | 39k71k with q8 cache | 6tok/sest. | 132 tok/s | – |
| Magistral Small 2506 Mistral AI | Q8_0 | fits | 29.6 GB | 39k40k with q8 cache | 6tok/sest. | 132 tok/s | – |
| Magistral Small 2507 Mistral AI | Q8_0 | fits | 29.6 GB | 39k40k with q8 cache | 6tok/sest. | 132 tok/s | – |
| Magistral Small 2509 Mistral AI | Q8_0 | fits | 29.6 GB | 39k71k with q8 cache | 6tok/sest. | 130 tok/s | – |
| Qwen 3.5 122B-A10B Alibaba Qwen | UD-Q2_K_XL | offload | 41.1 GB | – | 16tok/sest. | 245 tok/s | chat |
| Qwen3 Coder Next Alibaba Qwen | Q4_K_M | offload | 47.3 GB | – | 18tok/sest. | 548 tok/s | chat |
| Nemotron 3 Super 120B-A12B NVIDIA | UD-Q2_K_XL | offload | 52.6 GB | – | 9tok/sest. | 218 tok/s | – |
| Mistral Small 4 119B Mistral AI | UD-Q2_K_XL | offload | 39.4 GB | – | 22tok/sest. | 279 tok/s | chat, coding |
| GLM 4.5 Air Z.ai | Q2_K | offload | 49.2 GB | – | 5tok/sest. | 92 tok/s | – |
| Qwen3 Next 80B-A3B Alibaba Qwen | Q4_K_M | offload | 47.3 GB | – | 22tok/sest. | 652 tok/s | chat, coding |
| Llama 3.3 70B Meta | Q4_K_M | offload | 50.9 GB | – | 2tok/sest. | 32 tok/s | – |
| DeepSeek R1 Distill Llama 70B DeepSeek | Q4_K_M | offload | 50.9 GB | – | 2tok/sest. | 32 tok/s | – |
| DeepSeek V4 Flash DeepSeek | UD-Q2_K_XL | won't run | 94.2 GB | – | – | – | – |
| GLM 5.3 Flash Z.ai | UD-Q2_K_XL | won't run | 103.0 GB | – | – | – | – |
| Qwen 3.8 Flash Next Alibaba Qwen | UD-Q2_K_XL | won't run | 75.6 GB | – | – | – | – |
| MiniMax M2.7 MiniMax | UD-Q2_K_XL | won't run | 79.2 GB | – | – | – | – |
| gpt-oss 120B OpenAI | Q4_K_M | won't run | 60.9 GB | – | – | – | – |
| DeepSeek R1 DeepSeek | Q4_K_M | won't run | 380.1 GB | – | – | – | – |
| DeepSeek R1 0528 DeepSeek | Q4_K_M | won't run | 380.6 GB | – | – | – | – |
| DeepSeek R1 Zero DeepSeek | Q4_K_M | won't run | 380.1 GB | – | – | – | – |
| DeepSeek V3 DeepSeek | Q4_K_M | won't run | 380.1 GB | – | – | – | – |
| DeepSeek V3 0324 DeepSeek | Q4_K_M | won't run | 380.1 GB | – | – | – | – |
| DeepSeek V3.1 DeepSeek | Q4_K_M | won't run | 381.0 GB | – | – | – | – |
| DeepSeek V3.1 Terminus DeepSeek | Q4_K_M | won't run | 381.0 GB | – | – | – | – |
| DeepSeek V3.2 DeepSeek | Q4_K_M | won't run | 381.0 GB | – | – | – | – |
| DeepSeek V4 Flash 0731 DeepSeek | MXFP4 | won't run | 149.6 GB | – | – | – | – |
| DeepSeek V4 Flash Vision Exp DeepSeek | MXFP4 | won't run | 158.4 GB | – | – | – | – |
| Devstral 2 123B Instruct 2512 Mistral AI | Q4_K_M | won't run | 82.0 GB | – | – | – | – |
| GLM 4.5 Z.ai | Q4_K_M | won't run | 214.4 GB | – | – | – | – |
| GLM 4.5V Z.ai | Q4_K_M | won't run | 66.3 GB | – | – | – | – |
| GLM 4.6 Z.ai | Q4_K_M | won't run | 213.6 GB | – | – | – | – |
| GLM 4.6V Z.ai | Q4_K_M | won't run | 72.6 GB | – | – | – | – |
| GLM 4.7 Z.ai | Q4_K_M | won't run | 214.4 GB | – | – | – | – |
| GLM 5 Z.ai | MXFP4 | won't run | 386.4 GB | – | – | – | – |
| GLM 5.1 Z.ai | MXFP4 | won't run | 424.0 GB | – | – | – | – |
| GLM 5.2 Z.ai | UD-Q4_K_M | won't run | 437.9 GB | – | – | – | – |
| GLM 5.3 Z.ai | Q8_0 | won't run | 750.4 GB | – | – | – | – |
| Kimi Dev 72B Moonshot AI | Q6_K | won't run | 71.2 GB | – | – | – | – |
| Kimi K2 Moonshot AI | Q4_K_M | won't run | 581.6 GB | – | – | – | – |
| Kimi K2 Instruct 0905 Moonshot AI | Q4_K_M | won't run | 582.0 GB | – | – | – | – |
| Kimi K2 Thinking Moonshot AI | Q4_K_M | won't run | 582.0 GB | – | – | – | – |
| Kimi K2.5 Moonshot AI | Q4_K_M | won't run | 582.0 GB | – | – | – | – |
| MiniMax M2 MiniMax | Q4_K_M | won't run | 137.9 GB | – | – | – | – |
| MiniMax M2.1 MiniMax | Q4_K_M | won't run | 137.9 GB | – | – | – | – |
| MiniMax M2.5 MiniMax | MXFP4 | won't run | 124.3 GB | – | – | – | – |
| MiniMax M3 MiniMax | MXFP4 | won't run | 244.7 GB | – | – | – | – |
| Mistral Medium 3.5 128B Mistral AI | Q4_K_M | won't run | 82.0 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 32k 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 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.