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 44 of the 129 open models we track fully in memory; the most capable is Devstral Small 2 24B at Q4_K_M, at about 11 tok/s (estimated). The best new price for the NVIDIA RTX PRO 2000 Blackwell in Canada is $2,241 at Amazon.
Updated . Speeds are estimates; how they are worked out.
Prices
Every retailer we track in Canada, cheapest first. Buy links may earn us a commission.
| Retailer | Condition | Price | Stock | Checked | Buy |
|---|---|---|---|---|---|
| AmazonNVIDIA RTX PRO 2000 Blackwell Professional Workstation GPU | New | $2,241 | In stock | 4 h ago | Buy |
| AmazonPNY VCNRTXPRO2000B-B NVIDIA RTX Pro 2000 Black Well 16GB GDDR7 128B Graphics… | New | $2,559 | In stock | 4 h ago | Buy |
| eBayNVIDIA RTX PRO 2000 Blackwell Professional Workstation Graphics Card GPU | New | $3,400 | In stock | 2 h ago | Buy |
| AmazonPNY NVIDIA RTX PRO 2000 Blackwell | New | $3,459 | In stock | 4 h ago | Buy |
| AmazonPNY Quadro RTX PRO 2000 Blackwell 16GB GDDR7 4xmDP | New | $3,494 | In stock | 4 h ago | Buy |
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 @8k | Prefill | Good for |
|---|---|---|---|---|---|---|---|
| Devstral Small 2 24B Mistral AI | Q4_K_M | fits | 15.3 GB | 8k15k with q8 cache | 11tok/sest. | 151 tok/s | – |
| gpt-oss 20B OpenAI | MXFP4 | fits | 12.2 GB | 116k128k with q8 cache | 41tok/sest. | 832 tok/s | chat, coding, agents, long documents |
| Gemma 4 12B | Q8_0 | fits | 13.0 GB | 120k193k with q8 cache | 14tok/sest. | 305 tok/s | – |
| Qwen 3.5 9B Alibaba Qwen | Q8_0 | fits | 9.9 GB | 155k256k with q8 cache | 18tok/sest. | 386 tok/s | chat |
| Mistral Small 3.2 24B Mistral AI | Q4_K_M | fits | 15.3 GB | 8k15k with q8 cache | 11tok/sest. | 151 tok/s | – |
| Qwen3 14B Alibaba Qwen | Q6_K | fits | 13.3 GB | 21k38k with q8 cache | 13tok/sest. | 233 tok/s | – |
| Ministral 3 14B Mistral AI | Q8_0 | fits | 15.4 GB | 8k14k with q8 cache | 11tok/sest. | 251 tok/s | – |
| DeepSeek R1 0528 Qwen3 8B DeepSeek | Q8_0 | fits | 10.0 GB | 44k81k with q8 cache | 18tok/sest. | 408 tok/s | chat |
| Qwen3 8B Alibaba Qwen | Q8_0 | fits | 10.0 GB | 40k | 18tok/sest. | 408 tok/s | chat |
| Phi-4 14B Microsoft | Q6_K | fits | 13.5 GB | 16k | 13tok/sest. | 235 tok/s | – |
| Qwen 3.5 4B Alibaba Qwen | Q8_0 | fits | 5.2 GB | 256k | 37tok/sest. | 778 tok/s | chat, coding, long documents |
| Ministral 3 8B Mistral AI | Q8_0 | fits | 10.2 GB | 45k82k with q8 cache | 18tok/sest. | 381 tok/s | chat |
| Granite 4.2 8B IBM | Q8_0 | fits | 10.7 GB | 36k67k with q8 cache | 17tok/sest. | 378 tok/s | chat |
| Gemma 4 E4B | Q8_0 | fits | 8.6 GB | 128k | 21tok/sest. | 468 tok/s | chat |
| LFM2.5 8B-A1B Liquid AI | Q8_0 | fits | 9.5 GB | 119k125k with q8 cache | 73tok/sest. | 2732 tok/s | chat, coding |
| Llama 3.1 8B Meta | Q6_K | fits | 7.9 GB | 65k118k with q8 cache | 23tok/sest. | 421 tok/s | chat |
| Qwen2.5 0.5B Alibaba Qwen | Q8_0 | fits | 1.3 GB | 32k | 283tok/sest. | 5717 tok/s | chat, coding, long documents |
| Qwen3.5 0.8B Alibaba Qwen | Q8_0 | fits | 1.6 GB | 256k | 192tok/sest. | 3934 tok/s | chat, coding, agents, long documents |
| Qwen3.5 2B Alibaba Qwen | Q5_K_M | fits | 2.2 GB | 256k | 115tok/sest. | 1614 tok/s | chat, coding, agents, long documents |
| LFM3 1.7B Liquid AI | Q8_0 | fits | 2.6 GB | 256k | 91tok/sest. | 2073 tok/s | chat, coding, agents, long documents |
| DeepSeek R1 Distill Qwen 1.5B DeepSeek | Q8_0 | fits | 2.7 GB | 128k | 84tok/sest. | 1799 tok/s | chat, coding, long documents |
| MiniCPM5 2B openbmb | Q8_0 | fits | 3.6 GB | 128k | 59tok/sest. | 1190 tok/s | chat, coding, agents |
| LFM2.5 2.6B Liquid AI | Q8_0 | fits | 3.9 GB | 128k | 53tok/sest. | 1197 tok/s | chat, coding, agents, long documents |
| LFM2.5 VL 3B Liquid AI | Q8_0 | fits | 3.9 GB | 32k | 53tok/sest. | 1057 tok/s | chat, coding, agents, long documents |
| Spark X2.5 4B XHToken | mlx-4bit | fits | 3.9 GB | 288k482k with q8 cache | 53tok/sest. | 846 tok/s | chat, coding, agents, long documents |
| Kimi VL A3B Thinking 2506 Moonshot AI | Q4_K_M | fits | 10.8 GB | 128k | 49tok/sest. | 1110 tok/s | chat, coding, long documents |
| granite vision 4.1 4b IBM | Q8_0 | fits | 4.7 GB | 128k | 42tok/sest. | 792 tok/s | chat, coding |
| Ministral 3 3B Instruct 2512 Mistral AI | Q8_0 | fits | 5.0 GB | 105k188k with q8 cache | 40tok/sest. | 812 tok/s | chat, coding |
| granite 4.2 3b IBM | Q8_0 | fits | 5.0 GB | 128k | 39tok/sest. | 852 tok/s | chat, coding |
| Kimi VL A3B Moonshot AI | mlx-6bit | fits | 13.9 GB | 51k84k with q8 cache | 39tok/sest. | 1041 tok/s | chat, coding, agents |
| Mellum2.1 12B A2.5B Thinking JetBrains | Q8_0 | fits | 12.9 GB | 128k | 38tok/sest. | 1316 tok/s | chat, coding, agents, long documents |
| FrogNano 4B 2609 Microsoft | Q8_0 | fits | 5.3 GB | 256k | 36tok/sest. | 778 tok/s | chat, coding, agents, long documents |
| DeepSeek R1 Distill Qwen 7B DeepSeek | Q8_0 | fits | 8.7 GB | 118k128k with q8 cache | 21tok/sest. | 454 tok/s | chat |
| GLM 4 9B 0414 Z.ai | mlx-5bit | fits | 8.7 GB | 32k | 21tok/sest. | 357 tok/s | chat |
| DeepSeek R1 Distill Llama 8B DeepSeek | Q8_0 | fits | 9.7 GB | 51k93k with q8 cache | 19tok/sest. | 421 tok/s | chat |
| Ministral 3 8B Reasoning 2512 Mistral AI | Q8_0 | fits | 10.2 GB | 45k82k with q8 cache | 18tok/sest. | 381 tok/s | chat |
| GLM 4.6V Flash Z.ai | Q8_0 | fits | 10.4 GB | 119k128k with q8 cache | 17tok/sest. | 329 tok/s | chat |
| GLM 4.1V 9B Thinking Z.ai | mlx-8bit | fits | 12.0 GB | 64k | 15tok/sest. | 329 tok/s | – |
| DeepSeek R1 Distill Qwen 14B DeepSeek | Q6_K | fits | 13.5 GB | 17k32k with q8 cache | 13tok/sest. | 228 tok/s | – |
| Devstral Small 2507 Mistral AI | Q4_K_M | fits | 15.3 GB | 8k15k with q8 cache | 11tok/sest. | 154 tok/s | – |
| Magistral Small 2506 Mistral AI | Q4_K_M | fits | 15.3 GB | 8k15k with q8 cache | 11tok/sest. | 154 tok/s | – |
| Magistral Small 2507 Mistral AI | Q4_K_M | fits | 15.3 GB | 8k15k with q8 cache | 11tok/sest. | 154 tok/s | – |
| Magistral Small 2509 Mistral AI | Q4_K_M | fits | 15.3 GB | 8k15k with q8 cache | 11tok/sest. | 151 tok/s | – |
| Ministral 3 14B Reasoning 2512 Mistral AI | Q8_0 | fits | 15.4 GB | 8k14k with q8 cache | 11tok/sest. | 251 tok/s | – |
| Qwen 3.8 27B Alibaba Qwen | UD-Q4_K_M | offload | 16.7 GB | – | 9tok/sest. | 127 tok/s | – |
| Qwen 3.5 122B-A10B Alibaba Qwen | UD-Q2_K_XL | offload | 40.0 GB | – | 12tok/sest. | 221 tok/s | – |
| Qwen 3.6 27B Alibaba Qwen | Q4_K_M | offload | 17.0 GB | – | 8tok/sest. | 126 tok/s | – |
| Gemma 4 31B | Q4_K_M | offload | 19.2 GB | – | 6tok/sest. | 102 tok/s | – |
| Mistral Small 4 119B Mistral AI | UD-Q2_K_XL | offload | 38.4 GB | – | 17tok/sest. | 294 tok/s | chat |
| Qwen 3.6 35B-A3B Alibaba Qwen | MXFP4 | offload | 21.2 GB | – | 32tok/sest. | 954 tok/s | chat, coding |
| Qwen 3.5 27B Alibaba Qwen | Q4_K_M | offload | 17.0 GB | – | 8tok/sest. | 126 tok/s | – |
| Gemma 4 26B-A4B | MXFP4 | offload | 16.5 GB | – | 31tok/sest. | 838 tok/s | chat, coding |
| Qwen 3.5 35B-A3B Alibaba Qwen | Q4_K_M | offload | 21.5 GB | – | 31tok/sest. | 947 tok/s | chat, coding |
| GLM 4.7 Flash Z.ai | Q4_K_M | offload | 18.2 GB | – | 22tok/sest. | 581 tok/s | chat |
| Nemotron 3.5 Lightning 30B-A3B NVIDIA | MXFP4 | offload | 22.5 GB | – | 27tok/sest. | 964 tok/s | chat, coding, long documents |
| Qwen3 Coder 30B-A3B Alibaba Qwen | Q4_K_M | offload | 18.8 GB | – | 28tok/sest. | 738 tok/s | chat |
| Kimi Linear 48B-A3B Moonshot AI | Q4_K_M | offload | 28.8 GB | – | 26tok/sest. | 778 tok/s | chat, coding |
| Qwen3 32B Alibaba Qwen | Q4_K_M | offload | 21.2 GB | – | 5tok/sest. | 86 tok/s | – |
| Gemma 3 27B | Q4_K_M | offload | 17.2 GB | – | 8tok/sest. | 127 tok/s | – |
| DeepSeek R1 Distill Qwen 32B DeepSeek | Q4_K_M | offload | 21.2 GB | – | 5tok/sest. | 90 tok/s | – |
| LFM2.5 24B A2B Liquid AI | Q4_K_M | offload | 15.7 GB | 4k8k with q8 cache | 53tok/sest. | 1419 tok/s | chat, coding |
| Muse Glimmer 30B meta-models | Q4_K_M | offload | 17.0 GB | – | 8tok/sest. | 117 tok/s | – |
| granite 4.2 30b IBM | Q4_K_M | offload | 19.5 GB | – | 6tok/sest. | 106 tok/s | – |
| Qwen3.8 27B OBLITERATED OBLITERATUS | mlx-4bit | offload | 20.6 GB | – | 5tok/sest. | 113 tok/s | – |
| DeepSeek V4 Flash DeepSeek | UD-Q2_K_XL | won't run | 91.6 GB | – | – | – | – |
| GLM 5.3 Flash Z.ai | UD-Q2_K_XL | won't run | 102.2 GB | – | – | – | – |
| Qwen 3.8 Flash Next Alibaba Qwen | UD-Q2_K_XL | won't run | 74.5 GB | – | – | – | – |
| MiniMax M2.7 MiniMax | UD-Q2_K_XL | won't run | 72.8 GB | – | – | – | – |
| Nemotron 3 Super 120B-A12B NVIDIA | UD-Q2_K_XL | won't run | 51.9 GB | – | – | – | – |
| Qwen3 Coder Next Alibaba Qwen | Q4_K_M | won't run | 46.2 GB | – | – | – | – |
| gpt-oss 120B OpenAI | Q4_K_M | won't run | 59.5 GB | – | – | – | – |
| GLM 4.5 Air Z.ai | Q2_K | won't run | 44.4 GB | – | – | – | – |
| Qwen3 Next 80B-A3B Alibaba Qwen | Q4_K_M | won't run | 46.2 GB | – | – | – | – |
| Llama 3.3 70B Meta | Q4_K_M | won't run | 42.8 GB | – | – | – | – |
| DeepSeek R1 DeepSeek | Q4_K_M | won't run | 377.9 GB | – | – | – | – |
| DeepSeek R1 0528 DeepSeek | Q4_K_M | won't run | 378.4 GB | – | – | – | – |
| DeepSeek R1 Distill Llama 70B DeepSeek | Q4_K_M | won't run | 42.8 GB | – | – | – | – |
| DeepSeek R1 Zero DeepSeek | Q4_K_M | won't run | 377.9 GB | – | – | – | – |
| DeepSeek V3 DeepSeek | Q4_K_M | won't run | 377.9 GB | – | – | – | – |
| DeepSeek V3 0324 DeepSeek | Q4_K_M | won't run | 377.9 GB | – | – | – | – |
| DeepSeek V3.1 DeepSeek | Q4_K_M | won't run | 378.8 GB | – | – | – | – |
| DeepSeek V3.1 Terminus DeepSeek | Q4_K_M | won't run | 378.8 GB | – | – | – | – |
| DeepSeek V3.2 DeepSeek | Q4_K_M | won't run | 378.8 GB | – | – | – | – |
| DeepSeek V4 Flash 0731 DeepSeek | MXFP4 | won't run | 147.1 GB | – | – | – | – |
| DeepSeek V4 Flash Vision Exp DeepSeek | MXFP4 | won't run | 155.8 GB | – | – | – | – |
| Devstral 2 123B Instruct 2512 Mistral AI | Q4_K_M | won't run | 73.3 GB | – | – | – | – |
| GLM 4.5 Z.ai | Q4_K_M | won't run | 205.2 GB | – | – | – | – |
| GLM 4.5V Z.ai | Q4_K_M | won't run | 61.4 GB | – | – | – | – |
| GLM 4.6 Z.ai | Q4_K_M | won't run | 204.4 GB | – | – | – | – |
| GLM 4.6V Z.ai | Q4_K_M | won't run | 67.8 GB | – | – | – | – |
| GLM 4.7 Z.ai | Q4_K_M | won't run | 205.2 GB | – | – | – | – |
| GLM 5 Z.ai | MXFP4 | won't run | 383.8 GB | – | – | – | – |
| GLM 5.1 Z.ai | MXFP4 | won't run | 421.4 GB | – | – | – | – |
| GLM 5.2 Z.ai | UD-Q4_K_M | won't run | 435.3 GB | – | – | – | – |
| GLM 5.3 Z.ai | Q8_0 | won't run | 747.8 GB | – | – | – | – |
| Kimi Dev 72B Moonshot AI | Q6_K | won't run | 63.2 GB | – | – | – | – |
| Kimi K2 Moonshot AI | Q4_K_M | won't run | 579.4 GB | – | – | – | – |
| Kimi K2 Instruct 0905 Moonshot AI | Q4_K_M | won't run | 579.9 GB | – | – | – | – |
| Kimi K2 Thinking Moonshot AI | Q4_K_M | won't run | 579.9 GB | – | – | – | – |
| Kimi K2.5 Moonshot AI | Q4_K_M | won't run | 579.9 GB | – | – | – | – |
| MiniMax M2 MiniMax | Q4_K_M | won't run | 131.5 GB | – | – | – | – |
| MiniMax M2.1 MiniMax | Q4_K_M | won't run | 131.5 GB | – | – | – | – |
| MiniMax M2.5 MiniMax | MXFP4 | won't run | 118.0 GB | – | – | – | – |
| MiniMax M3 MiniMax | MXFP4 | won't run | 241.4 GB | – | – | – | – |
| Mistral Medium 3.5 128B Mistral AI | Q4_K_M | won't run | 73.3 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?
- Devstral Small 2 24B (Q4_K_M) fits fully in its memory at 8k context and generates at about 11 tok/s, an estimate from its memory bandwidth. 44 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 Canada?
- The best new price for the NVIDIA RTX PRO 2000 Blackwell in Canada is $2,241 at Amazon.
Speeds on this page are for 8k context. See the best hardware for local AI at every budget.