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 128k context it runs 49 of the 129 open models we track fully in memory; the most capable is Qwen 3.8 27B at UD-Q6_K, at about 6 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 | 5 h ago | Buy |
| AmazonPNY VCNRTXPRO2000B-B NVIDIA RTX Pro 2000 Black Well 16GB GDDR7 128B Graphics… | New | $2,559 | In stock | 5 h ago | Buy |
| eBayNVIDIA RTX PRO 2000 Blackwell Professional Workstation Graphics Card GPU | New | $3,400 | In stock | 5 min ago | Buy |
| AmazonPNY NVIDIA RTX PRO 2000 Blackwell | New | $3,459 | In stock | 5 h ago | Buy |
| AmazonPNY Quadro RTX PRO 2000 Blackwell 16GB GDDR7 4xmDP | New | $3,494 | In stock | 5 h 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 @128k | Prefill | Good for |
|---|---|---|---|---|---|---|---|
| Qwen 3.8 27B Alibaba Qwen | UD-Q6_K | fits | 30.5 GB | 132k232k with q8 cache | 6tok/sest. | 94 tok/s | – |
| Qwen 3.6 27B Alibaba Qwen | Q5_K_M | fits | 28.2 GB | 166k256k with q8 cache | 6tok/sest. | 94 tok/s | – |
| Gemma 4 31B | Q4_K_M | fits | 29.7 GB | 141k256k with q8 cache | 6tok/sest. | 89 tok/s | – |
| Qwen 3.6 35B-A3B Alibaba Qwen | UD-Q5_K_M | fits | 29.0 GB | 197k256k with q8 cache | 22tok/sest. | 458 tok/s | chat, coding, agents, long documents |
| Qwen 3.5 27B Alibaba Qwen | Q5_K_M | fits | 28.3 GB | 165k256k with q8 cache | 6tok/sest. | 94 tok/s | – |
| Gemma 4 26B-A4B | Q8_0 | fits | 29.6 GB | 176k256k with q8 cache | 15tok/sest. | 556 tok/s | chat, coding, long documents |
| Qwen 3.5 35B-A3B Alibaba Qwen | Q5_K_M | fits | 28.9 GB | 204k256k with q8 cache | 22tok/sest. | 458 tok/s | chat, coding, agents, long documents |
| GLM 4.7 Flash Z.ai | Q5_K_M | fits | 28.4 GB | 169k198k with q8 cache | 10tok/sest. | 216 tok/s | chat, coding |
| Nemotron 3.5 Lightning 30B-A3B NVIDIA | UD-Q5_K_M | fits | 30.8 GB | 129k168k with q8 cache | 29tok/sest. | 616 tok/s | chat, coding, agents, long documents |
| gpt-oss 20B OpenAI | MXFP4 | fits | 16.1 GB | 128k | 19tok/sest. | 360 tok/s | chat, coding, agents, long documents |
| Gemma 4 12B | Q8_0 | fits | 16.0 GB | 256k | 12tok/sest. | 231 tok/s | – |
| Qwen 3.5 9B Alibaba Qwen | Q8_0 | fits | 14.8 GB | 256k | 13tok/sest. | 275 tok/s | chat |
| Gemma 3 27B | Q5_K_M | fits | 30.2 GB | 128k | 6tok/sest. | 115 tok/s | – |
| Ministral 3 14B Mistral AI | Q4_K_M | fits | 29.5 GB | 135k247k with q8 cache | 6tok/sest. | 108 tok/s | chat |
| Qwen3 14B Alibaba Qwen | Q4_K_M | fits | 30.2 GB | 40k | 6tok/sest. | 92 tok/s | chat |
| DeepSeek R1 0528 Qwen3 8B DeepSeek | Q8_0 | fits | 28.0 GB | 128k | 6tok/sest. | 139 tok/s | chat |
| Qwen3 8B Alibaba Qwen | Q8_0 | fits | 28.0 GB | 40k | 6tok/sest. | 139 tok/s | chat |
| Qwen 3.5 4B Alibaba Qwen | Q8_0 | fits | 10.1 GB | 256k | 20tok/sest. | 428 tok/s | chat, coding, long documents |
| Ministral 3 8B Mistral AI | Q8_0 | fits | 27.3 GB | 153k256k with q8 cache | 7tok/sest. | 141 tok/s | chat |
| Granite 4.2 8B IBM | Q8_0 | fits | 30.6 GB | 128k | 6tok/sest. | 127 tok/s | chat |
| Gemma 4 E4B | Q8_0 | fits | 13.0 GB | 128k | 15tok/sest. | 385 tok/s | chat |
| LFM2.5 8B-A1B Liquid AI | Q8_0 | fits | 16.2 GB | 125k | 14tok/sest. | 515 tok/s | chat, coding |
| Llama 3.1 8B Meta | Q6_K | fits | 24.0 GB | 128k | 8tok/sest. | 152 tok/s | chat |
| Qwen2.5 0.5B Alibaba Qwen | Q8_0 | fits | 3.8 GB | 32k | 83tok/sest. | 1157 tok/s | chat, coding, long documents |
| Qwen3.5 0.8B Alibaba Qwen | Q8_0 | fits | 4.1 GB | 256k | 73tok/sest. | 1543 tok/s | chat, coding, agents, long documents |
| Qwen3.5 2B Alibaba Qwen | Q5_K_M | fits | 4.7 GB | 256k | 58tok/sest. | 986 tok/s | chat, coding, agents, long documents |
| LFM3 1.7B Liquid AI | Q8_0 | fits | 5.1 GB | 256k | 52tok/sest. | 1141 tok/s | chat, coding, agents, long documents |
| DeepSeek R1 Distill Qwen 1.5B DeepSeek | Q8_0 | fits | 7.1 GB | 128k | 32tok/sest. | 517 tok/s | chat, coding, long documents |
| Mellum2.1 12B A2.5B Thinking JetBrains | Q8_0 | fits | 15.7 GB | 128k | 23tok/sest. | 596 tok/s | chat, coding, agents, long documents |
| Spark X2.5 4B XHToken | mlx-4bit | fits | 9.2 GB | 653k1024k with q8 cache | 23tok/sest. | 423 tok/s | chat, coding, agents, long documents |
| MiniCPM5 2B openbmb | Q8_0 | fits | 9.6 GB | 128k | 22tok/sest. | 278 tok/s | chat, coding, agents |
| FrogNano 4B 2609 Microsoft | Q8_0 | fits | 10.2 GB | 256k | 20tok/sest. | 428 tok/s | chat, coding, agents, long documents |
| LFM2.5 2.6B Liquid AI | Q8_0 | fits | 12.0 GB | 128k | 16tok/sest. | 356 tok/s | chat, coding, agents, long documents |
| LFM2.5 VL 3B Liquid AI | Q8_0 | fits | 12.0 GB | 32k | 16tok/sest. | 343 tok/s | chat, coding, agents, long documents |
| Kimi VL A3B Thinking 2506 Moonshot AI | Q8_0 | fits | 21.5 GB | 128k | 15tok/sest. | 374 tok/s | chat, coding |
| Kimi VL A3B Moonshot AI | mlx-8bit | fits | 22.2 GB | 128k | 15tok/sest. | 276 tok/s | chat, coding |
| GLM 4 9B 0414 Z.ai | mlx-5bit | fits | 14.5 GB | 32k | 13tok/sest. | 124 tok/s | chat |
| granite vision 4.1 4b IBM | Q8_0 | fits | 15.2 GB | 128k | 12tok/sest. | 220 tok/s | chat, coding |
| granite 4.2 3b IBM | Q8_0 | fits | 15.5 GB | 128k | 12tok/sest. | 224 tok/s | chat, coding |
| GLM 4.6V Flash Z.ai | Q8_0 | fits | 16.2 GB | 128k | 12tok/sest. | 121 tok/s | chat |
| DeepSeek R1 Distill Qwen 7B DeepSeek | Q8_0 | fits | 16.4 GB | 128k | 11tok/sest. | 185 tok/s | chat |
| GLM 4.1V 9B Thinking Z.ai | mlx-8bit | fits | 17.8 GB | 64k | 10tok/sest. | 121 tok/s | – |
| Ministral 3 3B Instruct 2512 Mistral AI | Q8_0 | fits | 18.3 GB | 244k256k with q8 cache | 10tok/sest. | 215 tok/s | chat, coding |
| LFM2.5 24B A2B Liquid AI | Q5_K_M | fits | 29.1 GB | 128k | 9tok/sest. | 301 tok/s | chat, coding |
| Muse Glimmer 30B meta-models | Q6_K | fits | 25.4 GB | 128k | 7tok/sest. | 102 tok/s | – |
| DeepSeek R1 Distill Llama 8B DeepSeek | Q8_0 | fits | 25.8 GB | 128k | 7tok/sest. | 152 tok/s | chat |
| Ministral 3 8B Reasoning 2512 Mistral AI | Q8_0 | fits | 27.3 GB | 153k256k with q8 cache | 7tok/sest. | 141 tok/s | chat |
| Qwen3.8 27B OBLITERATED OBLITERATUS | mlx-4bit | fits | 29.2 GB | 150k256k with q8 cache | 6tok/sest. | 94 tok/s | – |
| Ministral 3 14B Reasoning 2512 Mistral AI | Q4_K_M | fits | 29.5 GB | 135k247k with q8 cache | 6tok/sest. | 108 tok/s | chat |
| Qwen 3.5 122B-A10B Alibaba Qwen | UD-Q2_K_XL | offload | 44.0 GB | – | 9tok/sest. | 136 tok/s | chat |
| Qwen3 Coder Next Alibaba Qwen | Q4_K_M | offload | 50.1 GB | – | 10tok/sest. | 282 tok/s | chat |
| Nemotron 3 Super 120B-A12B NVIDIA | UD-Q2_K_XL | offload | 53.9 GB | – | 8tok/sest. | 173 tok/s | – |
| Mistral Small 4 119B Mistral AI | UD-Q2_K_XL | offload | 42.1 GB | – | 12tok/sest. | 121 tok/s | chat, coding |
| Qwen3 Next 80B-A3B Alibaba Qwen | Q4_K_M | offload | 50.1 GB | – | 11tok/sest. | 308 tok/s | chat, coding |
| Devstral Small 2 24B Mistral AI | Q4_K_M | offload | 35.2 GB | 100k183k with q8 cache | 4tok/sest. | 78 tok/s | – |
| Kimi Linear 48B-A3B Moonshot AI | Q4_K_M | offload | 30.9 GB | 122k167k with q8 cache | 36tok/sest. | 343 tok/s | chat, coding, long documents |
| Qwen3 Coder 30B-A3B Alibaba Qwen | Q4_K_M | offload | 31.1 GB | 124k223k with q8 cache | 7tok/sest. | 132 tok/s | chat, coding |
| Qwen3 32B Alibaba Qwen | Q4_K_M | offload | 52.3 GB | 40k | 2tok/sest. | 28 tok/s | – |
| Mistral Small 3.2 24B Mistral AI | Q4_K_M | offload | 35.2 GB | 100k128k with q8 cache | 4tok/sest. | 78 tok/s | – |
| DeepSeek R1 Distill Qwen 32B DeepSeek | Q4_K_M | offload | 52.3 GB | 43k80k with q8 cache | 2tok/sest. | 38 tok/s | – |
| Phi-4 14B Microsoft | Q4_K_M | offload | 35.1 GB | 16k | 4tok/sest. | 85 tok/s | chat |
| DeepSeek R1 Distill Qwen 14B DeepSeek | Q4_K_M | offload | 34.2 GB | 110k128k with q8 cache | 4tok/sest. | 76 tok/s | chat |
| Devstral Small 2507 Mistral AI | Q4_K_M | offload | 35.2 GB | 100k128k with q8 cache | 4tok/sest. | 78 tok/s | – |
| Magistral Small 2506 Mistral AI | Q4_K_M | offload | 35.2 GB | 40k | 4tok/sest. | 78 tok/s | – |
| Magistral Small 2507 Mistral AI | Q4_K_M | offload | 35.2 GB | 40k | 4tok/sest. | 78 tok/s | – |
| Magistral Small 2509 Mistral AI | Q4_K_M | offload | 35.2 GB | 100k128k with q8 cache | 4tok/sest. | 78 tok/s | – |
| granite 4.2 30b IBM | Q4_K_M | offload | 50.6 GB | 50k93k with q8 cache | 2tok/sest. | 46 tok/s | – |
| DeepSeek V4 Flash DeepSeek | UD-Q2_K_XL | won't run | 102.8 GB | – | – | – | – |
| GLM 5.3 Flash Z.ai | UD-Q2_K_XL | won't run | 104.6 GB | – | – | – | – |
| Qwen 3.8 Flash Next Alibaba Qwen | UD-Q2_K_XL | won't run | 78.4 GB | – | – | – | – |
| MiniMax M2.7 MiniMax | UD-Q2_K_XL | won't run | 103.0 GB | – | – | – | – |
| gpt-oss 120B OpenAI | Q4_K_M | won't run | 64.8 GB | – | – | – | – |
| GLM 4.5 Air Z.ai | Q2_K | won't run | 67.1 GB | – | – | – | – |
| Llama 3.3 70B Meta | Q4_K_M | won't run | 81.5 GB | – | – | – | – |
| DeepSeek R1 DeepSeek | Q4_K_M | won't run | 387.1 GB | – | – | – | – |
| DeepSeek R1 0528 DeepSeek | Q4_K_M | won't run | 387.6 GB | – | – | – | – |
| DeepSeek R1 Distill Llama 70B DeepSeek | Q4_K_M | won't run | 81.5 GB | – | – | – | – |
| DeepSeek R1 Zero DeepSeek | Q4_K_M | won't run | 387.1 GB | – | – | – | – |
| DeepSeek V3 DeepSeek | Q4_K_M | won't run | 387.1 GB | – | – | – | – |
| DeepSeek V3 0324 DeepSeek | Q4_K_M | won't run | 387.1 GB | – | – | – | – |
| DeepSeek V3.1 DeepSeek | Q4_K_M | won't run | 388.0 GB | – | – | – | – |
| DeepSeek V3.1 Terminus DeepSeek | Q4_K_M | won't run | 388.0 GB | – | – | – | – |
| DeepSeek V3.2 DeepSeek | Q4_K_M | won't run | 388.0 GB | – | – | – | – |
| DeepSeek V4 Flash 0731 DeepSeek | MXFP4 | won't run | 158.2 GB | – | – | – | – |
| DeepSeek V4 Flash Vision Exp DeepSeek | MXFP4 | won't run | 167.0 GB | – | – | – | – |
| Devstral 2 123B Instruct 2512 Mistral AI | Q4_K_M | won't run | 115.6 GB | – | – | – | – |
| GLM 4.5 Z.ai | Q4_K_M | won't run | 249.4 GB | – | – | – | – |
| GLM 4.5V Z.ai | Q4_K_M | won't run | 84.1 GB | – | – | – | – |
| GLM 4.6 Z.ai | Q4_K_M | won't run | 248.6 GB | – | – | – | – |
| GLM 4.6V Z.ai | Q4_K_M | won't run | 90.5 GB | – | – | – | – |
| GLM 4.7 Z.ai | Q4_K_M | won't run | 249.4 GB | – | – | – | – |
| GLM 5 Z.ai | MXFP4 | won't run | 395.2 GB | – | – | – | – |
| GLM 5.1 Z.ai | MXFP4 | won't run | 432.8 GB | – | – | – | – |
| GLM 5.2 Z.ai | UD-Q4_K_M | won't run | 446.7 GB | – | – | – | – |
| GLM 5.3 Z.ai | Q8_0 | won't run | 759.1 GB | – | – | – | – |
| Kimi Dev 72B Moonshot AI | Q6_K | won't run | 101.8 GB | – | – | – | – |
| Kimi K2 Moonshot AI | Q4_K_M | won't run | 588.6 GB | – | – | – | – |
| Kimi K2 Instruct 0905 Moonshot AI | Q4_K_M | won't run | 589.0 GB | – | – | – | – |
| Kimi K2 Thinking Moonshot AI | Q4_K_M | won't run | 589.0 GB | – | – | – | – |
| Kimi K2.5 Moonshot AI | Q4_K_M | won't run | 589.0 GB | – | – | – | – |
| MiniMax M2 MiniMax | Q4_K_M | won't run | 161.7 GB | – | – | – | – |
| MiniMax M2.1 MiniMax | Q4_K_M | won't run | 161.7 GB | – | – | – | – |
| MiniMax M2.5 MiniMax | MXFP4 | won't run | 148.1 GB | – | – | – | – |
| MiniMax M3 MiniMax | MXFP4 | won't run | 256.5 GB | – | – | – | – |
| Mistral Medium 3.5 128B Mistral AI | Q4_K_M | won't run | 115.6 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?
- Qwen 3.8 27B (UD-Q6_K) fits fully in its memory at 128k context and generates at about 6 tok/s, an estimate from its memory bandwidth. 49 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 103 GB at 128k 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 128k context. See the best hardware for local AI at every budget.