NVIDIA GeForce RTX 5060
8 GB · 448 GB/s · 145 W
In short
The NVIDIA GeForce RTX 5060 has 8 GB of GDDR7 memory with 448 GB/s of bandwidth, which sets how fast it generates text. At 64k context it runs 27 of the 129 open models we track fully in memory; the most capable is gpt-oss 20B at MXFP4, at about 41 tok/s (estimated). The best new price for the NVIDIA GeForce RTX 5060 in Canada is $660 at Amazon; used, it starts at $575 (asking price).
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 |
|---|---|---|---|---|---|
| eBayZOTAC GAMING GeForce RTX 5060 Twin Edge OC DLSS 4 8GB GDDR7 128-bit 28 Gbps PCIE | used, asking price | $575 | In stock | 3 h ago | Buy |
| AmazonASUS Dual GeForce RTX 5060 8GB GDDR7 OC Edition | New | $660 | In stock | 5 h ago | Buy |
| AmazonMSI Gaming RTX 5060 8G Shadow 2X OC Graphics Card | New | $690 | In stock | 5 h ago | Buy |
| AmazonGIGABYTE GeForce RTX 5060 WINDFORCE OC 8G Graphics Card | New | $710 | In stock | 5 h ago | Buy |
| AmazonASUS Dual GeForce RTX 5060 8GB GDDR7 White OC Edition Gaming Graphics Card | New | $720 | In stock | 5 h ago | Buy |
| AmazonZOTAC Gaming GeForce RTX 5060 Twin Edge OC DLSS 4 8GB GDDR7 128-bit 28 Gbps… | New | $728 | In stock | 5 h ago | Buy |
| AmazonASUS PRIME GeForce RTX 5060 8GB GDDR7 Gaming Graphics Card | New | $740 | In stock | 5 h ago | Buy |
| AmazonASUS The SFF-Ready Prime GeForce RTX 5060 8GB GDDR7 OC Edition Graphics Card | New | $740 | In stock | 5 h ago | Buy |
| AmazonMSI Gaming RTX 5060 8G Ventus 2X OC Graphics Card | New | $745 | In stock | 5 h ago | Buy |
| AmazonASUS GeForce RTX 5060 LP BRK 8GB GDDR7 OC Edition Graphics Card | New | $752 | In stock | 11 h ago | Buy |
| AmazonMSI Gaming RTX 5060 8G Ventus 3X OC Graphics Card | New | $770 | In stock | 5 h ago | Buy |
| AmazonMSI Gaming RTX 5060 8G Gaming Trio OC Graphics Card | New | $775 | In stock | 5 h ago | Buy |
| AmazonMSI Gaming RTX 5060 8G Gaming OC Graphics Card | New | $780 | In stock | 5 h ago | Buy |
| AmazonASUS TUF Gaming GeForce RTX 5060 8GB GDDR7 OC Edition Gaming Graphics Card | New | $800 | In stock | 5 h ago | Buy |
| eBayMSI GeForce RTX 5060 8G VENTUS 2X OC 8GB GDDR7 Graphics Card | New | $975 | In stock | 3 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 @64k | Prefill | Good for |
|---|---|---|---|---|---|---|---|
| gpt-oss 20B OpenAI | MXFP4 | fits | 14.3 GB | 82k128k with q8 cache | 41tok/sest. | 581 tok/s | chat, coding, agents, long documents |
| Gemma 4 12B | Q8_0 | fits | 14.6 GB | 73k122k with q8 cache | 20tok/sest. | 299 tok/s | chat, coding |
| Qwen 3.5 9B Alibaba Qwen | Q8_0 | fits | 12.4 GB | 128k212k with q8 cache | 24tok/sest. | 366 tok/s | chat, coding, long documents |
| Qwen 3.5 4B Alibaba Qwen | Q8_0 | fits | 7.7 GB | 255k256k with q8 cache | 42tok/sest. | 634 tok/s | chat, coding, long documents |
| Gemma 4 E4B | Q8_0 | fits | 10.9 GB | 128k | 28tok/sest. | 479 tok/s | chat, coding, long documents |
| LFM2.5 8B-A1B Liquid AI | Q8_0 | fits | 12.9 GB | 100k125k with q8 cache | 39tok/sest. | 1022 tok/s | chat, coding, long documents |
| Llama 3.1 8B Meta | Q4_K_M | fits | 14.1 GB | 69k126k with q8 cache | 21tok/sest. | 260 tok/s | chat, coding |
| Qwen2.5 0.5B Alibaba Qwen | Q8_0 | fits | 2.7 GB | 32k | 208tok/sest. | 2267 tok/s | chat, coding, long documents |
| Qwen3.5 0.8B Alibaba Qwen | Q8_0 | fits | 3.0 GB | 256k | 170tok/sest. | 2570 tok/s | chat, coding, agents, long documents |
| Qwen3.5 2B Alibaba Qwen | Q5_K_M | fits | 3.6 GB | 256k | 123tok/sest. | 1401 tok/s | chat, coding, agents, long documents |
| LFM3 1.7B Liquid AI | Q8_0 | fits | 4.0 GB | 256k | 104tok/sest. | 1690 tok/s | chat, coding, agents, long documents |
| DeepSeek R1 Distill Qwen 1.5B DeepSeek | Q8_0 | fits | 5.0 GB | 128k | 74tok/sest. | 939 tok/s | chat, coding, long documents |
| MiniCPM5 2B openbmb | Q8_0 | fits | 6.6 GB | 128k | 51tok/sest. | 529 tok/s | chat, coding, agents, long documents |
| Spark X2.5 4B XHToken | mlx-4bit | fits | 6.6 GB | 263k441k with q8 cache | 50tok/sest. | 650 tok/s | chat, coding, agents, long documents |
| Mellum2.1 12B A2.5B Thinking JetBrains | Q8_0 | fits | 14.4 GB | 83k125k with q8 cache | 46tok/sest. | 947 tok/s | chat, coding, agents, long documents |
| Kimi VL A3B Thinking 2506 Moonshot AI | Q4_K_M | fits | 13.2 GB | 109k128k with q8 cache | 42tok/sest. | 651 tok/s | chat, coding, long documents |
| FrogNano 4B 2609 Microsoft | Q8_0 | fits | 7.8 GB | 251k256k with q8 cache | 41tok/sest. | 634 tok/s | chat, coding, agents, long documents |
| LFM2.5 2.6B Liquid AI | Q8_0 | fits | 7.9 GB | 128k | 40tok/sest. | 642 tok/s | chat, coding, agents, long documents |
| LFM2.5 VL 3B Liquid AI | Q8_0 | fits | 7.9 GB | 32k | 40tok/sest. | 604 tok/s | chat, coding, agents, long documents |
| Kimi VL A3B Moonshot AI | mlx-5bit | fits | 14.4 GB | 75k123k with q8 cache | 40tok/sest. | 512 tok/s | chat, coding, agents |
| granite vision 4.1 4b IBM | Q8_0 | fits | 9.8 GB | 122k128k with q8 cache | 31tok/sest. | 403 tok/s | chat, coding |
| granite 4.2 3b IBM | Q8_0 | fits | 10.1 GB | 119k128k with q8 cache | 30tok/sest. | 416 tok/s | chat, coding, agents |
| Ministral 3 3B Instruct 2512 Mistral AI | Q8_0 | fits | 11.4 GB | 95k172k with q8 cache | 26tok/sest. | 399 tok/s | chat, coding, agents |
| GLM 4 9B 0414 Z.ai | mlx-5bit | fits | 11.7 GB | 32k | 25tok/sest. | 214 tok/s | chat, coding |
| DeepSeek R1 Distill Qwen 7B DeepSeek | Q8_0 | fits | 12.5 GB | 101k128k with q8 cache | 23tok/sest. | 304 tok/s | chat, coding |
| GLM 4.6V Flash Z.ai | Q8_0 | fits | 13.3 GB | 97k128k with q8 cache | 22tok/sest. | 205 tok/s | chat, coding |
| DeepSeek R1 Distill Llama 8B DeepSeek | Q4_K_M | fits | 14.1 GB | 69k126k with q8 cache | 21tok/sest. | 260 tok/s | chat, coding |
| Qwen 3.8 27B Alibaba Qwen | UD-Q4_K_M | offload | 20.9 GB | – | 6tok/sest. | 102 tok/s | – |
| Qwen 3.6 27B Alibaba Qwen | Q4_K_M | offload | 21.3 GB | – | 6tok/sest. | 101 tok/s | – |
| Gemma 4 31B | Q4_K_M | offload | 24.3 GB | – | 4tok/sest. | 86 tok/s | – |
| Mistral Small 4 119B Mistral AI | UD-Q2_K_XL | offload | 40.3 GB | – | 12tok/sest. | 156 tok/s | chat |
| Qwen 3.6 35B-A3B Alibaba Qwen | MXFP4 | offload | 23.0 GB | – | 20tok/sest. | 586 tok/s | chat, coding |
| Qwen 3.5 27B Alibaba Qwen | Q4_K_M | offload | 21.2 GB | – | 6tok/sest. | 102 tok/s | – |
| Gemma 4 26B-A4B | MXFP4 | offload | 18.3 GB | – | 22tok/sest. | 679 tok/s | chat, coding |
| Qwen 3.5 35B-A3B Alibaba Qwen | Q4_K_M | offload | 23.3 GB | – | 20tok/sest. | 582 tok/s | chat, coding |
| GLM 4.7 Flash Z.ai | Q4_K_M | offload | 21.8 GB | – | 10tok/sest. | 299 tok/s | chat |
| Devstral Small 2 24B Mistral AI | Q4_K_M | offload | 24.8 GB | 2k3k with q8 cache | 4tok/sest. | 92 tok/s | – |
| Nemotron 3.5 Lightning 30B-A3B NVIDIA | MXFP4 | offload | 23.5 GB | – | 24tok/sest. | 714 tok/s | chat, coding, long documents |
| Kimi Linear 48B-A3B Moonshot AI | Q4_K_M | offload | 30.0 GB | – | 22tok/sest. | 415 tok/s | chat, coding |
| Qwen3 Coder 30B-A3B Alibaba Qwen | Q4_K_M | offload | 24.8 GB | – | 7tok/sest. | 202 tok/s | chat |
| Qwen3 32B Alibaba Qwen | Q4_K_M | offload | 35.9 GB | – | 2tok/sest. | 41 tok/s | – |
| Gemma 3 27B | Q4_K_M | offload | 22.3 GB | – | 5tok/sest. | 111 tok/s | – |
| Mistral Small 3.2 24B Mistral AI | Q4_K_M | offload | 24.8 GB | 2k3k with q8 cache | 4tok/sest. | 92 tok/s | – |
| DeepSeek R1 Distill Qwen 32B DeepSeek | Q4_K_M | offload | 36.0 GB | – | 2tok/sest. | 50 tok/s | – |
| Ministral 3 14B Mistral AI | Q4_K_M | offload | 19.1 GB | 37k67k with q8 cache | 7tok/sest. | 149 tok/s | chat, coding |
| Qwen3 14B Alibaba Qwen | Q4_K_M | offload | 19.9 GB | 32k40k with q8 cache | 7tok/sest. | 128 tok/s | chat |
| DeepSeek R1 0528 Qwen3 8B DeepSeek | Q4_K_M | offload | 15.2 GB | 61k111k with q8 cache | 17tok/sest. | 238 tok/s | chat, coding |
| Qwen3 8B Alibaba Qwen | Q4_K_M | offload | 15.2 GB | 40k | 17tok/sest. | 238 tok/s | chat, coding |
| Phi-4 14B Microsoft | Q4_K_M | offload | 22.3 GB | 16k | 5tok/sest. | 121 tok/s | chat |
| Ministral 3 8B Mistral AI | Q4_K_M | offload | 14.8 GB | 63k115k with q8 cache | 19tok/sest. | 239 tok/s | chat, coding |
| Granite 4.2 8B IBM | Q4_K_M | offload | 16.6 GB | 52k96k with q8 cache | 11tok/sest. | 205 tok/s | chat, coding |
| Ministral 3 8B Reasoning 2512 Mistral AI | Q4_K_M | offload | 14.8 GB | 63k115k with q8 cache | 19tok/sest. | 239 tok/s | chat, coding |
| GLM 4.1V 9B Thinking Z.ai | mlx-8bit | offload | 14.9 GB | 60k64k with q8 cache | 18tok/sest. | 204 tok/s | chat, coding |
| LFM2.5 24B A2B Liquid AI | Q4_K_M | offload | 20.8 GB | – | 11tok/sest. | 477 tok/s | chat, coding |
| Muse Glimmer 30B meta-models | Q4_K_M | offload | 18.5 GB | – | 8tok/sest. | 111 tok/s | – |
| Ministral 3 14B Reasoning 2512 Mistral AI | Q4_K_M | offload | 19.1 GB | 37k67k with q8 cache | 7tok/sest. | 149 tok/s | chat, coding |
| DeepSeek R1 Distill Qwen 14B DeepSeek | Q4_K_M | offload | 21.8 GB | 27k50k with q8 cache | 5tok/sest. | 111 tok/s | chat |
| Devstral Small 2507 Mistral AI | Q4_K_M | offload | 24.8 GB | 2k3k with q8 cache | 4tok/sest. | 93 tok/s | – |
| Magistral Small 2506 Mistral AI | Q4_K_M | offload | 24.8 GB | 2k3k with q8 cache | 4tok/sest. | 93 tok/s | – |
| Magistral Small 2507 Mistral AI | Q4_K_M | offload | 24.8 GB | 2k3k with q8 cache | 4tok/sest. | 93 tok/s | – |
| Magistral Small 2509 Mistral AI | Q4_K_M | offload | 24.8 GB | 2k3k with q8 cache | 4tok/sest. | 92 tok/s | – |
| Qwen3.8 27B OBLITERATED OBLITERATUS | mlx-4bit | offload | 24.9 GB | – | 4tok/sest. | 94 tok/s | – |
| granite 4.2 30b IBM | Q4_K_M | offload | 34.3 GB | – | 2tok/sest. | 60 tok/s | – |
| DeepSeek V4 Flash DeepSeek | UD-Q2_K_XL | won't run | 97.0 GB | – | – | – | – |
| GLM 5.3 Flash Z.ai | UD-Q2_K_XL | won't run | 103.5 GB | – | – | – | – |
| Qwen 3.8 Flash Next Alibaba Qwen | UD-Q2_K_XL | won't run | 76.5 GB | – | – | – | – |
| MiniMax M2.7 MiniMax | UD-Q2_K_XL | won't run | 87.1 GB | – | – | – | – |
| Qwen 3.5 122B-A10B Alibaba Qwen | UD-Q2_K_XL | won't run | 42.1 GB | – | – | – | – |
| Nemotron 3 Super 120B-A12B NVIDIA | UD-Q2_K_XL | won't run | 53.0 GB | – | – | – | – |
| Qwen3 Coder Next Alibaba Qwen | Q4_K_M | won't run | 48.2 GB | – | – | – | – |
| gpt-oss 120B OpenAI | Q4_K_M | won't run | 62.2 GB | – | – | – | – |
| GLM 4.5 Air Z.ai | Q2_K | won't run | 55.2 GB | – | – | – | – |
| Qwen3 Next 80B-A3B Alibaba Qwen | Q4_K_M | won't run | 48.2 GB | – | – | – | – |
| Llama 3.3 70B Meta | Q4_K_M | won't run | 61.1 GB | – | – | – | – |
| DeepSeek R1 DeepSeek | Q4_K_M | won't run | 382.4 GB | – | – | – | – |
| DeepSeek R1 0528 DeepSeek | Q4_K_M | won't run | 382.9 GB | – | – | – | – |
| DeepSeek R1 Distill Llama 70B DeepSeek | Q4_K_M | won't run | 61.1 GB | – | – | – | – |
| DeepSeek R1 Zero DeepSeek | Q4_K_M | won't run | 382.4 GB | – | – | – | – |
| DeepSeek V3 DeepSeek | Q4_K_M | won't run | 382.4 GB | – | – | – | – |
| DeepSeek V3 0324 DeepSeek | Q4_K_M | won't run | 382.4 GB | – | – | – | – |
| DeepSeek V3.1 DeepSeek | Q4_K_M | won't run | 383.3 GB | – | – | – | – |
| DeepSeek V3.1 Terminus DeepSeek | Q4_K_M | won't run | 383.3 GB | – | – | – | – |
| DeepSeek V3.2 DeepSeek | Q4_K_M | won't run | 383.3 GB | – | – | – | – |
| DeepSeek V4 Flash 0731 DeepSeek | MXFP4 | won't run | 152.5 GB | – | – | – | – |
| DeepSeek V4 Flash Vision Exp DeepSeek | MXFP4 | won't run | 161.3 GB | – | – | – | – |
| Devstral 2 123B Instruct 2512 Mistral AI | Q4_K_M | won't run | 93.2 GB | – | – | – | – |
| GLM 4.5 Z.ai | Q4_K_M | won't run | 226.1 GB | – | – | – | – |
| GLM 4.5V Z.ai | Q4_K_M | won't run | 72.2 GB | – | – | – | – |
| GLM 4.6 Z.ai | Q4_K_M | won't run | 225.3 GB | – | – | – | – |
| GLM 4.6V Z.ai | Q4_K_M | won't run | 78.6 GB | – | – | – | – |
| GLM 4.7 Z.ai | Q4_K_M | won't run | 226.1 GB | – | – | – | – |
| GLM 5 Z.ai | MXFP4 | won't run | 389.3 GB | – | – | – | – |
| GLM 5.1 Z.ai | MXFP4 | won't run | 426.9 GB | – | – | – | – |
| GLM 5.2 Z.ai | UD-Q4_K_M | won't run | 440.8 GB | – | – | – | – |
| GLM 5.3 Z.ai | Q8_0 | won't run | 753.3 GB | – | – | – | – |
| Kimi Dev 72B Moonshot AI | Q6_K | won't run | 81.4 GB | – | – | – | – |
| Kimi K2 Moonshot AI | Q4_K_M | won't run | 583.9 GB | – | – | – | – |
| Kimi K2 Instruct 0905 Moonshot AI | Q4_K_M | won't run | 584.3 GB | – | – | – | – |
| Kimi K2 Thinking Moonshot AI | Q4_K_M | won't run | 584.3 GB | – | – | – | – |
| Kimi K2.5 Moonshot AI | Q4_K_M | won't run | 584.3 GB | – | – | – | – |
| MiniMax M2 MiniMax | Q4_K_M | won't run | 145.8 GB | – | – | – | – |
| MiniMax M2.1 MiniMax | Q4_K_M | won't run | 145.8 GB | – | – | – | – |
| MiniMax M2.5 MiniMax | MXFP4 | won't run | 132.2 GB | – | – | – | – |
| MiniMax M3 MiniMax | MXFP4 | won't run | 248.7 GB | – | – | – | – |
| Mistral Medium 3.5 128B Mistral AI | Q4_K_M | won't run | 93.2 GB | – | – | – | – |
Specifications
From the manufacturer's published specifications; sources are linked where we have them.
- GPU
- GeForce RTX 5060
- Memory
- 8 GB GDDR7
- Memory bus
- 128-bit
- Memory bandwidth
- 448 GB/s
- FP16 compute
- 19.177 TFLOPS
- Software
- CUDA
- Board power
- 145 W
- PCIe generation
- Gen 5
- PCIe lanes
- x8
Compare with
Questions
- How much memory does the NVIDIA GeForce RTX 5060 have for AI models?
- The NVIDIA GeForce RTX 5060 has 8 GB of GDDR7 memory with 448 GB/s of bandwidth, which sets how fast it generates text.
- What is the best model the NVIDIA GeForce RTX 5060 can run locally?
- gpt-oss 20B (MXFP4) fits fully in its memory at 64k context and generates at about 41 tok/s, an estimate from its memory bandwidth. 27 of the 129 models we track fit at that context.
- Can the NVIDIA GeForce RTX 5060 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 GeForce RTX 5060 and system RAM can hold.
- How much does the NVIDIA GeForce RTX 5060 cost in Canada?
- The best new price for the NVIDIA GeForce RTX 5060 in Canada is $660 at Amazon; used, it starts at $575 (asking price).
Speeds on this page are for 64k context. See the best hardware for local AI at every budget.