NVIDIA GeForce RTX 5060 Ti 8GB
8 GB · 448 GB/s · 180 W
In short
The NVIDIA GeForce RTX 5060 Ti 8GB has 8 GB of GDDR7 memory with 448 GB/s of bandwidth, which sets how fast it generates text. At 128k context it runs 21 of the 129 open models we track fully in memory; the most capable is Gemma 4 12B at Q6_K, at about 23 tok/s (estimated). The best new price for the NVIDIA GeForce RTX 5060 Ti 8GB in Canada is $732 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 |
|---|---|---|---|---|---|
| AmazonMSI GeForce RTX 5060 Ti 8G Shadow 2X OC Plus Graphics Card | New | $732 | In stock | 5 h ago | Buy |
| eBayGIGABYTE RTX 5060 Ti EAGLE OC 8GB | New | $770 | In stock | 3 h ago | Buy |
| AmazonASUS PRIME GeForce RTX 5060 Ti 8GB GDDR7 Gaming Graphics Card | New | $780 | In stock | 5 h ago | Buy |
| AmazonASUS Dual GeForce RTX 5060 Ti 8GB GDDR7 OC Edition | New | $780 | In stock | 5 h ago | Buy |
| AmazonGIGABYTE GeForce RTX 5060 Ti WINDFORCE OC 8G Graphics Card | New | $791 | In stock | 5 h ago | Buy |
| AmazonZOTAC Gaming GeForce RTX 5060 Ti 8GB Twin Edge OC DLSS 4 8GB GDDR7 128-bit 28… | New | $806 | In stock | 5 h ago | Buy |
| AmazonZOTAC Gaming GeForce RTX 5060 Ti 8GB Twin Edge OC White Edition DLSS 4 8GB… | New | $816 | In stock | 5 h ago | Buy |
| AmazonASUS Prime GeForce RTX 5060 Ti 8GB GDDR7 OC Edition Gaming Graphics Card | New | $850 | In stock | 5 h ago | Buy |
| AmazonGIGABYTE GeForce RTX 5060 Ti Eagle OC 8G Graphics Card | New | $895 | In stock | 5 h ago | Buy |
| AmazonMSI Gaming RTX 5060 Ti 8G Gaming OC Graphics Card | New | $900 | In stock | 5 h ago | Buy |
| AmazonPNY NVIDIA GeForce RTX 5060 Ti 8GB OC Dual-Fan Graphics Card | New | $907 | In stock | 5 h ago | Buy |
| AmazonASUS TUF Gaming GeForce RTX 5060 Ti 8GB GDDR7 OC Edition Graphics Card | New | $970 | In stock | 5 h ago | Buy |
| AmazonGigabyte GeForce RTX 5060 Ti WINDFORCE OC 8G Graphics Card | New | $980 | In stock | 5 h ago | Buy |
| AmazonMSI Gaming RTX 5060 Ti 8G Ventus 2X OC Plus Graphics Card | New | $989+ $8 delivery | In stock | 5 h ago | Buy |
| AmazonMSI Gaming RTX 5060 Ti 8G Ventus 2X OC White Plus Graphics Card | New | $1,032+ $8 delivery | In stock | 5 h ago | Buy |
| AmazonMSI GeForce RTX 5060 Ti 8G Ventus 2X OC Plus Graphics Card | New | $1,068 | In stock | 5 h ago | Buy |
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 |
|---|---|---|---|---|---|---|---|
| Gemma 4 12B | Q6_K | fits | 13.3 GB | 198k256k with q8 cache | 23tok/sest. | 322 tok/s | chat, coding, long documents |
| Qwen 3.5 9B Alibaba Qwen | Q8_0 | fits | 14.8 GB | 128k212k with q8 cache | 20tok/sest. | 382 tok/s | chat, coding, long documents |
| Qwen 3.5 4B Alibaba Qwen | Q8_0 | fits | 10.1 GB | 255k256k with q8 cache | 32tok/sest. | 596 tok/s | chat, coding, long documents |
| Gemma 4 E4B | Q8_0 | fits | 13.0 GB | 128k | 23tok/sest. | 536 tok/s | chat, coding, long documents |
| LFM2.5 8B-A1B Liquid AI | UD-Q6_K | fits | 14.5 GB | 125k | 23tok/sest. | 717 tok/s | chat, coding, long documents |
| Qwen2.5 0.5B Alibaba Qwen | Q8_0 | fits | 3.8 GB | 32k | 130tok/sest. | 1610 tok/s | chat, coding, long documents |
| Qwen3.5 0.8B Alibaba Qwen | Q8_0 | fits | 4.1 GB | 256k | 114tok/sest. | 2147 tok/s | chat, coding, agents, long documents |
| Qwen3.5 2B Alibaba Qwen | Q5_K_M | fits | 4.7 GB | 256k | 91tok/sest. | 1373 tok/s | chat, coding, agents, long documents |
| LFM3 1.7B Liquid AI | Q8_0 | fits | 5.1 GB | 256k | 80tok/sest. | 1588 tok/s | chat, coding, agents, long documents |
| DeepSeek R1 Distill Qwen 1.5B DeepSeek | Q8_0 | fits | 7.1 GB | 128k | 49tok/sest. | 719 tok/s | chat, coding, long documents |
| Mellum2.1 12B A2.5B Thinking JetBrains | Q6_K | fits | 14.3 GB | 128k | 39tok/sest. | 829 tok/s | chat, coding, agents, long documents |
| Spark X2.5 4B XHToken | mlx-4bit | fits | 9.2 GB | 263k441k with q8 cache | 35tok/sest. | 589 tok/s | chat, coding, agents, long documents |
| MiniCPM5 2B openbmb | Q8_0 | fits | 9.6 GB | 128k | 33tok/sest. | 387 tok/s | chat, coding, agents, long documents |
| FrogNano 4B 2609 Microsoft | Q8_0 | fits | 10.2 GB | 251k256k with q8 cache | 31tok/sest. | 596 tok/s | chat, coding, agents, long documents |
| LFM2.5 2.6B Liquid AI | Q8_0 | fits | 12.0 GB | 128k | 25tok/sest. | 496 tok/s | chat, coding, agents, long documents |
| LFM2.5 VL 3B Liquid AI | Q8_0 | fits | 12.0 GB | 32k | 25tok/sest. | 477 tok/s | chat, coding, agents, long documents |
| GLM 4.6V Flash Z.ai | Q4_K_M | fits | 12.6 GB | 128k | 24tok/sest. | 168 tok/s | chat, coding |
| granite vision 4.1 4b IBM | Q5_K_M | fits | 14.1 GB | 128k | 21tok/sest. | 306 tok/s | chat, coding, long documents |
| GLM 4 9B 0414 Z.ai | mlx-5bit | fits | 14.5 GB | 32k | 20tok/sest. | 173 tok/s | chat, coding |
| DeepSeek R1 Distill Qwen 7B DeepSeek | Q6_K | fits | 14.7 GB | 128k | 20tok/sest. | 257 tok/s | chat, coding |
| granite 4.2 3b IBM | Q6_K | fits | 14.8 GB | 128k | 20tok/sest. | 312 tok/s | chat, coding, agents, long documents |
| Qwen 3.8 27B Alibaba Qwen | UD-Q4_K_M | offload | 25.3 GB | – | 4tok/sest. | 96 tok/s | – |
| Qwen 3.6 27B Alibaba Qwen | Q4_K_M | offload | 25.7 GB | – | 4tok/sest. | 95 tok/s | – |
| Gemma 4 31B | Q4_K_M | offload | 29.7 GB | – | 3tok/sest. | 85 tok/s | – |
| Qwen 3.6 35B-A3B Alibaba Qwen | MXFP4 | offload | 24.6 GB | – | 13tok/sest. | 472 tok/s | chat, coding |
| Qwen 3.5 27B Alibaba Qwen | Q4_K_M | offload | 25.6 GB | – | 4tok/sest. | 95 tok/s | – |
| Gemma 4 26B-A4B | MXFP4 | offload | 20.0 GB | – | 14tok/sest. | 641 tok/s | chat, coding |
| Qwen 3.5 35B-A3B Alibaba Qwen | Q4_K_M | offload | 24.9 GB | – | 13tok/sest. | 470 tok/s | chat, coding |
| GLM 4.7 Flash Z.ai | Q4_K_M | offload | 25.5 GB | – | 6tok/sest. | 219 tok/s | chat |
| Devstral Small 2 24B Mistral AI | Q4_K_M | offload | 35.2 GB | 2k3k with q8 cache | 2tok/sest. | 74 tok/s | – |
| Nemotron 3.5 Lightning 30B-A3B NVIDIA | MXFP4 | offload | 24.3 GB | – | 20tok/sest. | 640 tok/s | chat, coding, long documents |
| gpt-oss 20B OpenAI | Q4_K_M | offload | 15.7 GB | 97k128k with q8 cache | 24tok/sest. | 482 tok/s | chat, coding, agents, long documents |
| Kimi Linear 48B-A3B Moonshot AI | Q4_K_M | offload | 30.9 GB | – | 17tok/sest. | 319 tok/s | chat, coding, long documents |
| Qwen3 Coder 30B-A3B Alibaba Qwen | Q4_K_M | offload | 31.1 GB | – | 3tok/sest. | 123 tok/s | chat |
| Gemma 3 27B | Q4_K_M | offload | 27.7 GB | – | 3tok/sest. | 113 tok/s | – |
| Mistral Small 3.2 24B Mistral AI | Q4_K_M | offload | 35.2 GB | 2k3k with q8 cache | 2tok/sest. | 74 tok/s | – |
| Ministral 3 14B Mistral AI | Q4_K_M | offload | 29.5 GB | 37k67k with q8 cache | 3tok/sest. | 103 tok/s | chat, coding |
| Qwen3 14B Alibaba Qwen | Q4_K_M | offload | 30.2 GB | 32k40k with q8 cache | 3tok/sest. | 86 tok/s | chat |
| DeepSeek R1 0528 Qwen3 8B DeepSeek | Q4_K_M | offload | 24.5 GB | 61k111k with q8 cache | 4tok/sest. | 144 tok/s | chat, coding |
| Qwen3 8B Alibaba Qwen | Q4_K_M | offload | 24.5 GB | 40k | 4tok/sest. | 144 tok/s | chat, coding |
| Phi-4 14B Microsoft | Q4_K_M | offload | 35.1 GB | 16k | 2tok/sest. | 81 tok/s | chat |
| Ministral 3 8B Mistral AI | Q4_K_M | offload | 23.7 GB | 63k115k with q8 cache | 4tok/sest. | 148 tok/s | chat, coding |
| Granite 4.2 8B IBM | Q4_K_M | offload | 27.0 GB | 52k96k with q8 cache | 3tok/sest. | 125 tok/s | chat, coding |
| Llama 3.1 8B Meta | Q4_K_M | offload | 22.4 GB | 69k126k with q8 cache | 5tok/sest. | 165 tok/s | chat, coding |
| Kimi VL A3B Thinking 2506 Moonshot AI | Q4_K_M | offload | 15.5 GB | 109k128k with q8 cache | 23tok/sest. | 505 tok/s | chat, coding, long documents |
| Kimi VL A3B Moonshot AI | mlx-5bit | offload | 16.7 GB | 75k123k with q8 cache | 18tok/sest. | 355 tok/s | chat, coding, agents, long documents |
| Ministral 3 3B Instruct 2512 Mistral AI | Q4_K_M | offload | 16.8 GB | 108k195k with q8 cache | 11tok/sest. | 275 tok/s | chat, coding, agents |
| GLM 4.1V 9B Thinking Z.ai | mlx-8bit | offload | 17.8 GB | 60k64k with q8 cache | 9tok/sest. | 149 tok/s | chat, coding |
| Muse Glimmer 30B meta-models | Q4_K_M | offload | 19.7 GB | – | 7tok/sest. | 119 tok/s | – |
| DeepSeek R1 Distill Llama 8B DeepSeek | Q4_K_M | offload | 22.4 GB | 69k126k with q8 cache | 5tok/sest. | 165 tok/s | chat, coding |
| LFM2.5 24B A2B Liquid AI | Q4_K_M | offload | 26.1 GB | – | 5tok/sest. | 301 tok/s | chat, coding |
| Ministral 3 8B Reasoning 2512 Mistral AI | Q4_K_M | offload | 23.7 GB | 63k115k with q8 cache | 4tok/sest. | 148 tok/s | chat, coding |
| Qwen3.8 27B OBLITERATED OBLITERATUS | mlx-4bit | offload | 29.2 GB | – | 3tok/sest. | 90 tok/s | – |
| Ministral 3 14B Reasoning 2512 Mistral AI | Q4_K_M | offload | 29.5 GB | 37k67k with q8 cache | 3tok/sest. | 103 tok/s | chat, coding |
| DeepSeek R1 Distill Qwen 14B DeepSeek | Q4_K_M | offload | 34.2 GB | 27k50k with q8 cache | 2tok/sest. | 73 tok/s | chat |
| Devstral Small 2507 Mistral AI | Q4_K_M | offload | 35.2 GB | 2k3k with q8 cache | 2tok/sest. | 75 tok/s | – |
| Magistral Small 2506 Mistral AI | Q4_K_M | offload | 35.2 GB | 2k3k with q8 cache | 2tok/sest. | 75 tok/s | – |
| Magistral Small 2507 Mistral AI | Q4_K_M | offload | 35.2 GB | 2k3k with q8 cache | 2tok/sest. | 75 tok/s | – |
| Magistral Small 2509 Mistral AI | Q4_K_M | offload | 35.2 GB | 2k3k with q8 cache | 2tok/sest. | 74 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 | – | – | – | – |
| Qwen 3.5 122B-A10B Alibaba Qwen | UD-Q2_K_XL | won't run | 44.0 GB | – | – | – | – |
| Nemotron 3 Super 120B-A12B NVIDIA | UD-Q2_K_XL | won't run | 53.9 GB | – | – | – | – |
| Qwen3 Coder Next Alibaba Qwen | Q4_K_M | won't run | 50.1 GB | – | – | – | – |
| gpt-oss 120B OpenAI | Q4_K_M | won't run | 64.8 GB | – | – | – | – |
| Mistral Small 4 119B Mistral AI | UD-Q2_K_XL | won't run | 42.1 GB | – | – | – | – |
| GLM 4.5 Air Z.ai | Q2_K | won't run | 67.1 GB | – | – | – | – |
| Qwen3 Next 80B-A3B Alibaba Qwen | Q4_K_M | won't run | 50.1 GB | – | – | – | – |
| Qwen3 32B Alibaba Qwen | Q4_K_M | won't run | 52.3 GB | – | – | – | – |
| DeepSeek R1 Distill Qwen 32B DeepSeek | Q4_K_M | won't run | 52.3 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 | – | – | – | – |
| granite 4.2 30b IBM | Q4_K_M | won't run | 50.6 GB | – | – | – | – |
Specifications
From the manufacturer's published specifications; sources are linked where we have them.
- GPU
- GeForce RTX 5060 Ti 8GB
- Memory
- 8 GB GDDR7
- Memory bus
- 128-bit
- Memory bandwidth
- 448 GB/s
- FP16 compute
- 23.704 TFLOPS
- Software
- CUDA
- Board power
- 180 W
- PCIe generation
- Gen 5
- PCIe lanes
- x8
Compare with
Questions
- How much memory does the NVIDIA GeForce RTX 5060 Ti 8GB have for AI models?
- The NVIDIA GeForce RTX 5060 Ti 8GB 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 Ti 8GB can run locally?
- Gemma 4 12B (Q6_K) fits fully in its memory at 128k context and generates at about 23 tok/s, an estimate from its memory bandwidth. 21 of the 129 models we track fit at that context.
- Can the NVIDIA GeForce RTX 5060 Ti 8GB 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 GeForce RTX 5060 Ti 8GB and system RAM can hold.
- How much does the NVIDIA GeForce RTX 5060 Ti 8GB cost in Canada?
- The best new price for the NVIDIA GeForce RTX 5060 Ti 8GB in Canada is $732 at Amazon.
Speeds on this page are for 128k context. See the best hardware for local AI at every budget.