NVIDIA GeForce RTX 4060 Ti 8GB
8 GB · 288 GB/s · 160 W
In short
The NVIDIA GeForce RTX 4060 Ti 8GB has 8 GB of GDDR6 memory with 288 GB/s of bandwidth, which sets how fast it generates text. At 8k context it runs 38 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 4060 Ti 8GB in Canada is $864 at Amazon; used, it starts at $650 (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 |
|---|---|---|---|---|---|
| eBayUsed GeForce RTX 4060 Ti 8GB on eBay | used, asking price | $650 | In stock | 6 h ago | Buy |
| eBayMSI GeForce RTX 4060 Ti Ventus 2X Black OC 8GB GDDR6 Graphics Card | used, asking price | $766+ $26 delivery | In stock | 3 h ago | Buy |
| AmazonASUS Dual GeForce RTX 4060 Ti EVO OC Edition 8GB GDDR6 | New | $864 | In stock | 5 h ago | Buy |
| AmazonPNY GeForce RTX 4060 Ti 8GB XLR8 Gaming Verto RGB Triple Fan OC Graphics Card… | New | $902+ $23 delivery | In stock | 5 h ago | Buy |
Used on eBay: median asking price $650, most between $650 and $766 (5 listings, 2026-11-03). Used price history
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 @8k | Prefill | Good for |
|---|---|---|---|---|---|---|---|
| gpt-oss 20B OpenAI | MXFP4 | fits | 12.6 GB | 82k128k with q8 cache | 41tok/sest. | 1077 tok/s | chat, coding, agents, long documents |
| Gemma 4 12B | Q8_0 | fits | 13.4 GB | 73k122k with q8 cache | 14tok/sest. | 395 tok/s | – |
| Qwen 3.5 9B Alibaba Qwen | Q8_0 | fits | 10.3 GB | 128k212k with q8 cache | 18tok/sest. | 500 tok/s | chat |
| Ministral 3 14B Mistral AI | Q6_K | fits | 12.7 GB | 20k37k with q8 cache | 14tok/sest. | 325 tok/s | – |
| Qwen3 14B Alibaba Qwen | Q6_K | fits | 13.7 GB | 14k27k with q8 cache | 13tok/sest. | 302 tok/s | – |
| DeepSeek R1 0528 Qwen3 8B DeepSeek | Q8_0 | fits | 10.4 GB | 38k69k with q8 cache | 18tok/sest. | 528 tok/s | chat |
| Qwen3 8B Alibaba Qwen | Q8_0 | fits | 10.4 GB | 38k40k with q8 cache | 18tok/sest. | 528 tok/s | chat |
| Phi-4 14B Microsoft | Q6_K | fits | 13.9 GB | 12k16k with q8 cache | 13tok/sest. | 304 tok/s | – |
| Qwen 3.5 4B Alibaba Qwen | Q8_0 | fits | 5.6 GB | 255k256k with q8 cache | 37tok/sest. | 1007 tok/s | chat, coding, long documents |
| Ministral 3 8B Mistral AI | Q8_0 | fits | 10.6 GB | 38k69k with q8 cache | 18tok/sest. | 494 tok/s | chat |
| Granite 4.2 8B IBM | Q8_0 | fits | 11.1 GB | 30k56k with q8 cache | 17tok/sest. | 490 tok/s | chat |
| Gemma 4 E4B | Q8_0 | fits | 9.0 GB | 128k | 21tok/sest. | 606 tok/s | chat |
| LFM2.5 8B-A1B Liquid AI | Q8_0 | fits | 9.9 GB | 100k125k with q8 cache | 73tok/sest. | 3540 tok/s | chat, coding |
| Llama 3.1 8B Meta | Q6_K | fits | 8.3 GB | 57k104k with q8 cache | 23tok/sest. | 545 tok/s | chat |
| Qwen2.5 0.5B Alibaba Qwen | Q8_0 | fits | 1.7 GB | 32k | 283tok/sest. | 7408 tok/s | chat, coding, long documents |
| Qwen3.5 0.8B Alibaba Qwen | Q8_0 | fits | 2.0 GB | 256k | 192tok/sest. | 5097 tok/s | chat, coding, agents, long documents |
| Qwen3.5 2B Alibaba Qwen | Q5_K_M | fits | 2.6 GB | 256k | 115tok/sest. | 2091 tok/s | chat, coding, agents, long documents |
| LFM3 1.7B Liquid AI | Q8_0 | fits | 3.0 GB | 256k | 91tok/sest. | 2686 tok/s | chat, coding, agents, long documents |
| DeepSeek R1 Distill Qwen 1.5B DeepSeek | Q8_0 | fits | 3.1 GB | 128k | 84tok/sest. | 2332 tok/s | chat, coding, long documents |
| MiniCPM5 2B openbmb | Q8_0 | fits | 4.0 GB | 128k | 59tok/sest. | 1541 tok/s | chat, coding, agents, long documents |
| LFM2.5 2.6B Liquid AI | Q8_0 | fits | 4.3 GB | 128k | 53tok/sest. | 1551 tok/s | chat, coding, agents, long documents |
| LFM2.5 VL 3B Liquid AI | Q8_0 | fits | 4.3 GB | 32k | 53tok/sest. | 1369 tok/s | chat, coding, agents, long documents |
| Spark X2.5 4B XHToken | mlx-4bit | fits | 4.3 GB | 263k441k with q8 cache | 53tok/sest. | 1097 tok/s | chat, coding, agents, long documents |
| Kimi VL A3B Thinking 2506 Moonshot AI | Q4_K_M | fits | 11.2 GB | 109k128k with q8 cache | 49tok/sest. | 1438 tok/s | chat, coding, long documents |
| granite vision 4.1 4b IBM | Q8_0 | fits | 5.1 GB | 122k128k with q8 cache | 42tok/sest. | 1027 tok/s | chat, coding |
| Ministral 3 3B Instruct 2512 Mistral AI | Q8_0 | fits | 5.4 GB | 95k172k with q8 cache | 40tok/sest. | 1052 tok/s | chat, coding |
| granite 4.2 3b IBM | Q8_0 | fits | 5.4 GB | 119k128k with q8 cache | 39tok/sest. | 1104 tok/s | chat, coding |
| Kimi VL A3B Moonshot AI | mlx-6bit | fits | 14.3 GB | 23k37k with q8 cache | 39tok/sest. | 1349 tok/s | chat, coding |
| Mellum2.1 12B A2.5B Thinking JetBrains | Q8_0 | fits | 13.3 GB | 83k125k with q8 cache | 38tok/sest. | 1705 tok/s | chat, coding, agents, long documents |
| FrogNano 4B 2609 Microsoft | Q8_0 | fits | 5.7 GB | 251k256k with q8 cache | 36tok/sest. | 1007 tok/s | chat, coding, agents, long documents |
| DeepSeek R1 Distill Qwen 7B DeepSeek | Q8_0 | fits | 9.1 GB | 101k128k with q8 cache | 21tok/sest. | 588 tok/s | chat |
| GLM 4 9B 0414 Z.ai | mlx-5bit | fits | 9.1 GB | 32k | 21tok/sest. | 462 tok/s | chat |
| DeepSeek R1 Distill Llama 8B DeepSeek | Q8_0 | fits | 10.1 GB | 43k79k with q8 cache | 19tok/sest. | 545 tok/s | chat |
| Ministral 3 8B Reasoning 2512 Mistral AI | Q8_0 | fits | 10.6 GB | 38k69k with q8 cache | 18tok/sest. | 494 tok/s | chat |
| GLM 4.6V Flash Z.ai | Q8_0 | fits | 10.8 GB | 97k128k with q8 cache | 17tok/sest. | 427 tok/s | chat |
| GLM 4.1V 9B Thinking Z.ai | mlx-8bit | fits | 12.4 GB | 60k64k with q8 cache | 15tok/sest. | 427 tok/s | – |
| Ministral 3 14B Reasoning 2512 Mistral AI | Q6_K | fits | 12.7 GB | 20k37k with q8 cache | 14tok/sest. | 325 tok/s | – |
| DeepSeek R1 Distill Qwen 14B DeepSeek | Q6_K | fits | 13.9 GB | 12k22k with q8 cache | 13tok/sest. | 296 tok/s | – |
| Qwen 3.8 27B Alibaba Qwen | UD-Q4_K_M | offload | 17.1 GB | – | 8tok/sest. | 159 tok/s | – |
| Qwen 3.5 122B-A10B Alibaba Qwen | UD-Q2_K_XL | offload | 40.4 GB | – | 12tok/sest. | 280 tok/s | – |
| Qwen 3.6 27B Alibaba Qwen | Q4_K_M | offload | 17.4 GB | – | 7tok/sest. | 157 tok/s | – |
| Gemma 4 31B | Q4_K_M | offload | 19.6 GB | – | 6tok/sest. | 128 tok/s | – |
| Mistral Small 4 119B Mistral AI | UD-Q2_K_XL | offload | 38.8 GB | – | 17tok/sest. | 371 tok/s | chat |
| Qwen 3.6 35B-A3B Alibaba Qwen | MXFP4 | offload | 21.6 GB | – | 30tok/sest. | 1192 tok/s | chat, coding |
| Qwen 3.5 27B Alibaba Qwen | Q4_K_M | offload | 17.4 GB | – | 8tok/sest. | 157 tok/s | – |
| Gemma 4 26B-A4B | MXFP4 | offload | 16.9 GB | – | 28tok/sest. | 1043 tok/s | chat, coding |
| Qwen 3.5 35B-A3B Alibaba Qwen | Q4_K_M | offload | 21.9 GB | – | 29tok/sest. | 1184 tok/s | chat, coding |
| GLM 4.7 Flash Z.ai | Q4_K_M | offload | 18.6 GB | – | 20tok/sest. | 724 tok/s | chat |
| Devstral Small 2 24B Mistral AI | Q4_K_M | offload | 15.7 GB | 2k3k with q8 cache | 10tok/sest. | 188 tok/s | – |
| Nemotron 3.5 Lightning 30B-A3B NVIDIA | MXFP4 | offload | 22.9 GB | – | 25tok/sest. | 1206 tok/s | chat, coding, long documents |
| Qwen3 Coder 30B-A3B Alibaba Qwen | Q4_K_M | offload | 19.2 GB | – | 26tok/sest. | 920 tok/s | chat |
| Kimi Linear 48B-A3B Moonshot AI | Q4_K_M | offload | 29.2 GB | – | 25tok/sest. | 978 tok/s | chat, coding |
| Qwen3 32B Alibaba Qwen | Q4_K_M | offload | 21.6 GB | – | 5tok/sest. | 107 tok/s | – |
| Mistral Small 3.2 24B Mistral AI | Q4_K_M | offload | 15.7 GB | 2k3k with q8 cache | 10tok/sest. | 188 tok/s | – |
| Gemma 3 27B | Q4_K_M | offload | 17.6 GB | – | 7tok/sest. | 158 tok/s | – |
| DeepSeek R1 Distill Qwen 32B DeepSeek | Q4_K_M | offload | 21.6 GB | – | 5tok/sest. | 112 tok/s | – |
| LFM2.5 24B A2B Liquid AI | Q4_K_M | offload | 16.1 GB | – | 46tok/sest. | 1764 tok/s | chat |
| Devstral Small 2507 Mistral AI | Q4_K_M | offload | 15.7 GB | 2k3k with q8 cache | 10tok/sest. | 192 tok/s | – |
| Magistral Small 2506 Mistral AI | Q4_K_M | offload | 15.7 GB | 2k3k with q8 cache | 10tok/sest. | 192 tok/s | – |
| Magistral Small 2507 Mistral AI | Q4_K_M | offload | 15.7 GB | 2k3k with q8 cache | 10tok/sest. | 192 tok/s | – |
| Magistral Small 2509 Mistral AI | Q4_K_M | offload | 15.7 GB | 2k3k with q8 cache | 10tok/sest. | 188 tok/s | – |
| Muse Glimmer 30B meta-models | Q4_K_M | offload | 17.4 GB | – | 7tok/sest. | 145 tok/s | – |
| granite 4.2 30b IBM | Q4_K_M | offload | 19.9 GB | – | 5tok/sest. | 132 tok/s | – |
| Qwen3.8 27B OBLITERATED OBLITERATUS | mlx-4bit | offload | 21.0 GB | – | 5tok/sest. | 141 tok/s | – |
| DeepSeek V4 Flash DeepSeek | UD-Q2_K_XL | won't run | 92.0 GB | – | – | – | – |
| GLM 5.3 Flash Z.ai | UD-Q2_K_XL | won't run | 102.6 GB | – | – | – | – |
| Qwen 3.8 Flash Next Alibaba Qwen | UD-Q2_K_XL | won't run | 74.9 GB | – | – | – | – |
| MiniMax M2.7 MiniMax | UD-Q2_K_XL | won't run | 73.2 GB | – | – | – | – |
| Nemotron 3 Super 120B-A12B NVIDIA | UD-Q2_K_XL | won't run | 52.3 GB | – | – | – | – |
| Qwen3 Coder Next Alibaba Qwen | Q4_K_M | won't run | 46.6 GB | – | – | – | – |
| gpt-oss 120B OpenAI | Q4_K_M | won't run | 59.9 GB | – | – | – | – |
| GLM 4.5 Air Z.ai | Q2_K | won't run | 44.8 GB | – | – | – | – |
| Qwen3 Next 80B-A3B Alibaba Qwen | Q4_K_M | won't run | 46.6 GB | – | – | – | – |
| Llama 3.3 70B Meta | Q4_K_M | won't run | 43.2 GB | – | – | – | – |
| DeepSeek R1 DeepSeek | Q4_K_M | won't run | 378.3 GB | – | – | – | – |
| DeepSeek R1 0528 DeepSeek | Q4_K_M | won't run | 378.8 GB | – | – | – | – |
| DeepSeek R1 Distill Llama 70B DeepSeek | Q4_K_M | won't run | 43.2 GB | – | – | – | – |
| DeepSeek R1 Zero DeepSeek | Q4_K_M | won't run | 378.3 GB | – | – | – | – |
| DeepSeek V3 DeepSeek | Q4_K_M | won't run | 378.3 GB | – | – | – | – |
| DeepSeek V3 0324 DeepSeek | Q4_K_M | won't run | 378.3 GB | – | – | – | – |
| DeepSeek V3.1 DeepSeek | Q4_K_M | won't run | 379.2 GB | – | – | – | – |
| DeepSeek V3.1 Terminus DeepSeek | Q4_K_M | won't run | 379.2 GB | – | – | – | – |
| DeepSeek V3.2 DeepSeek | Q4_K_M | won't run | 379.2 GB | – | – | – | – |
| DeepSeek V4 Flash 0731 DeepSeek | MXFP4 | won't run | 147.5 GB | – | – | – | – |
| DeepSeek V4 Flash Vision Exp DeepSeek | MXFP4 | won't run | 156.2 GB | – | – | – | – |
| Devstral 2 123B Instruct 2512 Mistral AI | Q4_K_M | won't run | 73.7 GB | – | – | – | – |
| GLM 4.5 Z.ai | Q4_K_M | won't run | 205.6 GB | – | – | – | – |
| GLM 4.5V Z.ai | Q4_K_M | won't run | 61.8 GB | – | – | – | – |
| GLM 4.6 Z.ai | Q4_K_M | won't run | 204.8 GB | – | – | – | – |
| GLM 4.6V Z.ai | Q4_K_M | won't run | 68.2 GB | – | – | – | – |
| GLM 4.7 Z.ai | Q4_K_M | won't run | 205.6 GB | – | – | – | – |
| GLM 5 Z.ai | MXFP4 | won't run | 384.2 GB | – | – | – | – |
| GLM 5.1 Z.ai | MXFP4 | won't run | 421.8 GB | – | – | – | – |
| GLM 5.2 Z.ai | UD-Q4_K_M | won't run | 435.7 GB | – | – | – | – |
| GLM 5.3 Z.ai | Q8_0 | won't run | 748.2 GB | – | – | – | – |
| Kimi Dev 72B Moonshot AI | Q6_K | won't run | 63.6 GB | – | – | – | – |
| Kimi K2 Moonshot AI | Q4_K_M | won't run | 579.8 GB | – | – | – | – |
| Kimi K2 Instruct 0905 Moonshot AI | Q4_K_M | won't run | 580.3 GB | – | – | – | – |
| Kimi K2 Thinking Moonshot AI | Q4_K_M | won't run | 580.3 GB | – | – | – | – |
| Kimi K2.5 Moonshot AI | Q4_K_M | won't run | 580.3 GB | – | – | – | – |
| MiniMax M2 MiniMax | Q4_K_M | won't run | 131.9 GB | – | – | – | – |
| MiniMax M2.1 MiniMax | Q4_K_M | won't run | 131.9 GB | – | – | – | – |
| MiniMax M2.5 MiniMax | MXFP4 | won't run | 118.4 GB | – | – | – | – |
| MiniMax M3 MiniMax | MXFP4 | won't run | 241.8 GB | – | – | – | – |
| Mistral Medium 3.5 128B Mistral AI | Q4_K_M | won't run | 73.7 GB | – | – | – | – |
Specifications
From the manufacturer's published specifications; sources are linked where we have them.
- GPU
- GeForce RTX 4060 Ti 8GB
- Memory
- 8 GB GDDR6
- Memory bus
- 128-bit
- Memory bandwidth
- 288 GB/s
- FP16 compute
- 22.065 TFLOPS
- Software
- CUDA
- Board power
- 160 W
- PCIe generation
- Gen 4
- PCIe lanes
- x8
Compare with
Questions
- How much memory does the NVIDIA GeForce RTX 4060 Ti 8GB have for AI models?
- The NVIDIA GeForce RTX 4060 Ti 8GB has 8 GB of GDDR6 memory with 288 GB/s of bandwidth, which sets how fast it generates text.
- What is the best model the NVIDIA GeForce RTX 4060 Ti 8GB can run locally?
- gpt-oss 20B (MXFP4) fits fully in its memory at 8k context and generates at about 41 tok/s, an estimate from its memory bandwidth. 38 of the 129 models we track fit at that context.
- Can the NVIDIA GeForce RTX 4060 Ti 8GB 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 GeForce RTX 4060 Ti 8GB and system RAM can hold.
- How much does the NVIDIA GeForce RTX 4060 Ti 8GB cost in Canada?
- The best new price for the NVIDIA GeForce RTX 4060 Ti 8GB in Canada is $864 at Amazon; used, it starts at $650 (asking price).
Speeds on this page are for 8k context. See the best hardware for local AI at every budget.