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 64k context it runs 11 of the 129 open models we track fully in memory; the most capable is Qwen 3.5 4B at Q8_0, at about 27 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 | 5 h ago | Buy |
| eBayMSI GeForce RTX 4060 Ti Ventus 2X Black OC 8GB GDDR6 Graphics Card | used, asking price | $766+ $26 delivery | In stock | 2 h ago | Buy |
| AmazonASUS Dual GeForce RTX 4060 Ti EVO OC Edition 8GB GDDR6 | New | $864 | In stock | 4 h ago | Buy |
| AmazonPNY GeForce RTX 4060 Ti 8GB XLR8 Gaming Verto RGB Triple Fan OC Graphics Card… | New | $902+ $23 delivery | In stock | 4 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 it runs
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 |
|---|---|---|---|---|---|---|---|
| Qwen 3.5 4B Alibaba Qwen | Q8_0 | fits | 7.3 GB | 66k110k with q8 cache | 27tok/sest. | 729 tok/s | chat, coding |
| Qwen2.5 0.5B Alibaba Qwen | Q8_0 | fits | 2.3 GB | 32k | 134tok/sest. | 2608 tok/s | chat, coding, long documents |
| Qwen3.5 0.8B Alibaba Qwen | Q8_0 | fits | 2.6 GB | 256k | 109tok/sest. | 2957 tok/s | chat, coding, agents, long documents |
| Qwen3.5 2B Alibaba Qwen | Q5_K_M | fits | 3.2 GB | 256k | 79tok/sest. | 1612 tok/s | chat, coding, agents, long documents |
| LFM3 1.7B Liquid AI | Q8_0 | fits | 3.6 GB | 256k | 67tok/sest. | 1945 tok/s | chat, coding, agents, long documents |
| DeepSeek R1 Distill Qwen 1.5B DeepSeek | Q8_0 | fits | 4.6 GB | 128k | 47tok/sest. | 1080 tok/s | chat, coding, long documents |
| MiniCPM5 2B openbmb | Q8_0 | fits | 6.2 GB | 89k128k with q8 cache | 33tok/sest. | 609 tok/s | chat, coding, agents |
| Spark X2.5 4B XHToken | mlx-4bit | fits | 6.2 GB | 93k156k with q8 cache | 32tok/sest. | 748 tok/s | chat, coding, agents, long documents |
| FrogNano 4B 2609 Microsoft | Q6_K | fits | 6.6 GB | 84k140k with q8 cache | 30tok/sest. | 729 tok/s | chat, coding, agents |
| LFM2.5 2.6B Liquid AI | Q6_K | fits | 6.9 GB | 71k125k with q8 cache | 29tok/sest. | 738 tok/s | chat, coding, agents |
| LFM2.5 VL 3B Liquid AI | Q6_K | fits | 6.9 GB | 32k | 29tok/sest. | 694 tok/s | chat, coding, agents |
| Qwen 3.8 27B Alibaba Qwen | UD-Q4_K_M | offload | 20.5 GB | – | 3tok/sest. | 86 tok/s | – |
| Qwen 3.6 27B Alibaba Qwen | Q4_K_M | offload | 20.9 GB | – | 3tok/sest. | 86 tok/s | – |
| Gemma 4 31B | Q4_K_M | offload | 23.9 GB | – | 3tok/sest. | 75 tok/s | – |
| Qwen 3.6 35B-A3B Alibaba Qwen | MXFP4 | offload | 22.6 GB | – | 13tok/sest. | 503 tok/s | chat |
| Qwen 3.5 27B Alibaba Qwen | Q4_K_M | offload | 20.8 GB | – | 3tok/sest. | 86 tok/s | – |
| Gemma 4 26B-A4B | MXFP4 | offload | 17.9 GB | – | 11tok/sest. | 559 tok/s | chat |
| Qwen 3.5 35B-A3B Alibaba Qwen | Q4_K_M | offload | 22.9 GB | – | 13tok/sest. | 501 tok/s | chat |
| GLM 4.7 Flash Z.ai | Q4_K_M | offload | 21.4 GB | – | 6tok/sest. | 254 tok/s | – |
| Devstral Small 2 24B Mistral AI | Q4_K_M | offload | 24.4 GB | – | 3tok/sest. | 80 tok/s | – |
| Nemotron 3.5 Lightning 30B-A3B NVIDIA | MXFP4 | offload | 23.1 GB | – | 15tok/sest. | 616 tok/s | chat |
| Kimi Linear 48B-A3B Moonshot AI | Q4_K_M | offload | 29.6 GB | – | 16tok/sest. | 374 tok/s | chat |
| gpt-oss 20B OpenAI | Q4_K_M | offload | 13.4 GB | – | 13tok/sest. | 473 tok/s | chat |
| Qwen3 Coder 30B-A3B Alibaba Qwen | Q4_K_M | offload | 24.4 GB | – | 5tok/sest. | 176 tok/s | chat |
| Gemma 4 12B | Q4_K_M | offload | 9.0 GB | – | 14tok/sest. | 302 tok/s | chat |
| Qwen 3.5 9B Alibaba Qwen | Q4_K_M | offload | 8.4 GB | 36k60k with q8 cache | 17tok/sest. | 386 tok/s | chat, coding |
| Gemma 3 27B | Q4_K_M | offload | 21.9 GB | – | 3tok/sest. | 95 tok/s | – |
| Mistral Small 3.2 24B Mistral AI | Q4_K_M | offload | 24.4 GB | – | 3tok/sest. | 80 tok/s | – |
| Ministral 3 14B Mistral AI | Q4_K_M | offload | 18.7 GB | – | 4tok/sest. | 124 tok/s | – |
| Qwen3 14B Alibaba Qwen | Q4_K_M | offload | 19.5 GB | – | 4tok/sest. | 107 tok/s | – |
| DeepSeek R1 0528 Qwen3 8B DeepSeek | Q4_K_M | offload | 14.8 GB | 13k25k with q8 cache | 5tok/sest. | 188 tok/s | chat |
| Qwen3 8B Alibaba Qwen | Q4_K_M | offload | 14.8 GB | 13k25k with q8 cache | 5tok/sest. | 188 tok/s | chat |
| Phi-4 14B Microsoft | Q4_K_M | offload | 21.9 GB | – | 3tok/sest. | 103 tok/s | – |
| Ministral 3 8B Mistral AI | Q4_K_M | offload | 14.4 GB | 13k24k with q8 cache | 6tok/sest. | 188 tok/s | chat |
| Granite 4.2 8B IBM | Q4_K_M | offload | 16.2 GB | 9k17k with q8 cache | 5tok/sest. | 165 tok/s | chat |
| Gemma 4 E4B | Q4_K_M | offload | 7.5 GB | 61k100k with q8 cache | 25tok/sest. | 546 tok/s | chat, coding |
| LFM2.5 8B-A1B Liquid AI | UD-Q4_K_M | offload | 9.0 GB | 33k56k with q8 cache | 19tok/sest. | 1031 tok/s | chat, coding |
| Llama 3.1 8B Meta | Q4_K_M | offload | 13.7 GB | 16k29k with q8 cache | 6tok/sest. | 210 tok/s | chat |
| Mellum2.1 12B A2.5B Thinking JetBrains | Q4_K_M | offload | 10.2 GB | – | 22tok/sest. | 889 tok/s | chat, coding |
| granite vision 4.1 4b IBM | Q4_K_M | offload | 8.0 GB | 56k100k with q8 cache | 20tok/sest. | 438 tok/s | chat, coding |
| granite 4.2 3b IBM | Q4_K_M | offload | 8.2 GB | 54k95k with q8 cache | 18tok/sest. | 445 tok/s | chat, coding, agents |
| DeepSeek R1 Distill Qwen 7B DeepSeek | Q4_K_M | offload | 8.9 GB | 38k66k with q8 cache | 15tok/sest. | 309 tok/s | chat, coding |
| GLM 4.6V Flash Z.ai | Q4_K_M | offload | 9.3 GB | 21k35k with q8 cache | 13tok/sest. | 203 tok/s | chat, coding |
| Kimi VL A3B Thinking 2506 Moonshot AI | Q4_K_M | offload | 12.8 GB | – | 13tok/sest. | 543 tok/s | chat |
| Ministral 3 3B Instruct 2512 Mistral AI | Q4_K_M | offload | 9.6 GB | 43k78k with q8 cache | 12tok/sest. | 388 tok/s | chat, coding, agents |
| Kimi VL A3B Moonshot AI | mlx-5bit | offload | 14.0 GB | – | 12tok/sest. | 408 tok/s | chat |
| GLM 4 9B 0414 Z.ai | mlx-4bit | offload | 10.2 GB | 2k4k with q8 cache | 11tok/sest. | 202 tok/s | chat |
| LFM2.5 24B A2B Liquid AI | Q4_K_M | offload | 20.4 GB | – | 6tok/sest. | 402 tok/s | chat |
| DeepSeek R1 Distill Llama 8B DeepSeek | Q4_K_M | offload | 13.7 GB | 16k29k with q8 cache | 6tok/sest. | 210 tok/s | chat |
| Ministral 3 8B Reasoning 2512 Mistral AI | Q4_K_M | offload | 14.4 GB | 13k24k with q8 cache | 6tok/sest. | 188 tok/s | chat |
| GLM 4.1V 9B Thinking Z.ai | mlx-8bit | offload | 14.5 GB | – | 6tok/sest. | 161 tok/s | – |
| Muse Glimmer 30B meta-models | Q4_K_M | offload | 18.1 GB | – | 4tok/sest. | 91 tok/s | – |
| Ministral 3 14B Reasoning 2512 Mistral AI | Q4_K_M | offload | 18.7 GB | – | 4tok/sest. | 124 tok/s | – |
| DeepSeek R1 Distill Qwen 14B DeepSeek | Q4_K_M | offload | 21.4 GB | – | 3tok/sest. | 94 tok/s | – |
| Devstral Small 2507 Mistral AI | Q4_K_M | offload | 24.4 GB | – | 3tok/sest. | 81 tok/s | – |
| Magistral Small 2506 Mistral AI | Q4_K_M | offload | 24.4 GB | – | 3tok/sest. | 81 tok/s | – |
| Magistral Small 2507 Mistral AI | Q4_K_M | offload | 24.4 GB | – | 3tok/sest. | 81 tok/s | – |
| Magistral Small 2509 Mistral AI | Q4_K_M | offload | 24.4 GB | – | 3tok/sest. | 80 tok/s | – |
| Qwen3.8 27B OBLITERATED OBLITERATUS | mlx-4bit | offload | 24.5 GB | – | 3tok/sest. | 82 tok/s | – |
| DeepSeek V4 Flash DeepSeek | UD-Q2_K_XL | won't run | 96.6 GB | – | – | – | – |
| GLM 5.3 Flash Z.ai | UD-Q2_K_XL | won't run | 103.1 GB | – | – | – | – |
| Qwen 3.8 Flash Next Alibaba Qwen | UD-Q2_K_XL | won't run | 76.1 GB | – | – | – | – |
| MiniMax M2.7 MiniMax | UD-Q2_K_XL | won't run | 86.7 GB | – | – | – | – |
| Qwen 3.5 122B-A10B Alibaba Qwen | UD-Q2_K_XL | won't run | 41.7 GB | – | – | – | – |
| Nemotron 3 Super 120B-A12B NVIDIA | UD-Q2_K_XL | won't run | 52.6 GB | – | – | – | – |
| Qwen3 Coder Next Alibaba Qwen | Q4_K_M | won't run | 47.8 GB | – | – | – | – |
| gpt-oss 120B OpenAI | Q4_K_M | won't run | 61.8 GB | – | – | – | – |
| Mistral Small 4 119B Mistral AI | UD-Q2_K_XL | won't run | 39.9 GB | – | – | – | – |
| GLM 4.5 Air Z.ai | Q2_K | won't run | 54.8 GB | – | – | – | – |
| Qwen3 Next 80B-A3B Alibaba Qwen | Q4_K_M | won't run | 47.8 GB | – | – | – | – |
| Qwen3 32B Alibaba Qwen | Q4_K_M | won't run | 35.5 GB | – | – | – | – |
| DeepSeek R1 Distill Qwen 32B DeepSeek | Q4_K_M | won't run | 35.6 GB | – | – | – | – |
| Llama 3.3 70B Meta | Q4_K_M | won't run | 60.7 GB | – | – | – | – |
| DeepSeek R1 DeepSeek | Q4_K_M | won't run | 382.0 GB | – | – | – | – |
| DeepSeek R1 0528 DeepSeek | Q4_K_M | won't run | 382.5 GB | – | – | – | – |
| DeepSeek R1 Distill Llama 70B DeepSeek | Q4_K_M | won't run | 60.7 GB | – | – | – | – |
| DeepSeek R1 Zero DeepSeek | Q4_K_M | won't run | 382.0 GB | – | – | – | – |
| DeepSeek V3 DeepSeek | Q4_K_M | won't run | 382.0 GB | – | – | – | – |
| DeepSeek V3 0324 DeepSeek | Q4_K_M | won't run | 382.0 GB | – | – | – | – |
| DeepSeek V3.1 DeepSeek | Q4_K_M | won't run | 382.9 GB | – | – | – | – |
| DeepSeek V3.1 Terminus DeepSeek | Q4_K_M | won't run | 382.9 GB | – | – | – | – |
| DeepSeek V3.2 DeepSeek | Q4_K_M | won't run | 382.9 GB | – | – | – | – |
| DeepSeek V4 Flash 0731 DeepSeek | MXFP4 | won't run | 152.1 GB | – | – | – | – |
| DeepSeek V4 Flash Vision Exp DeepSeek | MXFP4 | won't run | 160.9 GB | – | – | – | – |
| Devstral 2 123B Instruct 2512 Mistral AI | Q4_K_M | won't run | 92.8 GB | – | – | – | – |
| GLM 4.5 Z.ai | Q4_K_M | won't run | 225.7 GB | – | – | – | – |
| GLM 4.5V Z.ai | Q4_K_M | won't run | 71.8 GB | – | – | – | – |
| GLM 4.6 Z.ai | Q4_K_M | won't run | 224.9 GB | – | – | – | – |
| GLM 4.6V Z.ai | Q4_K_M | won't run | 78.2 GB | – | – | – | – |
| GLM 4.7 Z.ai | Q4_K_M | won't run | 225.7 GB | – | – | – | – |
| GLM 5 Z.ai | MXFP4 | won't run | 388.9 GB | – | – | – | – |
| GLM 5.1 Z.ai | MXFP4 | won't run | 426.5 GB | – | – | – | – |
| GLM 5.2 Z.ai | UD-Q4_K_M | won't run | 440.4 GB | – | – | – | – |
| GLM 5.3 Z.ai | Q8_0 | won't run | 752.9 GB | – | – | – | – |
| Kimi Dev 72B Moonshot AI | Q6_K | won't run | 81.0 GB | – | – | – | – |
| Kimi K2 Moonshot AI | Q4_K_M | won't run | 583.5 GB | – | – | – | – |
| Kimi K2 Instruct 0905 Moonshot AI | Q4_K_M | won't run | 583.9 GB | – | – | – | – |
| Kimi K2 Thinking Moonshot AI | Q4_K_M | won't run | 583.9 GB | – | – | – | – |
| Kimi K2.5 Moonshot AI | Q4_K_M | won't run | 583.9 GB | – | – | – | – |
| MiniMax M2 MiniMax | Q4_K_M | won't run | 145.4 GB | – | – | – | – |
| MiniMax M2.1 MiniMax | Q4_K_M | won't run | 145.4 GB | – | – | – | – |
| MiniMax M2.5 MiniMax | MXFP4 | won't run | 131.8 GB | – | – | – | – |
| MiniMax M3 MiniMax | MXFP4 | won't run | 248.3 GB | – | – | – | – |
| Mistral Medium 3.5 128B Mistral AI | Q4_K_M | won't run | 92.8 GB | – | – | – | – |
| granite 4.2 30b IBM | Q4_K_M | won't run | 33.9 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?
- Qwen 3.5 4B (Q8_0) fits fully in its memory at 64k context and generates at about 27 tok/s, an estimate from its memory bandwidth. 11 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 97 GB at 64k 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 64k context. See the best hardware for local AI at every budget.