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 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 15 tok/s (estimated). The best new price for the NVIDIA GeForce RTX 4060 Ti 8GB in Germany is 425 € at Amazon; used, it starts at 450 € (asking price).
Updated . Speeds are estimates; how they are worked out.
Prices
Every retailer we track in Germany, cheapest first. Buy links may earn us a commission.
| Retailer | Condition | Price | Stock | Checked | Buy |
|---|---|---|---|---|---|
| AmazonINNO3D Geforce RTX 4060 Ti 8GB Twin X2 OC Graphics Card | New | 425 € | In stock | 5 h ago | Buy |
| eBayMSI GeForce RTX 4060 Ti Ventus 2X Black 8G OC | used, asking price | 450 €+ 6 € delivery | In stock | 42 min ago | Buy |
| eBayKFA2 GeForce RTX 4060 Ti 8GB GDDR6 Grafikkarte | used, asking price | 500 €+ 7 € delivery | In stock | 42 min ago | Buy |
| eBayUsed GeForce RTX 4060 Ti 8GB on eBay | used, asking price | 569 € | In stock | 29 min ago | Buy |
| eBayKFA2 NIVDIA GeForce RTX 4060 Ti 8GB | used, asking price | 569 €+ 8 € delivery | In stock | 42 min ago | Buy |
| eBayASUS Dual NVIDIA GeForce RTX 4060 Ti 8GB GDDR6 PCIe x16 DUAL-RTX4060TI-O8G | used, asking price | 581 €+ 8 € delivery | In stock | 42 min ago | Buy |
| AmazonASUS Dual GeForce RTX 4060 Ti OC Edition 8 GB GDDR6 Gaming Graphics Card | New | 622 € | In stock | 4 h ago | Buy |
| AmazonASUS Dual GeForce RTX 4060 Ti EVO White OC Edition 8GB GDDR6 Gaming Graphics… | New | 625 € | In stock | 4 h ago | Buy |
| eBayAsus Dual GeForce RTX 4060 Ti EVO OC Edition 8 GB GDDR6 Grafikkarte | used, asking price | 625 € | In stock | 42 min ago | Buy |
| AmazonMSI GeForce RTX 4060 Ti Ventus 2X Black 8G OC Graphics Card | New | 993 € | In stock | 5 h ago | Buy |
| AmazonASUS Dual GeForce RTX 4060 Ti EVO OC Edition 8GB GDDR6 Gaming Graphics Card | New | 650 € | Out of stock | 5 h ago | Buy |
Used on eBay: median asking price 569 €, most between 475 € and 585 € (7 listings, 2026-11-04). Used price history
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 |
|---|---|---|---|---|---|---|---|
| Gemma 4 12B | Q6_K | fits | 13.3 GB | 198k256k with q8 cache | 15tok/sest. | 299 tok/s | chat |
| Qwen 3.5 9B Alibaba Qwen | Q8_0 | fits | 14.8 GB | 128k212k with q8 cache | 13tok/sest. | 356 tok/s | chat |
| Qwen 3.5 4B Alibaba Qwen | Q8_0 | fits | 10.1 GB | 255k256k with q8 cache | 20tok/sest. | 554 tok/s | chat, coding, long documents |
| Gemma 4 E4B | Q8_0 | fits | 13.0 GB | 128k | 15tok/sest. | 499 tok/s | chat |
| LFM2.5 8B-A1B Liquid AI | UD-Q6_K | fits | 14.5 GB | 125k | 15tok/sest. | 667 tok/s | chat, coding |
| Qwen2.5 0.5B Alibaba Qwen | Q8_0 | fits | 3.8 GB | 32k | 83tok/sest. | 1499 tok/s | chat, coding, long documents |
| Qwen3.5 0.8B Alibaba Qwen | Q8_0 | fits | 4.1 GB | 256k | 73tok/sest. | 1999 tok/s | chat, coding, agents, long documents |
| Qwen3.5 2B Alibaba Qwen | Q5_K_M | fits | 4.7 GB | 256k | 58tok/sest. | 1278 tok/s | chat, coding, agents, long documents |
| LFM3 1.7B Liquid AI | Q8_0 | fits | 5.1 GB | 256k | 52tok/sest. | 1478 tok/s | chat, coding, agents, long documents |
| DeepSeek R1 Distill Qwen 1.5B DeepSeek | Q8_0 | fits | 7.1 GB | 128k | 32tok/sest. | 670 tok/s | chat, coding, long documents |
| Mellum2.1 12B A2.5B Thinking JetBrains | Q6_K | fits | 14.3 GB | 128k | 25tok/sest. | 772 tok/s | chat, coding, agents, long documents |
| Spark X2.5 4B XHToken | mlx-4bit | fits | 9.2 GB | 263k441k with q8 cache | 23tok/sest. | 548 tok/s | chat, coding, agents, long documents |
| MiniCPM5 2B openbmb | Q8_0 | fits | 9.6 GB | 128k | 22tok/sest. | 360 tok/s | chat, coding, agents, long documents |
| FrogNano 4B 2609 Microsoft | Q8_0 | fits | 10.2 GB | 251k256k with q8 cache | 20tok/sest. | 554 tok/s | chat, coding, agents, long documents |
| LFM2.5 2.6B Liquid AI | Q8_0 | fits | 12.0 GB | 128k | 16tok/sest. | 462 tok/s | chat, coding, agents, long documents |
| LFM2.5 VL 3B Liquid AI | Q8_0 | fits | 12.0 GB | 32k | 16tok/sest. | 444 tok/s | chat, coding, agents, long documents |
| GLM 4.6V Flash Z.ai | Q4_K_M | fits | 12.6 GB | 128k | 16tok/sest. | 156 tok/s | chat, coding |
| granite vision 4.1 4b IBM | Q5_K_M | fits | 14.1 GB | 128k | 14tok/sest. | 285 tok/s | chat, coding |
| GLM 4 9B 0414 Z.ai | mlx-5bit | fits | 14.5 GB | 32k | 13tok/sest. | 161 tok/s | chat |
| DeepSeek R1 Distill Qwen 7B DeepSeek | Q6_K | fits | 14.7 GB | 128k | 13tok/sest. | 239 tok/s | chat, coding |
| granite 4.2 3b IBM | Q6_K | fits | 14.8 GB | 128k | 13tok/sest. | 291 tok/s | chat, coding, agents |
| Qwen 3.8 27B Alibaba Qwen | UD-Q4_K_M | offload | 25.3 GB | – | 4tok/sest. | 89 tok/s | – |
| Qwen 3.6 27B Alibaba Qwen | Q4_K_M | offload | 25.7 GB | – | 3tok/sest. | 89 tok/s | – |
| Gemma 4 31B | Q4_K_M | offload | 29.7 GB | – | 3tok/sest. | 79 tok/s | – |
| Qwen 3.6 35B-A3B Alibaba Qwen | MXFP4 | offload | 24.6 GB | – | 12tok/sest. | 440 tok/s | chat, coding |
| Qwen 3.5 27B Alibaba Qwen | Q4_K_M | offload | 25.6 GB | – | 3tok/sest. | 89 tok/s | – |
| Gemma 4 26B-A4B | MXFP4 | offload | 20.0 GB | – | 12tok/sest. | 597 tok/s | chat, coding |
| Qwen 3.5 35B-A3B Alibaba Qwen | Q4_K_M | offload | 24.9 GB | – | 12tok/sest. | 437 tok/s | chat, coding |
| GLM 4.7 Flash Z.ai | Q4_K_M | offload | 25.5 GB | – | 5tok/sest. | 204 tok/s | chat |
| Devstral Small 2 24B Mistral AI | Q4_K_M | offload | 35.2 GB | 2k3k with q8 cache | 2tok/sest. | 69 tok/s | – |
| Nemotron 3.5 Lightning 30B-A3B NVIDIA | MXFP4 | offload | 24.3 GB | – | 18tok/sest. | 596 tok/s | chat, coding, long documents |
| gpt-oss 20B OpenAI | Q4_K_M | offload | 15.7 GB | 97k128k with q8 cache | 17tok/sest. | 448 tok/s | chat, coding, agents, long documents |
| Kimi Linear 48B-A3B Moonshot AI | Q4_K_M | offload | 30.9 GB | – | 16tok/sest. | 297 tok/s | chat, coding |
| Qwen3 Coder 30B-A3B Alibaba Qwen | Q4_K_M | offload | 31.1 GB | – | 3tok/sest. | 115 tok/s | chat |
| Gemma 3 27B | Q4_K_M | offload | 27.7 GB | – | 3tok/sest. | 105 tok/s | – |
| Mistral Small 3.2 24B Mistral AI | Q4_K_M | offload | 35.2 GB | 2k3k with q8 cache | 2tok/sest. | 69 tok/s | – |
| Ministral 3 14B Mistral AI | Q4_K_M | offload | 29.5 GB | 37k67k with q8 cache | 3tok/sest. | 95 tok/s | chat |
| Qwen3 14B Alibaba Qwen | Q4_K_M | offload | 30.2 GB | 32k40k with q8 cache | 3tok/sest. | 80 tok/s | chat |
| DeepSeek R1 0528 Qwen3 8B DeepSeek | Q4_K_M | offload | 24.5 GB | 61k111k with q8 cache | 4tok/sest. | 134 tok/s | chat |
| Qwen3 8B Alibaba Qwen | Q4_K_M | offload | 24.5 GB | 40k | 4tok/sest. | 134 tok/s | chat |
| Phi-4 14B Microsoft | Q4_K_M | offload | 35.1 GB | 16k | 2tok/sest. | 76 tok/s | chat |
| Ministral 3 8B Mistral AI | Q4_K_M | offload | 23.7 GB | 63k115k with q8 cache | 4tok/sest. | 138 tok/s | chat |
| Granite 4.2 8B IBM | Q4_K_M | offload | 27.0 GB | 52k96k with q8 cache | 3tok/sest. | 116 tok/s | chat |
| Llama 3.1 8B Meta | Q4_K_M | offload | 22.4 GB | 69k126k with q8 cache | 4tok/sest. | 153 tok/s | chat |
| Kimi VL A3B Thinking 2506 Moonshot AI | Q4_K_M | offload | 15.5 GB | 109k128k with q8 cache | 16tok/sest. | 470 tok/s | chat, coding, long documents |
| Kimi VL A3B Moonshot AI | mlx-5bit | offload | 16.7 GB | 75k123k with q8 cache | 13tok/sest. | 331 tok/s | chat, coding, agents |
| Ministral 3 3B Instruct 2512 Mistral AI | Q4_K_M | offload | 16.8 GB | 108k195k with q8 cache | 8tok/sest. | 256 tok/s | chat, coding, agents |
| GLM 4.1V 9B Thinking Z.ai | mlx-8bit | offload | 17.8 GB | 60k64k with q8 cache | 7tok/sest. | 139 tok/s | – |
| Muse Glimmer 30B meta-models | Q4_K_M | offload | 19.7 GB | – | 6tok/sest. | 111 tok/s | – |
| DeepSeek R1 Distill Llama 8B DeepSeek | Q4_K_M | offload | 22.4 GB | 69k126k with q8 cache | 4tok/sest. | 153 tok/s | chat |
| LFM2.5 24B A2B Liquid AI | Q4_K_M | offload | 26.1 GB | – | 4tok/sest. | 281 tok/s | chat |
| Ministral 3 8B Reasoning 2512 Mistral AI | Q4_K_M | offload | 23.7 GB | 63k115k with q8 cache | 4tok/sest. | 138 tok/s | chat |
| Ministral 3 14B Reasoning 2512 Mistral AI | Q4_K_M | offload | 29.5 GB | 37k67k with q8 cache | 3tok/sest. | 95 tok/s | chat |
| Qwen3.8 27B OBLITERATED OBLITERATUS | mlx-4bit | offload | 29.2 GB | – | 3tok/sest. | 84 tok/s | – |
| DeepSeek R1 Distill Qwen 14B DeepSeek | Q4_K_M | offload | 34.2 GB | 27k50k with q8 cache | 2tok/sest. | 68 tok/s | chat |
| Devstral Small 2507 Mistral AI | Q4_K_M | offload | 35.2 GB | 2k3k with q8 cache | 2tok/sest. | 70 tok/s | – |
| Magistral Small 2506 Mistral AI | Q4_K_M | offload | 35.2 GB | 2k3k with q8 cache | 2tok/sest. | 70 tok/s | – |
| Magistral Small 2507 Mistral AI | Q4_K_M | offload | 35.2 GB | 2k3k with q8 cache | 2tok/sest. | 70 tok/s | – |
| Magistral Small 2509 Mistral AI | Q4_K_M | offload | 35.2 GB | 2k3k with q8 cache | 2tok/sest. | 69 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 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?
- Gemma 4 12B (Q6_K) fits fully in its memory at 128k context and generates at about 15 tok/s, an estimate from its memory bandwidth. 21 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 103 GB at 128k 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 Germany?
- The best new price for the NVIDIA GeForce RTX 4060 Ti 8GB in Germany is 425 € at Amazon; used, it starts at 450 € (asking price).
Speeds on this page are for 128k context. See the best hardware for local AI at every budget.