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 32k context it runs 18 of the 129 open models we track fully in memory; the most capable is Qwen 3.5 9B at Q4_K_M, at about 26 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 | 3 h ago | Buy |
| eBayMSI GeForce RTX 4060 Ti Ventus 2X Black 8G OC | used, asking price | 450 €+ 6 € delivery | In stock | 2 h ago | Buy |
| eBayKFA2 GeForce RTX 4060 Ti 8GB GDDR6 Grafikkarte | used, asking price | 500 €+ 7 € delivery | In stock | 2 h ago | Buy |
| eBayUsed GeForce RTX 4060 Ti 8GB on eBay | used, asking price | 569 € | In stock | 5 h ago | Buy |
| eBayKFA2 NIVDIA GeForce RTX 4060 Ti 8GB | used, asking price | 569 €+ 8 € delivery | In stock | 2 h ago | Buy |
| eBayASUS Dual NVIDIA GeForce RTX 4060 Ti 8GB GDDR6 PCIe x16 DUAL-RTX4060TI-O8G | used, asking price | 581 €+ 8 € delivery | In stock | 2 h ago | Buy |
| AmazonASUS Dual GeForce RTX 4060 Ti OC Edition 8 GB GDDR6 Gaming Graphics Card | New | 622 € | In stock | 3 h ago | Buy |
| eBayAsus Dual GeForce RTX 4060 Ti EVO OC Edition 8 GB GDDR6 Grafikkarte | used, asking price | 625 € | In stock | 2 h ago | Buy |
| AmazonASUS Dual GeForce RTX 4060 Ti EVO White OC Edition 8GB GDDR6 Gaming Graphics… | New | 625 € | In stock | 3 h ago | Buy |
| AmazonMSI GeForce RTX 4060 Ti Ventus 2X Black 8G OC Graphics Card | New | 993 € | In stock | 3 h ago | Buy |
| AmazonASUS Dual GeForce RTX 4060 Ti EVO OC Edition 8GB GDDR6 Gaming Graphics Card | New | 650 € | Out of stock | 3 h ago | Buy |
Used on eBay: median asking price 569 €, most between 475 € and 585 € (7 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 @32k | Prefill | Good for |
|---|---|---|---|---|---|---|---|
| Qwen 3.5 9B Alibaba Qwen | Q4_K_M | fits | 7.2 GB | 36k60k with q8 cache | 26tok/sest. | 463 tok/s | chat, coding |
| Qwen 3.5 4B Alibaba Qwen | Q8_0 | fits | 6.1 GB | 66k110k with q8 cache | 32tok/sest. | 866 tok/s | chat, coding |
| Gemma 4 E4B | Q5_K_M | fits | 6.9 GB | 46k77k with q8 cache | 28tok/sest. | 581 tok/s | chat, coding |
| LFM2.5 8B-A1B Liquid AI | MXFP4 | fits | 7.4 GB | 32k55k with q8 cache | 48tok/sest. | 1902 tok/s | chat, coding |
| Qwen2.5 0.5B Alibaba Qwen | Q8_0 | fits | 1.8 GB | 32k | 191tok/sest. | 4142 tok/s | chat, coding, long documents |
| Qwen3.5 0.8B Alibaba Qwen | Q8_0 | fits | 2.0 GB | 256k | 145tok/sest. | 3891 tok/s | chat, coding, agents, long documents |
| Qwen3.5 2B Alibaba Qwen | Q5_K_M | fits | 2.6 GB | 256k | 96tok/sest. | 1855 tok/s | chat, coding, agents, long documents |
| LFM3 1.7B Liquid AI | Q8_0 | fits | 3.0 GB | 256k | 79tok/sest. | 2309 tok/s | chat, coding, agents, long documents |
| DeepSeek R1 Distill Qwen 1.5B DeepSeek | Q8_0 | fits | 3.5 GB | 128k | 63tok/sest. | 1558 tok/s | chat, coding, long documents |
| MiniCPM5 2B openbmb | Q8_0 | fits | 4.7 GB | 89k128k with q8 cache | 44tok/sest. | 931 tok/s | chat, coding, agents |
| Spark X2.5 4B XHToken | mlx-4bit | fits | 4.9 GB | 93k156k with q8 cache | 42tok/sest. | 914 tok/s | chat, coding, agents, long documents |
| LFM2.5 2.6B Liquid AI | Q8_0 | fits | 5.4 GB | 62k108k with q8 cache | 37tok/sest. | 1054 tok/s | chat, coding, agents |
| LFM2.5 VL 3B Liquid AI | Q8_0 | fits | 5.4 GB | 32k | 37tok/sest. | 967 tok/s | chat, coding, agents |
| FrogNano 4B 2609 Microsoft | Q8_0 | fits | 6.2 GB | 63k104k with q8 cache | 31tok/sest. | 866 tok/s | chat, coding, agents |
| Ministral 3 3B Instruct 2512 Mistral AI | Q6_K | fits | 6.8 GB | 37k68k with q8 cache | 28tok/sest. | 677 tok/s | chat, coding |
| granite vision 4.1 4b IBM | Q8_0 | fits | 6.8 GB | 39k70k with q8 cache | 28tok/sest. | 675 tok/s | chat, coding |
| DeepSeek R1 Distill Qwen 7B DeepSeek | Q4_K_M | fits | 7.0 GB | 38k66k with q8 cache | 27tok/sest. | 455 tok/s | chat, coding |
| granite 4.2 3b IBM | Q8_0 | fits | 7.0 GB | 36k64k with q8 cache | 27tok/sest. | 708 tok/s | chat, coding |
| Qwen 3.8 27B Alibaba Qwen | UD-Q4_K_M | offload | 18.4 GB | – | 4tok/sest. | 100 tok/s | – |
| Qwen 3.6 27B Alibaba Qwen | Q4_K_M | offload | 18.7 GB | – | 4tok/sest. | 99 tok/s | – |
| Gemma 4 31B | Q4_K_M | offload | 21.2 GB | – | 3tok/sest. | 85 tok/s | – |
| Qwen 3.6 35B-A3B Alibaba Qwen | MXFP4 | offload | 21.8 GB | – | 16tok/sest. | 671 tok/s | chat |
| Qwen 3.5 27B Alibaba Qwen | Q4_K_M | offload | 18.6 GB | – | 4tok/sest. | 99 tok/s | – |
| Gemma 4 26B-A4B | MXFP4 | offload | 17.1 GB | – | 13tok/sest. | 652 tok/s | chat |
| Qwen 3.5 35B-A3B Alibaba Qwen | Q4_K_M | offload | 22.1 GB | – | 16tok/sest. | 668 tok/s | chat |
| GLM 4.7 Flash Z.ai | Q4_K_M | offload | 19.6 GB | – | 9tok/sest. | 364 tok/s | – |
| Devstral Small 2 24B Mistral AI | Q4_K_M | offload | 19.2 GB | – | 4tok/sest. | 104 tok/s | – |
| Nemotron 3.5 Lightning 30B-A3B NVIDIA | MXFP4 | offload | 22.7 GB | – | 17tok/sest. | 755 tok/s | chat |
| Kimi Linear 48B-A3B Moonshot AI | Q4_K_M | offload | 29.2 GB | – | 18tok/sest. | 530 tok/s | chat |
| gpt-oss 20B OpenAI | Q4_K_M | offload | 12.5 GB | – | 17tok/sest. | 630 tok/s | chat |
| Qwen3 Coder 30B-A3B Alibaba Qwen | Q4_K_M | offload | 21.2 GB | – | 8tok/sest. | 312 tok/s | chat |
| Qwen3 32B Alibaba Qwen | Q4_K_M | offload | 27.3 GB | – | 2tok/sest. | 55 tok/s | – |
| Gemma 4 12B | Q4_K_M | offload | 8.3 GB | – | 17tok/sest. | 343 tok/s | chat |
| Gemma 3 27B | Q4_K_M | offload | 19.2 GB | – | 4tok/sest. | 104 tok/s | – |
| Mistral Small 3.2 24B Mistral AI | Q4_K_M | offload | 19.2 GB | – | 4tok/sest. | 104 tok/s | – |
| DeepSeek R1 Distill Qwen 32B DeepSeek | Q4_K_M | offload | 27.4 GB | – | 2tok/sest. | 63 tok/s | – |
| Ministral 3 14B Mistral AI | Q4_K_M | offload | 13.6 GB | – | 6tok/sest. | 182 tok/s | – |
| Qwen3 14B Alibaba Qwen | Q4_K_M | offload | 14.3 GB | – | 6tok/sest. | 160 tok/s | – |
| DeepSeek R1 0528 Qwen3 8B DeepSeek | Q4_K_M | offload | 10.1 GB | 13k25k with q8 cache | 11tok/sest. | 315 tok/s | chat |
| Qwen3 8B Alibaba Qwen | Q4_K_M | offload | 10.1 GB | 13k25k with q8 cache | 11tok/sest. | 315 tok/s | chat |
| Phi-4 14B Microsoft | Q4_K_M | offload | 15.4 GB | – | 5tok/sest. | 155 tok/s | – |
| Ministral 3 8B Mistral AI | Q4_K_M | offload | 10.0 GB | 13k24k with q8 cache | 11tok/sest. | 306 tok/s | chat |
| Granite 4.2 8B IBM | Q4_K_M | offload | 11.0 GB | 9k17k with q8 cache | 9tok/sest. | 275 tok/s | chat |
| Llama 3.1 8B Meta | Q4_K_M | offload | 9.5 GB | 16k29k with q8 cache | 12tok/sest. | 345 tok/s | chat |
| Mellum2.1 12B A2.5B Thinking JetBrains | Q4_K_M | offload | 9.6 GB | – | 29tok/sest. | 1167 tok/s | chat, coding |
| GLM 4.6V Flash Z.ai | Q4_K_M | offload | 7.9 GB | 21k35k with q8 cache | 21tok/sest. | 304 tok/s | chat, coding |
| Kimi VL A3B Thinking 2506 Moonshot AI | Q4_K_M | offload | 11.7 GB | – | 19tok/sest. | 787 tok/s | chat |
| Kimi VL A3B Moonshot AI | mlx-5bit | offload | 12.9 GB | – | 16tok/sest. | 631 tok/s | chat |
| GLM 4 9B 0414 Z.ai | mlx-4bit | offload | 8.7 GB | 2k4k with q8 cache | 15tok/sest. | 302 tok/s | chat |
| DeepSeek R1 Distill Llama 8B DeepSeek | Q4_K_M | offload | 9.5 GB | 16k29k with q8 cache | 12tok/sest. | 345 tok/s | chat |
| LFM2.5 24B A2B Liquid AI | Q4_K_M | offload | 17.7 GB | – | 11tok/sest. | 671 tok/s | chat |
| Ministral 3 8B Reasoning 2512 Mistral AI | Q4_K_M | offload | 10.0 GB | 13k24k with q8 cache | 11tok/sest. | 306 tok/s | chat |
| GLM 4.1V 9B Thinking Z.ai | mlx-8bit | offload | 13.1 GB | – | 7tok/sest. | 228 tok/s | – |
| Ministral 3 14B Reasoning 2512 Mistral AI | Q4_K_M | offload | 13.6 GB | – | 6tok/sest. | 182 tok/s | – |
| DeepSeek R1 Distill Qwen 14B DeepSeek | Q4_K_M | offload | 15.3 GB | – | 5tok/sest. | 146 tok/s | – |
| Muse Glimmer 30B meta-models | Q4_K_M | offload | 17.5 GB | – | 4tok/sest. | 98 tok/s | – |
| Devstral Small 2507 Mistral AI | Q4_K_M | offload | 19.2 GB | – | 4tok/sest. | 105 tok/s | – |
| Magistral Small 2506 Mistral AI | Q4_K_M | offload | 19.2 GB | – | 4tok/sest. | 105 tok/s | – |
| Magistral Small 2507 Mistral AI | Q4_K_M | offload | 19.2 GB | – | 4tok/sest. | 105 tok/s | – |
| Magistral Small 2509 Mistral AI | Q4_K_M | offload | 19.2 GB | – | 4tok/sest. | 104 tok/s | – |
| Qwen3.8 27B OBLITERATED OBLITERATUS | mlx-4bit | offload | 22.3 GB | – | 3tok/sest. | 93 tok/s | – |
| granite 4.2 30b IBM | Q4_K_M | offload | 25.7 GB | – | 3tok/sest. | 73 tok/s | – |
| DeepSeek V4 Flash DeepSeek | UD-Q2_K_XL | won't run | 93.8 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 | 75.2 GB | – | – | – | – |
| MiniMax M2.7 MiniMax | UD-Q2_K_XL | won't run | 78.8 GB | – | – | – | – |
| Qwen 3.5 122B-A10B Alibaba Qwen | UD-Q2_K_XL | won't run | 40.7 GB | – | – | – | – |
| Nemotron 3 Super 120B-A12B NVIDIA | UD-Q2_K_XL | won't run | 52.2 GB | – | – | – | – |
| Qwen3 Coder Next Alibaba Qwen | Q4_K_M | won't run | 46.9 GB | – | – | – | – |
| gpt-oss 120B OpenAI | Q4_K_M | won't run | 60.5 GB | – | – | – | – |
| Mistral Small 4 119B Mistral AI | UD-Q2_K_XL | won't run | 39.0 GB | – | – | – | – |
| GLM 4.5 Air Z.ai | Q2_K | won't run | 48.8 GB | – | – | – | – |
| Qwen3 Next 80B-A3B Alibaba Qwen | Q4_K_M | won't run | 46.9 GB | – | – | – | – |
| Llama 3.3 70B Meta | Q4_K_M | won't run | 50.5 GB | – | – | – | – |
| DeepSeek R1 DeepSeek | Q4_K_M | won't run | 379.7 GB | – | – | – | – |
| DeepSeek R1 0528 DeepSeek | Q4_K_M | won't run | 380.2 GB | – | – | – | – |
| DeepSeek R1 Distill Llama 70B DeepSeek | Q4_K_M | won't run | 50.5 GB | – | – | – | – |
| DeepSeek R1 Zero DeepSeek | Q4_K_M | won't run | 379.7 GB | – | – | – | – |
| DeepSeek V3 DeepSeek | Q4_K_M | won't run | 379.7 GB | – | – | – | – |
| DeepSeek V3 0324 DeepSeek | Q4_K_M | won't run | 379.7 GB | – | – | – | – |
| DeepSeek V3.1 DeepSeek | Q4_K_M | won't run | 380.6 GB | – | – | – | – |
| DeepSeek V3.1 Terminus DeepSeek | Q4_K_M | won't run | 380.6 GB | – | – | – | – |
| DeepSeek V3.2 DeepSeek | Q4_K_M | won't run | 380.6 GB | – | – | – | – |
| DeepSeek V4 Flash 0731 DeepSeek | MXFP4 | won't run | 149.2 GB | – | – | – | – |
| DeepSeek V4 Flash Vision Exp DeepSeek | MXFP4 | won't run | 158.0 GB | – | – | – | – |
| Devstral 2 123B Instruct 2512 Mistral AI | Q4_K_M | won't run | 81.6 GB | – | – | – | – |
| GLM 4.5 Z.ai | Q4_K_M | won't run | 214.0 GB | – | – | – | – |
| GLM 4.5V Z.ai | Q4_K_M | won't run | 65.9 GB | – | – | – | – |
| GLM 4.6 Z.ai | Q4_K_M | won't run | 213.2 GB | – | – | – | – |
| GLM 4.6V Z.ai | Q4_K_M | won't run | 72.2 GB | – | – | – | – |
| GLM 4.7 Z.ai | Q4_K_M | won't run | 214.0 GB | – | – | – | – |
| GLM 5 Z.ai | MXFP4 | won't run | 386.0 GB | – | – | – | – |
| GLM 5.1 Z.ai | MXFP4 | won't run | 423.6 GB | – | – | – | – |
| GLM 5.2 Z.ai | UD-Q4_K_M | won't run | 437.5 GB | – | – | – | – |
| GLM 5.3 Z.ai | Q8_0 | won't run | 750.0 GB | – | – | – | – |
| Kimi Dev 72B Moonshot AI | Q6_K | won't run | 70.8 GB | – | – | – | – |
| Kimi K2 Moonshot AI | Q4_K_M | won't run | 581.2 GB | – | – | – | – |
| Kimi K2 Instruct 0905 Moonshot AI | Q4_K_M | won't run | 581.6 GB | – | – | – | – |
| Kimi K2 Thinking Moonshot AI | Q4_K_M | won't run | 581.6 GB | – | – | – | – |
| Kimi K2.5 Moonshot AI | Q4_K_M | won't run | 581.6 GB | – | – | – | – |
| MiniMax M2 MiniMax | Q4_K_M | won't run | 137.5 GB | – | – | – | – |
| MiniMax M2.1 MiniMax | Q4_K_M | won't run | 137.5 GB | – | – | – | – |
| MiniMax M2.5 MiniMax | MXFP4 | won't run | 123.9 GB | – | – | – | – |
| MiniMax M3 MiniMax | MXFP4 | won't run | 244.3 GB | – | – | – | – |
| Mistral Medium 3.5 128B Mistral AI | Q4_K_M | won't run | 81.6 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?
- Qwen 3.5 9B (Q4_K_M) fits fully in its memory at 32k context and generates at about 26 tok/s, an estimate from its memory bandwidth. 18 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 94 GB at 32k 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 32k context. See the best hardware for local AI at every budget.