NVIDIA GeForce RTX 4060
8 GB · 272 GB/s · 115 W
In short
The NVIDIA GeForce RTX 4060 has 8 GB of GDDR6 memory with 272 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 25 tok/s (estimated). The best new price for the NVIDIA GeForce RTX 4060 in Germany is 652 € at Amazon; used, it starts at 350 € (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 |
|---|---|---|---|---|---|
| eBayPalit GeForce RTX 4060 Dual 8 GB GDDR6 | used, asking price | 350 €+ 6 € delivery | In stock | 2 h ago | Buy |
| eBayUsed GeForce RTX 4060 on eBay | used, asking price | 370 € | In stock | 5 h ago | Buy |
| eBayDell NVIDIA GeForce RTX 4060 | used, asking price | 379 € | In stock | 2 h ago | Buy |
| eBayMSI GeForce RTX 4060 Ventus 2X Black 8G OC | used, asking price | 404 €+ 8 € delivery | In stock | 2 h ago | Buy |
| eBayMSI NVIDIA GeForce RTX 4060 VENTUS 2X Black OC 8GB GDDR6 HDMI/DP | used, asking price | 450 € | In stock | 2 h ago | Buy |
| AmazonGigabyte NVIDIA GeForce RTX 4060 WINDFORCE OC Graphics Card | New | 652 € | In stock | 3 h ago | Buy |
| AmazonMSI GeForce RTX 4060 Ventus 2X Black 8G OC Graphics Card -NVIDIA RTX 4060 | New | 466 € | Out of stock | 3 h ago | Buy |
Used on eBay: median asking price 370 €, most between 359 € and 404 € (13 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 | 25tok/sest. | 317 tok/s | chat, coding |
| Qwen 3.5 4B Alibaba Qwen | Q8_0 | fits | 6.1 GB | 66k110k with q8 cache | 30tok/sest. | 593 tok/s | chat, coding |
| Gemma 4 E4B | Q5_K_M | fits | 6.9 GB | 46k77k with q8 cache | 26tok/sest. | 398 tok/s | chat, coding |
| LFM2.5 8B-A1B Liquid AI | MXFP4 | fits | 7.4 GB | 32k55k with q8 cache | 45tok/sest. | 1303 tok/s | chat, coding |
| Qwen2.5 0.5B Alibaba Qwen | Q8_0 | fits | 1.8 GB | 32k | 181tok/sest. | 2837 tok/s | chat, coding, long documents |
| Qwen3.5 0.8B Alibaba Qwen | Q8_0 | fits | 2.0 GB | 256k | 137tok/sest. | 2665 tok/s | chat, coding, agents, long documents |
| Qwen3.5 2B Alibaba Qwen | Q5_K_M | fits | 2.6 GB | 256k | 91tok/sest. | 1271 tok/s | chat, coding, agents, long documents |
| LFM3 1.7B Liquid AI | Q8_0 | fits | 3.0 GB | 256k | 75tok/sest. | 1582 tok/s | chat, coding, agents, long documents |
| DeepSeek R1 Distill Qwen 1.5B DeepSeek | Q8_0 | fits | 3.5 GB | 128k | 60tok/sest. | 1067 tok/s | chat, coding, long documents |
| MiniCPM5 2B openbmb | Q8_0 | fits | 4.7 GB | 89k128k with q8 cache | 41tok/sest. | 637 tok/s | chat, coding, agents |
| Spark X2.5 4B XHToken | mlx-4bit | fits | 4.9 GB | 93k156k with q8 cache | 39tok/sest. | 626 tok/s | chat, coding, agents, long documents |
| LFM2.5 2.6B Liquid AI | Q8_0 | fits | 5.4 GB | 62k108k with q8 cache | 35tok/sest. | 722 tok/s | chat, coding, agents |
| LFM2.5 VL 3B Liquid AI | Q8_0 | fits | 5.4 GB | 32k | 35tok/sest. | 662 tok/s | chat, coding, agents |
| FrogNano 4B 2609 Microsoft | Q8_0 | fits | 6.2 GB | 63k104k with q8 cache | 29tok/sest. | 593 tok/s | chat, coding |
| granite vision 4.1 4b IBM | Q8_0 | fits | 6.8 GB | 39k70k with q8 cache | 27tok/sest. | 463 tok/s | chat, coding |
| Ministral 3 3B Instruct 2512 Mistral AI | Q6_K | fits | 6.8 GB | 37k68k with q8 cache | 27tok/sest. | 463 tok/s | chat, coding |
| DeepSeek R1 Distill Qwen 7B DeepSeek | Q4_K_M | fits | 7.0 GB | 38k66k with q8 cache | 26tok/sest. | 312 tok/s | chat, coding |
| granite 4.2 3b IBM | Q8_0 | fits | 7.0 GB | 36k64k with q8 cache | 26tok/sest. | 485 tok/s | chat, coding |
| Qwen 3.8 27B Alibaba Qwen | UD-Q4_K_M | offload | 18.4 GB | – | 4tok/sest. | 68 tok/s | – |
| Qwen 3.6 27B Alibaba Qwen | Q4_K_M | offload | 18.7 GB | – | 4tok/sest. | 68 tok/s | – |
| Gemma 4 31B | Q4_K_M | offload | 21.2 GB | – | 3tok/sest. | 58 tok/s | – |
| Qwen 3.6 35B-A3B Alibaba Qwen | MXFP4 | offload | 21.8 GB | – | 16tok/sest. | 460 tok/s | chat |
| Qwen 3.5 27B Alibaba Qwen | Q4_K_M | offload | 18.6 GB | – | 4tok/sest. | 68 tok/s | – |
| Gemma 4 26B-A4B | MXFP4 | offload | 17.1 GB | – | 13tok/sest. | 446 tok/s | chat |
| Qwen 3.5 35B-A3B Alibaba Qwen | Q4_K_M | offload | 22.1 GB | – | 16tok/sest. | 458 tok/s | chat |
| GLM 4.7 Flash Z.ai | Q4_K_M | offload | 19.6 GB | – | 9tok/sest. | 249 tok/s | – |
| Devstral Small 2 24B Mistral AI | Q4_K_M | offload | 19.2 GB | – | 4tok/sest. | 71 tok/s | – |
| Nemotron 3.5 Lightning 30B-A3B NVIDIA | MXFP4 | offload | 22.7 GB | – | 17tok/sest. | 517 tok/s | chat |
| Kimi Linear 48B-A3B Moonshot AI | Q4_K_M | offload | 29.2 GB | – | 18tok/sest. | 363 tok/s | chat |
| gpt-oss 20B OpenAI | Q4_K_M | offload | 12.5 GB | – | 17tok/sest. | 431 tok/s | chat |
| Qwen3 Coder 30B-A3B Alibaba Qwen | Q4_K_M | offload | 21.2 GB | – | 8tok/sest. | 213 tok/s | chat |
| Qwen3 32B Alibaba Qwen | Q4_K_M | offload | 27.3 GB | – | 2tok/sest. | 37 tok/s | – |
| Gemma 4 12B | Q4_K_M | offload | 8.3 GB | – | 17tok/sest. | 235 tok/s | chat |
| Gemma 3 27B | Q4_K_M | offload | 19.2 GB | – | 4tok/sest. | 71 tok/s | – |
| Mistral Small 3.2 24B Mistral AI | Q4_K_M | offload | 19.2 GB | – | 4tok/sest. | 71 tok/s | – |
| DeepSeek R1 Distill Qwen 32B DeepSeek | Q4_K_M | offload | 27.4 GB | – | 2tok/sest. | 43 tok/s | – |
| Ministral 3 14B Mistral AI | Q4_K_M | offload | 13.6 GB | – | 6tok/sest. | 125 tok/s | – |
| Qwen3 14B Alibaba Qwen | Q4_K_M | offload | 14.3 GB | – | 6tok/sest. | 110 tok/s | – |
| DeepSeek R1 0528 Qwen3 8B DeepSeek | Q4_K_M | offload | 10.1 GB | 13k25k with q8 cache | 11tok/sest. | 216 tok/s | chat |
| Qwen3 8B Alibaba Qwen | Q4_K_M | offload | 10.1 GB | 13k25k with q8 cache | 11tok/sest. | 216 tok/s | chat |
| Phi-4 14B Microsoft | Q4_K_M | offload | 15.4 GB | – | 5tok/sest. | 106 tok/s | – |
| Ministral 3 8B Mistral AI | Q4_K_M | offload | 10.0 GB | 13k24k with q8 cache | 11tok/sest. | 209 tok/s | chat |
| Granite 4.2 8B IBM | Q4_K_M | offload | 11.0 GB | 9k17k with q8 cache | 9tok/sest. | 188 tok/s | chat |
| Llama 3.1 8B Meta | Q4_K_M | offload | 9.5 GB | 16k29k with q8 cache | 12tok/sest. | 236 tok/s | chat |
| Mellum2.1 12B A2.5B Thinking JetBrains | Q4_K_M | offload | 9.6 GB | – | 28tok/sest. | 799 tok/s | chat, coding |
| GLM 4.6V Flash Z.ai | Q4_K_M | offload | 7.9 GB | 21k35k with q8 cache | 20tok/sest. | 208 tok/s | chat |
| Kimi VL A3B Thinking 2506 Moonshot AI | Q4_K_M | offload | 11.7 GB | – | 19tok/sest. | 539 tok/s | chat |
| Kimi VL A3B Moonshot AI | mlx-5bit | offload | 12.9 GB | – | 16tok/sest. | 432 tok/s | chat |
| GLM 4 9B 0414 Z.ai | mlx-4bit | offload | 8.7 GB | 2k4k with q8 cache | 15tok/sest. | 207 tok/s | chat |
| DeepSeek R1 Distill Llama 8B DeepSeek | Q4_K_M | offload | 9.5 GB | 16k29k with q8 cache | 12tok/sest. | 236 tok/s | chat |
| LFM2.5 24B A2B Liquid AI | Q4_K_M | offload | 17.7 GB | – | 11tok/sest. | 459 tok/s | chat |
| Ministral 3 8B Reasoning 2512 Mistral AI | Q4_K_M | offload | 10.0 GB | 13k24k with q8 cache | 11tok/sest. | 209 tok/s | chat |
| GLM 4.1V 9B Thinking Z.ai | mlx-8bit | offload | 13.1 GB | – | 6tok/sest. | 156 tok/s | – |
| Ministral 3 14B Reasoning 2512 Mistral AI | Q4_K_M | offload | 13.6 GB | – | 6tok/sest. | 125 tok/s | – |
| DeepSeek R1 Distill Qwen 14B DeepSeek | Q4_K_M | offload | 15.3 GB | – | 5tok/sest. | 100 tok/s | – |
| Muse Glimmer 30B meta-models | Q4_K_M | offload | 17.5 GB | – | 4tok/sest. | 67 tok/s | – |
| Devstral Small 2507 Mistral AI | Q4_K_M | offload | 19.2 GB | – | 4tok/sest. | 72 tok/s | – |
| Magistral Small 2506 Mistral AI | Q4_K_M | offload | 19.2 GB | – | 4tok/sest. | 72 tok/s | – |
| Magistral Small 2507 Mistral AI | Q4_K_M | offload | 19.2 GB | – | 4tok/sest. | 72 tok/s | – |
| Magistral Small 2509 Mistral AI | Q4_K_M | offload | 19.2 GB | – | 4tok/sest. | 71 tok/s | – |
| Qwen3.8 27B OBLITERATED OBLITERATUS | mlx-4bit | offload | 22.3 GB | – | 3tok/sest. | 64 tok/s | – |
| granite 4.2 30b IBM | Q4_K_M | offload | 25.7 GB | – | 3tok/sest. | 50 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
- Memory
- 8 GB GDDR6
- Memory bus
- 128-bit
- Memory bandwidth
- 272 GB/s
- FP16 compute
- 15.114 TFLOPS
- Software
- CUDA
- Board power
- 115 W
- PCIe generation
- Gen 4
- PCIe lanes
- x8
Compare with
Questions
- How much memory does the NVIDIA GeForce RTX 4060 have for AI models?
- The NVIDIA GeForce RTX 4060 has 8 GB of GDDR6 memory with 272 GB/s of bandwidth, which sets how fast it generates text.
- What is the best model the NVIDIA GeForce RTX 4060 can run locally?
- Qwen 3.5 9B (Q4_K_M) fits fully in its memory at 32k context and generates at about 25 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 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 and system RAM can hold.
- How much does the NVIDIA GeForce RTX 4060 cost in Germany?
- The best new price for the NVIDIA GeForce RTX 4060 in Germany is 652 € at Amazon; used, it starts at 350 € (asking price).
Speeds on this page are for 32k context. See the best hardware for local AI at every budget.