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 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 14 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 | 35 min ago | Buy |
| eBayUsed GeForce RTX 4060 on eBay | used, asking price | 375 € | In stock | 22 min ago | Buy |
| eBayDell NVIDIA GeForce RTX 4060 | used, asking price | 379 € | In stock | 35 min ago | Buy |
| eBayMSI GeForce RTX 4060 Ventus 2X Black 8G OC | used, asking price | 404 €+ 8 € delivery | In stock | 35 min ago | Buy |
| eBayMSI NVIDIA GeForce RTX 4060 VENTUS 2X Black OC 8GB GDDR6 HDMI/DP | used, asking price | 450 € | In stock | 35 min ago | Buy |
| AmazonGigabyte NVIDIA GeForce RTX 4060 WINDFORCE OC Graphics Card | New | 652 € | In stock | 4 h ago | Buy |
| AmazonMSI GeForce RTX 4060 Ventus 2X Black 8G OC Graphics Card -NVIDIA RTX 4060 | New | 466 € | Out of stock | 5 h ago | Buy |
Used on eBay: median asking price 375 €, most between 357 € and 416 € (12 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 | 14tok/sest. | 205 tok/s | chat |
| Qwen 3.5 9B Alibaba Qwen | Q8_0 | fits | 14.8 GB | 128k212k with q8 cache | 12tok/sest. | 244 tok/s | chat |
| Qwen 3.5 4B Alibaba Qwen | Q8_0 | fits | 10.1 GB | 255k256k with q8 cache | 19tok/sest. | 380 tok/s | chat, coding, long documents |
| Gemma 4 E4B | Q8_0 | fits | 13.0 GB | 128k | 14tok/sest. | 342 tok/s | chat |
| LFM2.5 8B-A1B Liquid AI | UD-Q6_K | fits | 14.5 GB | 125k | 14tok/sest. | 457 tok/s | chat, coding |
| Qwen2.5 0.5B Alibaba Qwen | Q8_0 | fits | 3.8 GB | 32k | 79tok/sest. | 1027 tok/s | chat, coding, long documents |
| Qwen3.5 0.8B Alibaba Qwen | Q8_0 | fits | 4.1 GB | 256k | 69tok/sest. | 1369 tok/s | chat, coding, agents, long documents |
| Qwen3.5 2B Alibaba Qwen | Q5_K_M | fits | 4.7 GB | 256k | 55tok/sest. | 875 tok/s | chat, coding, agents, long documents |
| LFM3 1.7B Liquid AI | Q8_0 | fits | 5.1 GB | 256k | 49tok/sest. | 1013 tok/s | chat, coding, agents, long documents |
| DeepSeek R1 Distill Qwen 1.5B DeepSeek | Q8_0 | fits | 7.1 GB | 128k | 30tok/sest. | 459 tok/s | chat, coding, long documents |
| Mellum2.1 12B A2.5B Thinking JetBrains | Q6_K | fits | 14.3 GB | 128k | 24tok/sest. | 529 tok/s | chat, coding, agents, long documents |
| Spark X2.5 4B XHToken | mlx-4bit | fits | 9.2 GB | 263k441k with q8 cache | 21tok/sest. | 376 tok/s | chat, coding, agents, long documents |
| MiniCPM5 2B openbmb | Q8_0 | fits | 9.6 GB | 128k | 20tok/sest. | 247 tok/s | chat, coding, agents |
| FrogNano 4B 2609 Microsoft | Q8_0 | fits | 10.2 GB | 251k256k with q8 cache | 19tok/sest. | 380 tok/s | chat, coding, long documents |
| LFM2.5 2.6B Liquid AI | Q8_0 | fits | 12.0 GB | 128k | 15tok/sest. | 316 tok/s | chat, coding, agents, long documents |
| LFM2.5 VL 3B Liquid AI | Q8_0 | fits | 12.0 GB | 32k | 15tok/sest. | 304 tok/s | chat, coding, agents, long documents |
| GLM 4.6V Flash Z.ai | Q4_K_M | fits | 12.6 GB | 128k | 15tok/sest. | 107 tok/s | chat, coding |
| granite vision 4.1 4b IBM | Q5_K_M | fits | 14.1 GB | 128k | 13tok/sest. | 195 tok/s | chat, coding |
| GLM 4 9B 0414 Z.ai | mlx-5bit | fits | 14.5 GB | 32k | 12tok/sest. | 110 tok/s | chat |
| DeepSeek R1 Distill Qwen 7B DeepSeek | Q6_K | fits | 14.7 GB | 128k | 12tok/sest. | 164 tok/s | chat, coding |
| granite 4.2 3b IBM | Q6_K | fits | 14.8 GB | 128k | 12tok/sest. | 199 tok/s | chat, coding |
| Qwen 3.8 27B Alibaba Qwen | UD-Q4_K_M | offload | 25.3 GB | – | 3tok/sest. | 61 tok/s | – |
| Qwen 3.6 27B Alibaba Qwen | Q4_K_M | offload | 25.7 GB | – | 3tok/sest. | 61 tok/s | – |
| Gemma 4 31B | Q4_K_M | offload | 29.7 GB | – | 3tok/sest. | 54 tok/s | – |
| Qwen 3.6 35B-A3B Alibaba Qwen | MXFP4 | offload | 24.6 GB | – | 12tok/sest. | 301 tok/s | chat, coding |
| Qwen 3.5 27B Alibaba Qwen | Q4_K_M | offload | 25.6 GB | – | 3tok/sest. | 61 tok/s | – |
| Gemma 4 26B-A4B | MXFP4 | offload | 20.0 GB | – | 12tok/sest. | 409 tok/s | chat, coding |
| Qwen 3.5 35B-A3B Alibaba Qwen | Q4_K_M | offload | 24.9 GB | – | 11tok/sest. | 299 tok/s | chat, coding |
| GLM 4.7 Flash Z.ai | Q4_K_M | offload | 25.5 GB | – | 5tok/sest. | 140 tok/s | chat |
| Devstral Small 2 24B Mistral AI | Q4_K_M | offload | 35.2 GB | 2k3k with q8 cache | 2tok/sest. | 47 tok/s | – |
| Nemotron 3.5 Lightning 30B-A3B NVIDIA | MXFP4 | offload | 24.3 GB | – | 17tok/sest. | 408 tok/s | chat, coding, long documents |
| gpt-oss 20B OpenAI | Q4_K_M | offload | 15.7 GB | 97k128k with q8 cache | 16tok/sest. | 307 tok/s | chat, coding, agents, long documents |
| Kimi Linear 48B-A3B Moonshot AI | Q4_K_M | offload | 30.9 GB | – | 16tok/sest. | 204 tok/s | chat, coding |
| Qwen3 Coder 30B-A3B Alibaba Qwen | Q4_K_M | offload | 31.1 GB | – | 3tok/sest. | 79 tok/s | chat |
| Gemma 3 27B | Q4_K_M | offload | 27.7 GB | – | 3tok/sest. | 72 tok/s | – |
| Mistral Small 3.2 24B Mistral AI | Q4_K_M | offload | 35.2 GB | 2k3k with q8 cache | 2tok/sest. | 47 tok/s | – |
| Ministral 3 14B Mistral AI | Q4_K_M | offload | 29.5 GB | 37k67k with q8 cache | 3tok/sest. | 65 tok/s | chat |
| Qwen3 14B Alibaba Qwen | Q4_K_M | offload | 30.2 GB | 32k40k with q8 cache | 3tok/sest. | 55 tok/s | chat |
| DeepSeek R1 0528 Qwen3 8B DeepSeek | Q4_K_M | offload | 24.5 GB | 61k111k with q8 cache | 4tok/sest. | 92 tok/s | chat |
| Qwen3 8B Alibaba Qwen | Q4_K_M | offload | 24.5 GB | 40k | 4tok/sest. | 92 tok/s | chat |
| Phi-4 14B Microsoft | Q4_K_M | offload | 35.1 GB | 16k | 2tok/sest. | 52 tok/s | chat |
| Ministral 3 8B Mistral AI | Q4_K_M | offload | 23.7 GB | 63k115k with q8 cache | 4tok/sest. | 95 tok/s | chat |
| Granite 4.2 8B IBM | Q4_K_M | offload | 27.0 GB | 52k96k with q8 cache | 3tok/sest. | 80 tok/s | chat |
| Llama 3.1 8B Meta | Q4_K_M | offload | 22.4 GB | 69k126k with q8 cache | 4tok/sest. | 105 tok/s | chat |
| Kimi VL A3B Thinking 2506 Moonshot AI | Q4_K_M | offload | 15.5 GB | 109k128k with q8 cache | 15tok/sest. | 322 tok/s | chat, coding, long documents |
| Kimi VL A3B Moonshot AI | mlx-5bit | offload | 16.7 GB | 75k123k with q8 cache | 13tok/sest. | 227 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. | 175 tok/s | chat, coding |
| GLM 4.1V 9B Thinking Z.ai | mlx-8bit | offload | 17.8 GB | 60k64k with q8 cache | 7tok/sest. | 95 tok/s | – |
| Muse Glimmer 30B meta-models | Q4_K_M | offload | 19.7 GB | – | 6tok/sest. | 76 tok/s | – |
| DeepSeek R1 Distill Llama 8B DeepSeek | Q4_K_M | offload | 22.4 GB | 69k126k with q8 cache | 4tok/sest. | 105 tok/s | chat |
| LFM2.5 24B A2B Liquid AI | Q4_K_M | offload | 26.1 GB | – | 4tok/sest. | 192 tok/s | chat |
| Ministral 3 8B Reasoning 2512 Mistral AI | Q4_K_M | offload | 23.7 GB | 63k115k with q8 cache | 4tok/sest. | 95 tok/s | chat |
| Ministral 3 14B Reasoning 2512 Mistral AI | Q4_K_M | offload | 29.5 GB | 37k67k with q8 cache | 3tok/sest. | 65 tok/s | chat |
| Qwen3.8 27B OBLITERATED OBLITERATUS | mlx-4bit | offload | 29.2 GB | – | 3tok/sest. | 57 tok/s | – |
| DeepSeek R1 Distill Qwen 14B DeepSeek | Q4_K_M | offload | 34.2 GB | 27k50k with q8 cache | 2tok/sest. | 46 tok/s | chat |
| Devstral Small 2507 Mistral AI | Q4_K_M | offload | 35.2 GB | 2k3k with q8 cache | 2tok/sest. | 48 tok/s | – |
| Magistral Small 2506 Mistral AI | Q4_K_M | offload | 35.2 GB | 2k3k with q8 cache | 2tok/sest. | 48 tok/s | – |
| Magistral Small 2507 Mistral AI | Q4_K_M | offload | 35.2 GB | 2k3k with q8 cache | 2tok/sest. | 48 tok/s | – |
| Magistral Small 2509 Mistral AI | Q4_K_M | offload | 35.2 GB | 2k3k with q8 cache | 2tok/sest. | 47 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
- 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?
- Gemma 4 12B (Q6_K) fits fully in its memory at 128k context and generates at about 14 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 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 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 128k context. See the best hardware for local AI at every budget.