NVIDIA GeForce RTX 4060 Ti 16GB
16 GB · 288 GB/s · 165 W
In short
The NVIDIA GeForce RTX 4060 Ti 16GB has 16 GB of GDDR6 memory with 288 GB/s of bandwidth, which sets how fast it generates text. At 8k context it runs 37 of the 113 open models we track fully in memory; the most capable is Devstral Small 2 24B at Q4_K_M, at about 11 tok/s (estimated). The best new price for the NVIDIA GeForce RTX 4060 Ti 16GB in the UK is £977 at Amazon; used, it starts at £496 (asking price).
Updated . Speeds are estimates; how they are worked out.
Prices
Every retailer we track in the UK, cheapest first. Buy links may earn us a commission.
| Retailer | Condition | Price | Stock | Checked | Buy |
|---|---|---|---|---|---|
| eBayMSI GeForce RTX 4060 Ti Gaming X Slim 16GB | used, asking price | £496 | In stock | 2 h ago | Buy |
| eBayMSI Gaming X Slim NVIDIA GeForce RTX 4060 Ti 16GB Graphics Card | used, asking price | £491+ £9 delivery | In stock | 2 h ago | Buy |
| AmazonASUS ProArt GeForce RTX 4060 Ti 16GB OC Edition GDDR6 Graphics Card | New | £977 | In stock | 20 min ago | Buy |
| AmazonASUS ProArt GeForce RTX 4060 Ti 16GB OC Edition GDDR6 Graphics Card | New | £1,169 | In stock | 20 min ago | Buy |
| AmazonMSI Gaming GeForce RTX 4060 Ti 16GB GDRR6 Boost Clock: 2625 MHz 128-Bit… | New | £1,178 | In stock | 20 min ago | Buy |
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 @8k | Prefill | Good for |
|---|---|---|---|---|---|---|---|
| Devstral Small 2 24B Mistral AI | Q4_K_M | fits | 15.3 GB | 8k15k with q8 cache | 11tok/sest. | 391 tok/s | – |
| gpt-oss 20B OpenAI | MXFP4 | fits | 12.2 GB | 116k128k with q8 cache | 41tok/sest. | 2148 tok/s | chat, coding, agents, long documents |
| Gemma 4 12B | Q8_0 | fits | 13.0 GB | 120k193k with q8 cache | 14tok/sest. | 788 tok/s | – |
| Qwen 3.5 9B Alibaba Qwen | Q8_0 | fits | 9.9 GB | 155k256k with q8 cache | 18tok/sest. | 998 tok/s | chat |
| Mistral Small 3.2 24B Mistral AI | Q4_K_M | fits | 15.3 GB | 8k15k with q8 cache | 11tok/sest. | 391 tok/s | – |
| Qwen3 14B Alibaba Qwen | Q6_K | fits | 13.3 GB | 21k38k with q8 cache | 13tok/sest. | 602 tok/s | – |
| Ministral 3 14B Mistral AI | Q8_0 | fits | 15.4 GB | 8k14k with q8 cache | 11tok/sest. | 648 tok/s | – |
| DeepSeek R1 0528 Qwen3 8B DeepSeek | Q8_0 | fits | 10.0 GB | 44k81k with q8 cache | 18tok/sest. | 1053 tok/s | chat |
| Qwen3 8B Alibaba Qwen | Q8_0 | fits | 10.0 GB | 40k | 18tok/sest. | 1053 tok/s | chat |
| Phi-4 14B Microsoft | Q6_K | fits | 13.5 GB | 16k | 13tok/sest. | 606 tok/s | – |
| Qwen 3.5 4B Alibaba Qwen | Q8_0 | fits | 5.2 GB | 256k | 37tok/sest. | 2009 tok/s | chat, coding, long documents |
| Ministral 3 8B Mistral AI | Q8_0 | fits | 10.2 GB | 45k82k with q8 cache | 18tok/sest. | 984 tok/s | chat |
| Granite 4.2 8B IBM | Q8_0 | fits | 10.7 GB | 36k67k with q8 cache | 17tok/sest. | 977 tok/s | chat |
| Gemma 4 E4B | Q8_0 | fits | 8.6 GB | 128k | 21tok/sest. | 1209 tok/s | chat |
| LFM2.5 8B-A1B Liquid AI | Q8_0 | fits | 9.5 GB | 119k125k with q8 cache | 73tok/sest. | 7058 tok/s | chat, coding |
| Llama 3.1 8B Meta | Q6_K | fits | 7.9 GB | 65k118k with q8 cache | 23tok/sest. | 1087 tok/s | chat |
| Qwen2.5 0.5B Alibaba Qwen | Q8_0 | fits | 1.3 GB | 32k | 283tok/sest. | 14772 tok/s | chat, coding, long documents |
| Qwen3.5 0.8B Alibaba Qwen | Q8_0 | fits | 1.6 GB | 256k | 192tok/sest. | 10163 tok/s | chat, coding, agents, long documents |
| Qwen3.5 2B Alibaba Qwen | Q5_K_M | fits | 2.2 GB | 256k | 115tok/sest. | 4169 tok/s | chat, coding, agents, long documents |
| DeepSeek R1 Distill Qwen 1.5B DeepSeek | Q8_0 | fits | 2.7 GB | 128k | 84tok/sest. | 4649 tok/s | chat, coding, long documents |
| MiniCPM5 2B openbmb | Q8_0 | fits | 3.6 GB | 128k | 59tok/sest. | 3073 tok/s | chat, coding, agents, long documents |
| LFM2.5 2.6B Liquid AI | Q8_0 | fits | 3.9 GB | 128k | 53tok/sest. | 3093 tok/s | chat, coding, agents, long documents |
| LFM2.5 VL 3B Liquid AI | Q8_0 | fits | 3.9 GB | 32k | 53tok/sest. | 2730 tok/s | chat, coding, agents, long documents |
| granite vision 4.1 4b IBM | Q8_0 | fits | 4.7 GB | 128k | 42tok/sest. | 2047 tok/s | chat, coding |
| granite 4.2 3b IBM | Q8_0 | fits | 5.0 GB | 128k | 39tok/sest. | 2201 tok/s | chat, coding |
| Kimi VL A3B Moonshot AI | mlx-6bit | fits | 13.9 GB | 51k84k with q8 cache | 39tok/sest. | 2867 tok/s | chat, coding, agents |
| Mellum2.1 12B A2.5B Thinking JetBrains | Q8_0 | fits | 12.9 GB | 128k | 38tok/sest. | 3401 tok/s | chat, coding, agents, long documents |
| DeepSeek R1 Distill Qwen 7B DeepSeek | Q8_0 | fits | 8.7 GB | 118k128k with q8 cache | 21tok/sest. | 1173 tok/s | chat |
| DeepSeek R1 Distill Llama 8B DeepSeek | Q8_0 | fits | 9.7 GB | 51k93k with q8 cache | 19tok/sest. | 1087 tok/s | chat |
| Ministral 3 8B Reasoning 2512 Mistral AI | Q8_0 | fits | 10.2 GB | 45k82k with q8 cache | 18tok/sest. | 984 tok/s | chat |
| GLM 4.6V Flash Z.ai | Q8_0 | fits | 10.4 GB | 119k128k with q8 cache | 17tok/sest. | 851 tok/s | chat |
| GLM 4.1V 9B Thinking Z.ai | mlx-8bit | fits | 12.0 GB | 64k | 15tok/sest. | 851 tok/s | – |
| DeepSeek R1 Distill Qwen 14B DeepSeek | Q6_K | fits | 13.5 GB | 17k32k with q8 cache | 13tok/sest. | 590 tok/s | – |
| Devstral Small 2507 Mistral AI | Q4_K_M | fits | 15.3 GB | 8k15k with q8 cache | 11tok/sest. | 397 tok/s | – |
| Magistral Small 2506 Mistral AI | Q4_K_M | fits | 15.3 GB | 8k15k with q8 cache | 11tok/sest. | 397 tok/s | – |
| Magistral Small 2507 Mistral AI | Q4_K_M | fits | 15.3 GB | 8k15k with q8 cache | 11tok/sest. | 397 tok/s | – |
| Magistral Small 2509 Mistral AI | Q4_K_M | fits | 15.3 GB | 8k15k with q8 cache | 11tok/sest. | 391 tok/s | – |
| Qwen 3.8 27B Alibaba Qwen | UD-Q4_K_M | offload | 16.7 GB | – | 9tok/sest. | 329 tok/s | – |
| Qwen 3.5 122B-A10B Alibaba Qwen | UD-Q2_K_XL | offload | 40.0 GB | – | 12tok/sest. | 572 tok/s | – |
| Qwen 3.6 27B Alibaba Qwen | Q4_K_M | offload | 17.0 GB | – | 8tok/sest. | 325 tok/s | – |
| Gemma 4 31B | Q4_K_M | offload | 19.2 GB | – | 6tok/sest. | 265 tok/s | – |
| Mistral Small 4 119B Mistral AI | UD-Q2_K_XL | offload | 38.4 GB | – | 17tok/sest. | 759 tok/s | chat |
| Qwen 3.6 35B-A3B Alibaba Qwen | MXFP4 | offload | 21.2 GB | – | 32tok/sest. | 2465 tok/s | chat, coding |
| Qwen 3.5 27B Alibaba Qwen | Q4_K_M | offload | 17.0 GB | – | 8tok/sest. | 326 tok/s | – |
| Gemma 4 26B-A4B | UD-Q4_K_M | offload | 16.9 GB | – | 29tok/sest. | 2137 tok/s | chat, coding |
| Qwen 3.5 35B-A3B Alibaba Qwen | Q4_K_M | offload | 21.5 GB | – | 31tok/sest. | 2447 tok/s | chat, coding |
| GLM 4.7 Flash Z.ai | Q4_K_M | offload | 18.2 GB | – | 22tok/sest. | 1500 tok/s | chat |
| Nemotron 3.5 Lightning 30B-A3B NVIDIA | UD-Q6_K_XL | offload | 33.4 GB | – | 14tok/sest. | 2069 tok/s | – |
| Qwen3 Coder 30B-A3B Alibaba Qwen | Q4_K_M | offload | 18.8 GB | – | 28tok/sest. | 1906 tok/s | chat |
| Kimi Linear 48B-A3B Moonshot AI | Q4_K_M | offload | 28.8 GB | – | 26tok/sest. | 2011 tok/s | chat, coding, long documents |
| Qwen3 32B Alibaba Qwen | Q4_K_M | offload | 21.2 GB | – | 5tok/sest. | 222 tok/s | – |
| Gemma 3 27B | Q4_K_M | offload | 17.2 GB | – | 8tok/sest. | 328 tok/s | – |
| DeepSeek R1 Distill Qwen 32B DeepSeek | Q4_K_M | offload | 21.2 GB | – | 5tok/sest. | 232 tok/s | – |
| LFM2.5 24B A2B Liquid AI | Q4_K_M | offload | 15.7 GB | 4k8k with q8 cache | 53tok/sest. | 3666 tok/s | chat, coding |
| granite 4.2 30b IBM | Q4_K_M | offload | 19.5 GB | – | 6tok/sest. | 273 tok/s | – |
| DeepSeek V4 Flash DeepSeek | UD-Q2_K_XL | won't run | 91.6 GB | – | – | – | – |
| GLM 5.3 Flash Z.ai | UD-Q2_K_XL | won't run | 102.2 GB | – | – | – | – |
| Qwen 3.8 Flash Next Alibaba Qwen | UD-Q2_K_XL | won't run | 74.5 GB | – | – | – | – |
| MiniMax M2.7 MiniMax | UD-Q2_K_XL | won't run | 72.8 GB | – | – | – | – |
| Nemotron 3 Super 120B-A12B NVIDIA | UD-Q2_K_XL | won't run | 51.9 GB | – | – | – | – |
| Qwen3 Coder Next Alibaba Qwen | Q4_K_M | won't run | 46.2 GB | – | – | – | – |
| gpt-oss 120B OpenAI | Q4_K_M | won't run | 59.5 GB | – | – | – | – |
| GLM 4.5 Air Z.ai | Q2_K | won't run | 44.4 GB | – | – | – | – |
| Qwen3 Next 80B-A3B Alibaba Qwen | Q4_K_M | won't run | 46.2 GB | – | – | – | – |
| Llama 3.3 70B Meta | Q4_K_M | won't run | 42.8 GB | – | – | – | – |
| DeepSeek R1 DeepSeek | Q4_K_M | won't run | 377.9 GB | – | – | – | – |
| DeepSeek R1 0528 DeepSeek | Q4_K_M | won't run | 378.4 GB | – | – | – | – |
| DeepSeek R1 Distill Llama 70B DeepSeek | Q4_K_M | won't run | 42.8 GB | – | – | – | – |
| DeepSeek V3 0324 DeepSeek | Q4_K_M | won't run | 377.9 GB | – | – | – | – |
| DeepSeek V3.1 DeepSeek | Q4_K_M | won't run | 378.8 GB | – | – | – | – |
| DeepSeek V3.2 DeepSeek | Q4_K_M | won't run | 378.8 GB | – | – | – | – |
| DeepSeek V4 Flash 0731 DeepSeek | MXFP4 | won't run | 147.1 GB | – | – | – | – |
| DeepSeek V4 Flash Vision Exp DeepSeek | MXFP4 | won't run | 155.8 GB | – | – | – | – |
| Devstral 2 123B Instruct 2512 Mistral AI | Q8_0 | won't run | 127.2 GB | – | – | – | – |
| GLM 4.5V Z.ai | Q4_K_M | won't run | 61.4 GB | – | – | – | – |
| GLM 4.6V Z.ai | Q4_K_M | won't run | 67.8 GB | – | – | – | – |
| GLM 4.7 Z.ai | Q4_K_M | won't run | 205.2 GB | – | – | – | – |
| GLM 5.1 Z.ai | MXFP4 | won't run | 421.4 GB | – | – | – | – |
| GLM 5.2 Z.ai | UD-Q4_K_M | won't run | 435.3 GB | – | – | – | – |
| GLM 5.3 Z.ai | Q8_0 | won't run | 747.8 GB | – | – | – | – |
| Kimi K2 Moonshot AI | Q4_K_M | won't run | 579.4 GB | – | – | – | – |
| Kimi K2 Instruct 0905 Moonshot AI | Q4_K_M | won't run | 579.9 GB | – | – | – | – |
| Kimi K2 Thinking Moonshot AI | Q4_K_M | won't run | 579.9 GB | – | – | – | – |
| Kimi K2.5 Moonshot AI | Q4_K_M | won't run | 579.9 GB | – | – | – | – |
| MiniMax M2 MiniMax | Q4_K_M | won't run | 131.5 GB | – | – | – | – |
| MiniMax M2.1 MiniMax | Q4_K_M | won't run | 131.5 GB | – | – | – | – |
| MiniMax M2.5 MiniMax | MXFP4 | won't run | 118.0 GB | – | – | – | – |
| MiniMax M3 MiniMax | UD-Q4_K_M | won't run | 248.4 GB | – | – | – | – |
| Mistral Medium 3.5 128B Mistral AI | Q4_K_M | won't run | 73.3 GB | – | – | – | – |
Specifications
From the manufacturer's published specifications; sources are linked where we have them.
- GPU
- GeForce RTX 4060 Ti 16GB
- Memory
- 16 GB GDDR6
- Memory bus
- 128-bit
- Memory bandwidth
- 288 GB/s
- FP16 compute
- 44 TFLOPS
- Software
- CUDA
- Board power
- 165 W
- PCIe generation
- Gen 4
- PCIe lanes
- x8
Compare with
Questions
- How much memory does the NVIDIA GeForce RTX 4060 Ti 16GB have for AI models?
- The NVIDIA GeForce RTX 4060 Ti 16GB has 16 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 16GB can run locally?
- Devstral Small 2 24B (Q4_K_M) fits fully in its memory at 8k context and generates at about 11 tok/s, an estimate from its memory bandwidth. 37 of the 113 models we track fit at that context.
- Can the NVIDIA GeForce RTX 4060 Ti 16GB run DeepSeek V4 Flash?
- No: even at UD-Q2_K_XL, DeepSeek V4 Flash needs about 92 GB at 8k context, more than the NVIDIA GeForce RTX 4060 Ti 16GB and system RAM can hold.
- How much does the NVIDIA GeForce RTX 4060 Ti 16GB cost in the UK?
- The best new price for the NVIDIA GeForce RTX 4060 Ti 16GB in the UK is £977 at Amazon; used, it starts at £496 (asking price).
Speeds on this page are for 8k context. See the best hardware for local AI at every budget.