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 128k context it runs 18 of the 113 open models we track fully in memory; the most capable is gpt-oss 20B at Q4_K_M, at about 19 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 @128k | Prefill | Good for |
|---|---|---|---|---|---|---|---|
| gpt-oss 20B OpenAI | Q4_K_M | fits | 15.3 GB | 128k | 19tok/sest. | 930 tok/s | chat, coding, agents, long documents |
| Gemma 4 12B | Q6_K | fits | 12.9 GB | 245k256k with q8 cache | 15tok/sest. | 597 tok/s | chat |
| Qwen 3.5 9B Alibaba Qwen | Q8_0 | fits | 14.4 GB | 155k256k with q8 cache | 13tok/sest. | 710 tok/s | chat |
| Qwen 3.5 4B Alibaba Qwen | Q8_0 | fits | 9.7 GB | 256k | 20tok/sest. | 1106 tok/s | chat, coding, long documents |
| Gemma 4 E4B | Q8_0 | fits | 12.6 GB | 128k | 15tok/sest. | 995 tok/s | chat |
| LFM2.5 8B-A1B Liquid AI | UD-Q6_K | fits | 14.1 GB | 125k | 15tok/sest. | 1330 tok/s | chat, coding |
| Qwen2.5 0.5B Alibaba Qwen | Q8_0 | fits | 3.4 GB | 32k | 83tok/sest. | 2989 tok/s | chat, coding, long documents |
| Qwen3.5 0.8B Alibaba Qwen | Q8_0 | fits | 3.7 GB | 256k | 73tok/sest. | 3985 tok/s | chat, coding, agents, long documents |
| Qwen3.5 2B Alibaba Qwen | Q5_K_M | fits | 4.3 GB | 256k | 58tok/sest. | 2549 tok/s | chat, coding, agents, long documents |
| DeepSeek R1 Distill Qwen 1.5B DeepSeek | Q8_0 | fits | 6.7 GB | 128k | 32tok/sest. | 1335 tok/s | chat, coding, long documents |
| Mellum2.1 12B A2.5B Thinking JetBrains | Q8_0 | fits | 15.3 GB | 128k | 23tok/sest. | 1539 tok/s | chat, coding, agents, long documents |
| MiniCPM5 2B openbmb | Q8_0 | fits | 9.2 GB | 128k | 22tok/sest. | 718 tok/s | chat, coding, agents, long documents |
| LFM2.5 2.6B Liquid AI | Q8_0 | fits | 11.6 GB | 128k | 16tok/sest. | 921 tok/s | chat, coding, agents, long documents |
| LFM2.5 VL 3B Liquid AI | Q8_0 | fits | 11.6 GB | 32k | 16tok/sest. | 886 tok/s | chat, coding, agents, long documents |
| GLM 4.6V Flash Z.ai | Q4_K_M | fits | 12.2 GB | 128k | 16tok/sest. | 312 tok/s | chat, coding, long documents |
| DeepSeek R1 Distill Qwen 7B DeepSeek | Q6_K | fits | 14.3 GB | 128k | 13tok/sest. | 477 tok/s | chat, coding |
| granite vision 4.1 4b IBM | Q8_0 | fits | 14.8 GB | 128k | 12tok/sest. | 568 tok/s | chat, coding |
| granite 4.2 3b IBM | Q8_0 | fits | 15.1 GB | 128k | 12tok/sest. | 580 tok/s | chat, coding |
| Qwen 3.8 27B Alibaba Qwen | UD-Q4_K_M | offload | 24.9 GB | – | 4tok/sest. | 184 tok/s | – |
| Qwen 3.6 27B Alibaba Qwen | Q4_K_M | offload | 25.3 GB | – | 4tok/sest. | 183 tok/s | – |
| Gemma 4 31B | Q4_K_M | offload | 29.3 GB | – | 3tok/sest. | 162 tok/s | – |
| Qwen 3.6 35B-A3B Alibaba Qwen | MXFP4 | offload | 24.2 GB | – | 12tok/sest. | 908 tok/s | chat, coding |
| Qwen 3.5 27B Alibaba Qwen | Q4_K_M | offload | 25.2 GB | – | 4tok/sest. | 183 tok/s | – |
| Gemma 4 26B-A4B | UD-Q4_K_M | offload | 19.9 GB | – | 12tok/sest. | 1225 tok/s | chat, coding |
| Qwen 3.5 35B-A3B Alibaba Qwen | Q4_K_M | offload | 24.5 GB | – | 12tok/sest. | 903 tok/s | chat, coding |
| GLM 4.7 Flash Z.ai | Q4_K_M | offload | 25.1 GB | – | 5tok/sest. | 421 tok/s | chat |
| Devstral Small 2 24B Mistral AI | Q4_K_M | offload | 34.8 GB | 8k15k with q8 cache | 2tok/sest. | 142 tok/s | – |
| Nemotron 3.5 Lightning 30B-A3B NVIDIA | UD-Q6_K_XL | offload | 34.8 GB | – | 11tok/sest. | 1035 tok/s | – |
| Kimi Linear 48B-A3B Moonshot AI | Q4_K_M | offload | 30.5 GB | – | 17tok/sest. | 611 tok/s | chat, coding, long documents |
| Qwen3 Coder 30B-A3B Alibaba Qwen | Q4_K_M | offload | 30.7 GB | – | 3tok/sest. | 236 tok/s | chat |
| Gemma 3 27B | Q4_K_M | offload | 27.3 GB | – | 3tok/sest. | 216 tok/s | – |
| Mistral Small 3.2 24B Mistral AI | Q4_K_M | offload | 34.8 GB | 8k15k with q8 cache | 2tok/sest. | 142 tok/s | – |
| Ministral 3 14B Mistral AI | Q4_K_M | offload | 29.1 GB | 43k79k with q8 cache | 3tok/sest. | 196 tok/s | chat |
| Qwen3 14B Alibaba Qwen | Q4_K_M | offload | 29.8 GB | 38k40k with q8 cache | 3tok/sest. | 165 tok/s | chat |
| DeepSeek R1 0528 Qwen3 8B DeepSeek | Q4_K_M | offload | 24.1 GB | 68k124k with q8 cache | 4tok/sest. | 277 tok/s | chat |
| Qwen3 8B Alibaba Qwen | Q4_K_M | offload | 24.1 GB | 40k | 4tok/sest. | 277 tok/s | chat |
| Phi-4 14B Microsoft | Q4_K_M | offload | 34.7 GB | 16k | 2tok/sest. | 155 tok/s | chat |
| Ministral 3 8B Mistral AI | Q4_K_M | offload | 23.3 GB | 71k129k with q8 cache | 4tok/sest. | 285 tok/s | chat |
| Granite 4.2 8B IBM | Q4_K_M | offload | 26.6 GB | 58k107k with q8 cache | 3tok/sest. | 240 tok/s | chat |
| Llama 3.1 8B Meta | Q4_K_M | offload | 22.0 GB | 77k128k with q8 cache | 5tok/sest. | 317 tok/s | chat |
| Kimi VL A3B Moonshot AI | mlx-5bit | offload | 16.3 GB | 103k128k with q8 cache | 15tok/sest. | 932 tok/s | chat, coding, agents, long documents |
| GLM 4.1V 9B Thinking Z.ai | mlx-8bit | offload | 17.4 GB | 64k | 8tok/sest. | 288 tok/s | – |
| DeepSeek R1 Distill Llama 8B DeepSeek | Q4_K_M | offload | 22.0 GB | 77k128k with q8 cache | 5tok/sest. | 317 tok/s | chat |
| LFM2.5 24B A2B Liquid AI | Q4_K_M | offload | 25.7 GB | 4k8k with q8 cache | 5tok/sest. | 579 tok/s | chat, coding |
| Ministral 3 8B Reasoning 2512 Mistral AI | Q4_K_M | offload | 23.3 GB | 71k129k with q8 cache | 4tok/sest. | 285 tok/s | chat |
| DeepSeek R1 Distill Qwen 14B DeepSeek | Q4_K_M | offload | 33.8 GB | 32k60k with q8 cache | 2tok/sest. | 139 tok/s | chat |
| Devstral Small 2507 Mistral AI | Q4_K_M | offload | 34.8 GB | 8k15k with q8 cache | 2tok/sest. | 143 tok/s | – |
| Magistral Small 2506 Mistral AI | Q4_K_M | offload | 34.8 GB | 8k15k with q8 cache | 2tok/sest. | 143 tok/s | – |
| Magistral Small 2507 Mistral AI | Q4_K_M | offload | 34.8 GB | 8k15k with q8 cache | 2tok/sest. | 143 tok/s | – |
| Magistral Small 2509 Mistral AI | Q4_K_M | offload | 34.8 GB | 8k15k with q8 cache | 2tok/sest. | 142 tok/s | – |
| DeepSeek V4 Flash DeepSeek | UD-Q2_K_XL | won't run | 102.4 GB | – | – | – | – |
| GLM 5.3 Flash Z.ai | UD-Q2_K_XL | won't run | 104.2 GB | – | – | – | – |
| Qwen 3.8 Flash Next Alibaba Qwen | UD-Q2_K_XL | won't run | 78.0 GB | – | – | – | – |
| MiniMax M2.7 MiniMax | UD-Q2_K_XL | won't run | 102.6 GB | – | – | – | – |
| Qwen 3.5 122B-A10B Alibaba Qwen | UD-Q2_K_XL | won't run | 43.6 GB | – | – | – | – |
| Nemotron 3 Super 120B-A12B NVIDIA | UD-Q2_K_XL | won't run | 53.5 GB | – | – | – | – |
| Qwen3 Coder Next Alibaba Qwen | Q4_K_M | won't run | 49.7 GB | – | – | – | – |
| gpt-oss 120B OpenAI | Q4_K_M | won't run | 64.4 GB | – | – | – | – |
| Mistral Small 4 119B Mistral AI | UD-Q2_K_XL | won't run | 41.7 GB | – | – | – | – |
| GLM 4.5 Air Z.ai | Q2_K | won't run | 66.7 GB | – | – | – | – |
| Qwen3 Next 80B-A3B Alibaba Qwen | Q4_K_M | won't run | 49.7 GB | – | – | – | – |
| Qwen3 32B Alibaba Qwen | Q4_K_M | won't run | 51.9 GB | – | – | – | – |
| DeepSeek R1 Distill Qwen 32B DeepSeek | Q4_K_M | won't run | 51.9 GB | – | – | – | – |
| Llama 3.3 70B Meta | Q4_K_M | won't run | 81.1 GB | – | – | – | – |
| DeepSeek R1 DeepSeek | Q4_K_M | won't run | 386.7 GB | – | – | – | – |
| DeepSeek R1 0528 DeepSeek | Q4_K_M | won't run | 387.2 GB | – | – | – | – |
| DeepSeek R1 Distill Llama 70B DeepSeek | Q4_K_M | won't run | 81.1 GB | – | – | – | – |
| DeepSeek V3 0324 DeepSeek | Q4_K_M | won't run | 386.7 GB | – | – | – | – |
| DeepSeek V3.1 DeepSeek | Q4_K_M | won't run | 387.6 GB | – | – | – | – |
| DeepSeek V3.2 DeepSeek | Q4_K_M | won't run | 387.6 GB | – | – | – | – |
| DeepSeek V4 Flash 0731 DeepSeek | MXFP4 | won't run | 157.8 GB | – | – | – | – |
| DeepSeek V4 Flash Vision Exp DeepSeek | MXFP4 | won't run | 166.6 GB | – | – | – | – |
| Devstral 2 123B Instruct 2512 Mistral AI | Q8_0 | won't run | 169.2 GB | – | – | – | – |
| GLM 4.5V Z.ai | Q4_K_M | won't run | 83.7 GB | – | – | – | – |
| GLM 4.6V Z.ai | Q4_K_M | won't run | 90.1 GB | – | – | – | – |
| GLM 4.7 Z.ai | Q4_K_M | won't run | 249.0 GB | – | – | – | – |
| GLM 5.1 Z.ai | MXFP4 | won't run | 432.4 GB | – | – | – | – |
| GLM 5.2 Z.ai | UD-Q4_K_M | won't run | 446.3 GB | – | – | – | – |
| GLM 5.3 Z.ai | Q8_0 | won't run | 758.7 GB | – | – | – | – |
| Kimi K2 Moonshot AI | Q4_K_M | won't run | 588.2 GB | – | – | – | – |
| Kimi K2 Instruct 0905 Moonshot AI | Q4_K_M | won't run | 588.6 GB | – | – | – | – |
| Kimi K2 Thinking Moonshot AI | Q4_K_M | won't run | 588.6 GB | – | – | – | – |
| Kimi K2.5 Moonshot AI | Q4_K_M | won't run | 588.6 GB | – | – | – | – |
| MiniMax M2 MiniMax | Q4_K_M | won't run | 161.3 GB | – | – | – | – |
| MiniMax M2.1 MiniMax | Q4_K_M | won't run | 161.3 GB | – | – | – | – |
| MiniMax M2.5 MiniMax | MXFP4 | won't run | 147.7 GB | – | – | – | – |
| MiniMax M3 MiniMax | UD-Q4_K_M | won't run | 263.2 GB | – | – | – | – |
| Mistral Medium 3.5 128B Mistral AI | Q4_K_M | won't run | 115.2 GB | – | – | – | – |
| granite 4.2 30b IBM | Q4_K_M | won't run | 50.2 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?
- gpt-oss 20B (Q4_K_M) fits fully in its memory at 128k context and generates at about 19 tok/s, an estimate from its memory bandwidth. 18 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 102 GB at 128k 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 128k context. See the best hardware for local AI at every budget.