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 32k context it runs 30 of the 113 open models we track fully in memory; the most capable is gpt-oss 20B at MXFP4, at about 33 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 @32k | Prefill | Good for |
|---|---|---|---|---|---|---|---|
| gpt-oss 20B OpenAI | MXFP4 | fits | 12.9 GB | 116k128k with q8 cache | 33tok/sest. | 1702 tok/s | chat, coding, agents, long documents |
| Gemma 4 12B | Q8_0 | fits | 13.5 GB | 120k193k with q8 cache | 13tok/sest. | 741 tok/s | – |
| Qwen 3.5 9B Alibaba Qwen | Q8_0 | fits | 10.8 GB | 155k256k with q8 cache | 17tok/sest. | 923 tok/s | chat |
| Qwen3 14B Alibaba Qwen | Q4_K_M | fits | 14.3 GB | 38k40k with q8 cache | 12tok/sest. | 461 tok/s | chat |
| Ministral 3 14B Mistral AI | Q5_K_M | fits | 14.8 GB | 35k64k with q8 cache | 12tok/sest. | 513 tok/s | chat |
| DeepSeek R1 0528 Qwen3 8B DeepSeek | Q8_0 | fits | 13.5 GB | 44k81k with q8 cache | 13tok/sest. | 760 tok/s | chat |
| Qwen3 8B Alibaba Qwen | Q8_0 | fits | 13.5 GB | 40k | 13tok/sest. | 760 tok/s | chat |
| Qwen 3.5 4B Alibaba Qwen | Q8_0 | fits | 6.1 GB | 256k | 32tok/sest. | 1727 tok/s | chat, coding, long documents |
| Ministral 3 8B Mistral AI | Q8_0 | fits | 13.5 GB | 45k82k with q8 cache | 13tok/sest. | 734 tok/s | chat |
| Granite 4.2 8B IBM | Q8_0 | fits | 14.6 GB | 36k67k with q8 cache | 12tok/sest. | 699 tok/s | chat |
| Gemma 4 E4B | Q8_0 | fits | 9.4 GB | 128k | 20tok/sest. | 1159 tok/s | chat |
| LFM2.5 8B-A1B Liquid AI | Q8_0 | fits | 10.8 GB | 119k125k with q8 cache | 40tok/sest. | 3792 tok/s | chat, coding |
| Llama 3.1 8B Meta | Q6_K | fits | 11.0 GB | 65k118k with q8 cache | 16tok/sest. | 803 tok/s | chat |
| Qwen2.5 0.5B Alibaba Qwen | Q8_0 | fits | 1.8 GB | 32k | 191tok/sest. | 8259 tok/s | chat, coding, long documents |
| Qwen3.5 0.8B Alibaba Qwen | Q8_0 | fits | 2.0 GB | 256k | 145tok/sest. | 7758 tok/s | chat, coding, agents, long documents |
| Qwen3.5 2B Alibaba Qwen | Q5_K_M | fits | 2.6 GB | 256k | 96tok/sest. | 3699 tok/s | chat, coding, agents, long documents |
| DeepSeek R1 Distill Qwen 1.5B DeepSeek | Q8_0 | fits | 3.5 GB | 128k | 63tok/sest. | 3107 tok/s | chat, coding, long documents |
| MiniCPM5 2B openbmb | Q8_0 | fits | 4.7 GB | 128k | 44tok/sest. | 1856 tok/s | chat, coding, agents, long documents |
| LFM2.5 2.6B Liquid AI | Q8_0 | fits | 5.4 GB | 128k | 37tok/sest. | 2102 tok/s | chat, coding, agents, long documents |
| LFM2.5 VL 3B Liquid AI | Q8_0 | fits | 5.4 GB | 32k | 37tok/sest. | 1927 tok/s | chat, coding, agents, long documents |
| Mellum2.1 12B A2.5B Thinking JetBrains | Q8_0 | fits | 13.4 GB | 128k | 34tok/sest. | 2738 tok/s | chat, coding, agents, long documents |
| Kimi VL A3B Moonshot AI | mlx-6bit | fits | 14.7 GB | 51k84k with q8 cache | 30tok/sest. | 2057 tok/s | chat, coding, agents |
| granite vision 4.1 4b IBM | Q8_0 | fits | 6.8 GB | 128k | 28tok/sest. | 1347 tok/s | chat, coding |
| granite 4.2 3b IBM | Q8_0 | fits | 7.0 GB | 128k | 27tok/sest. | 1411 tok/s | chat, coding |
| DeepSeek R1 Distill Qwen 7B DeepSeek | Q8_0 | fits | 10.2 GB | 118k128k with q8 cache | 18tok/sest. | 908 tok/s | chat |
| GLM 4.6V Flash Z.ai | Q8_0 | fits | 11.5 GB | 119k128k with q8 cache | 16tok/sest. | 632 tok/s | chat |
| DeepSeek R1 Distill Llama 8B DeepSeek | Q8_0 | fits | 12.8 GB | 51k93k with q8 cache | 14tok/sest. | 803 tok/s | chat |
| GLM 4.1V 9B Thinking Z.ai | mlx-8bit | fits | 13.1 GB | 64k | 14tok/sest. | 632 tok/s | – |
| Ministral 3 8B Reasoning 2512 Mistral AI | Q8_0 | fits | 13.5 GB | 45k82k with q8 cache | 13tok/sest. | 734 tok/s | chat |
| DeepSeek R1 Distill Qwen 14B DeepSeek | Q4_K_M | fits | 15.3 GB | 32k60k with q8 cache | 12tok/sest. | 434 tok/s | chat |
| Qwen 3.8 27B Alibaba Qwen | UD-Q4_K_M | offload | 18.4 GB | – | 7tok/sest. | 287 tok/s | – |
| Qwen 3.5 122B-A10B Alibaba Qwen | UD-Q2_K_XL | offload | 40.7 GB | – | 10tok/sest. | 463 tok/s | – |
| Qwen 3.6 27B Alibaba Qwen | Q4_K_M | offload | 18.7 GB | – | 7tok/sest. | 284 tok/s | – |
| Gemma 4 31B | Q4_K_M | offload | 21.2 GB | – | 5tok/sest. | 236 tok/s | – |
| Mistral Small 4 119B Mistral AI | UD-Q2_K_XL | offload | 39.0 GB | – | 14tok/sest. | 523 tok/s | chat |
| Qwen 3.6 35B-A3B Alibaba Qwen | MXFP4 | offload | 21.8 GB | – | 25tok/sest. | 1862 tok/s | chat, coding |
| Qwen 3.5 27B Alibaba Qwen | Q4_K_M | offload | 18.6 GB | – | 7tok/sest. | 285 tok/s | – |
| Gemma 4 26B-A4B | UD-Q4_K_M | offload | 17.5 GB | – | 24tok/sest. | 1875 tok/s | chat, coding |
| Qwen 3.5 35B-A3B Alibaba Qwen | Q4_K_M | offload | 22.1 GB | – | 24tok/sest. | 1849 tok/s | chat, coding |
| GLM 4.7 Flash Z.ai | Q4_K_M | offload | 19.6 GB | – | 15tok/sest. | 1030 tok/s | chat |
| Devstral Small 2 24B Mistral AI | Q4_K_M | offload | 19.2 GB | 8k15k with q8 cache | 6tok/sest. | 295 tok/s | – |
| Nemotron 3.5 Lightning 30B-A3B NVIDIA | UD-Q6_K_XL | offload | 33.7 GB | – | 13tok/sest. | 1731 tok/s | – |
| Kimi Linear 48B-A3B Moonshot AI | Q4_K_M | offload | 29.2 GB | – | 24tok/sest. | 1391 tok/s | chat, coding, long documents |
| Qwen3 Coder 30B-A3B Alibaba Qwen | Q4_K_M | offload | 21.2 GB | – | 13tok/sest. | 870 tok/s | chat |
| Qwen3 32B Alibaba Qwen | Q4_K_M | offload | 27.3 GB | – | 3tok/sest. | 145 tok/s | – |
| Gemma 3 27B | Q4_K_M | offload | 19.2 GB | – | 6tok/sest. | 297 tok/s | – |
| Mistral Small 3.2 24B Mistral AI | Q4_K_M | offload | 19.2 GB | 8k15k with q8 cache | 6tok/sest. | 295 tok/s | – |
| DeepSeek R1 Distill Qwen 32B DeepSeek | Q4_K_M | offload | 27.4 GB | – | 3tok/sest. | 166 tok/s | – |
| Phi-4 14B Microsoft | Q4_K_M | offload | 15.4 GB | 16k | 11tok/sest. | 463 tok/s | chat |
| LFM2.5 24B A2B Liquid AI | Q4_K_M | offload | 17.7 GB | 4k8k with q8 cache | 21tok/sest. | 1941 tok/s | chat, coding |
| Devstral Small 2507 Mistral AI | Q4_K_M | offload | 19.2 GB | 8k15k with q8 cache | 6tok/sest. | 299 tok/s | – |
| Magistral Small 2506 Mistral AI | Q4_K_M | offload | 19.2 GB | 8k15k with q8 cache | 6tok/sest. | 299 tok/s | – |
| Magistral Small 2507 Mistral AI | Q4_K_M | offload | 19.2 GB | 8k15k with q8 cache | 6tok/sest. | 299 tok/s | – |
| Magistral Small 2509 Mistral AI | Q4_K_M | offload | 19.2 GB | 8k15k with q8 cache | 6tok/sest. | 295 tok/s | – |
| granite 4.2 30b IBM | Q4_K_M | offload | 25.7 GB | – | 3tok/sest. | 196 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 | – | – | – | – |
| 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 | – | – | – | – |
| 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 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 V3.2 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 | Q8_0 | won't run | 135.6 GB | – | – | – | – |
| GLM 4.5V Z.ai | Q4_K_M | won't run | 65.9 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 5.1 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 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 | UD-Q4_K_M | won't run | 251.4 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 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 (MXFP4) fits fully in its memory at 32k context and generates at about 33 tok/s, an estimate from its memory bandwidth. 30 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 94 GB at 32k 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 32k context. See the best hardware for local AI at every budget.