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 64k context it runs 49 of the 113 open models we track fully in memory; the most capable is Qwen 3.8 27B at Q6_K, at about 6 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 | 3 h ago | Buy |
| eBayMSI Gaming X Slim NVIDIA GeForce RTX 4060 Ti 16GB Graphics Card | used, asking price | £491+ £9 delivery | In stock | 3 h ago | Buy |
| AmazonASUS ProArt GeForce RTX 4060 Ti 16GB OC Edition GDDR6 Graphics Card | New | £977 | In stock | 1 h ago | Buy |
| AmazonASUS ProArt GeForce RTX 4060 Ti 16GB OC Edition GDDR6 Graphics Card | New | £1,169 | In stock | 1 h ago | Buy |
| AmazonMSI Gaming GeForce RTX 4060 Ti 16GB GDRR6 Boost Clock: 2625 MHz 128-Bit… | New | £1,178 | In stock | 1 h ago | Buy |
Price history
Lowest price shown each day over the last 90 days.
Not enough price history yet.
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 @64k | Prefill | Good for |
|---|---|---|---|---|---|---|---|
| Qwen 3.8 27B Alibaba Qwen | Q6_K | fits | 27.8 GB | 107k187k with q8 cache | 6tok/sest. | 289 tok/s | – |
| Qwen 3.6 27B Alibaba Qwen | Q6_K | fits | 26.6 GB | 125k219k with q8 cache | 7tok/sest. | 289 tok/s | – |
| Gemma 4 31B | Q6_K | fits | 30.7 GB | 64k122k with q8 cache | 6tok/sest. | 264 tok/s | – |
| Qwen 3.6 35B-A3B Alibaba Qwen | UD-Q6_K | fits | 30.1 GB | 92k144k with q8 cache | 28tok/sest. | 1742 tok/s | chat, coding, agents, long documents |
| Qwen 3.5 27B Alibaba Qwen | Q6_K | fits | 26.5 GB | 126k221k with q8 cache | 7tok/sest. | 289 tok/s | – |
| Gemma 4 26B-A4B | Q8_0 | fits | 27.9 GB | 176k256k with q8 cache | 19tok/sest. | 1783 tok/s | chat, coding, long documents |
| Qwen 3.5 35B-A3B Alibaba Qwen | Q6_K | fits | 29.7 GB | 109k170k with q8 cache | 28tok/sest. | 1742 tok/s | chat, coding, agents, long documents |
| GLM 4.7 Flash Z.ai | Q6_K | fits | 27.8 GB | 116k198k with q8 cache | 14tok/sest. | 864 tok/s | chat |
| Devstral Small 2 24B Mistral AI | Q6_K | fits | 29.5 GB | 72k131k with q8 cache | 6tok/sest. | 285 tok/s | – |
| Kimi Linear 48B-A3B Moonshot AI | Q4_K_M | fits | 30.0 GB | 122k167k with q8 cache | 45tok/sest. | 1399 tok/s | chat, coding, long documents |
| gpt-oss 20B OpenAI | MXFP4 | fits | 14.3 GB | 128k | 27tok/sest. | 1333 tok/s | chat, coding, agents, long documents |
| Qwen3 Coder 30B-A3B Alibaba Qwen | Q5_K_M | fits | 27.7 GB | 95k170k with q8 cache | 13tok/sest. | 623 tok/s | chat, coding |
| Gemma 4 12B | Q8_0 | fits | 14.6 GB | 256k | 13tok/sest. | 686 tok/s | – |
| Qwen 3.5 9B Alibaba Qwen | Q8_0 | fits | 12.4 GB | 256k | 15tok/sest. | 839 tok/s | chat |
| Gemma 3 27B | Q6_K | fits | 27.5 GB | 102k128k with q8 cache | 6tok/sest. | 324 tok/s | – |
| Mistral Small 3.2 24B Mistral AI | Q6_K | fits | 29.5 GB | 72k128k with q8 cache | 6tok/sest. | 285 tok/s | – |
| Ministral 3 14B Mistral AI | Q8_0 | fits | 24.8 GB | 100k183k with q8 cache | 7tok/sest. | 401 tok/s | – |
| Qwen3 14B Alibaba Qwen | Q8_0 | fits | 26.1 GB | 40k | 7tok/sest. | 351 tok/s | – |
| DeepSeek R1 0528 Qwen3 8B DeepSeek | Q8_0 | fits | 18.6 GB | 128k | 10tok/sest. | 555 tok/s | chat |
| Qwen3 8B Alibaba Qwen | Q8_0 | fits | 18.6 GB | 40k | 10tok/sest. | 555 tok/s | chat |
| Phi-4 14B Microsoft | Q8_0 | fits | 28.5 GB | 16k | 6tok/sest. | 353 tok/s | – |
| Qwen 3.5 4B Alibaba Qwen | Q8_0 | fits | 7.7 GB | 256k | 27tok/sest. | 1454 tok/s | chat, coding, long documents |
| Ministral 3 8B Mistral AI | Q8_0 | fits | 18.4 GB | 153k256k with q8 cache | 10tok/sest. | 549 tok/s | chat |
| Granite 4.2 8B IBM | Q8_0 | fits | 20.2 GB | 128k | 9tok/sest. | 507 tok/s | chat |
| Gemma 4 E4B | Q8_0 | fits | 10.9 GB | 128k | 18tok/sest. | 1099 tok/s | chat |
| LFM2.5 8B-A1B Liquid AI | Q8_0 | fits | 12.9 GB | 125k | 25tok/sest. | 2345 tok/s | chat, coding |
| Llama 3.1 8B Meta | Q6_K | fits | 15.6 GB | 128k | 12tok/sest. | 596 tok/s | chat |
| Qwen2.5 0.5B Alibaba Qwen | Q8_0 | fits | 2.7 GB | 32k | 134tok/sest. | 5201 tok/s | chat, coding, long documents |
| Qwen3.5 0.8B Alibaba Qwen | Q8_0 | fits | 3.0 GB | 256k | 109tok/sest. | 5897 tok/s | chat, coding, agents, long documents |
| Qwen3.5 2B Alibaba Qwen | Q5_K_M | fits | 3.6 GB | 256k | 79tok/sest. | 3215 tok/s | chat, coding, agents, long documents |
| DeepSeek R1 Distill Qwen 1.5B DeepSeek | Q8_0 | fits | 5.0 GB | 128k | 47tok/sest. | 2154 tok/s | chat, coding, long documents |
| MiniCPM5 2B openbmb | Q8_0 | fits | 6.6 GB | 128k | 33tok/sest. | 1214 tok/s | chat, coding, agents, long documents |
| Mellum2.1 12B A2.5B Thinking JetBrains | Q8_0 | fits | 14.4 GB | 128k | 29tok/sest. | 2173 tok/s | chat, coding, agents, long documents |
| LFM2.5 2.6B Liquid AI | Q8_0 | fits | 7.9 GB | 128k | 26tok/sest. | 1472 tok/s | chat, coding, agents, long documents |
| LFM2.5 VL 3B Liquid AI | Q8_0 | fits | 7.9 GB | 32k | 26tok/sest. | 1385 tok/s | chat, coding, agents, long documents |
| Kimi VL A3B Moonshot AI | mlx-8bit | fits | 20.0 GB | 128k | 20tok/sest. | 1495 tok/s | chat, coding |
| granite vision 4.1 4b IBM | Q8_0 | fits | 9.8 GB | 128k | 20tok/sest. | 925 tok/s | chat, coding |
| granite 4.2 3b IBM | Q8_0 | fits | 10.1 GB | 128k | 19tok/sest. | 955 tok/s | chat, coding |
| DeepSeek R1 Distill Qwen 7B DeepSeek | Q8_0 | fits | 12.5 GB | 128k | 15tok/sest. | 698 tok/s | chat |
| LFM2.5 24B A2B Liquid AI | Q8_0 | fits | 30.2 GB | 70k125k with q8 cache | 14tok/sest. | 1344 tok/s | chat, coding |
| GLM 4.6V Flash Z.ai | Q8_0 | fits | 13.3 GB | 128k | 14tok/sest. | 471 tok/s | chat |
| GLM 4.1V 9B Thinking Z.ai | mlx-8bit | fits | 14.9 GB | 64k | 12tok/sest. | 471 tok/s | – |
| DeepSeek R1 Distill Llama 8B DeepSeek | Q8_0 | fits | 17.4 GB | 128k | 10tok/sest. | 596 tok/s | chat |
| Ministral 3 8B Reasoning 2512 Mistral AI | Q8_0 | fits | 18.4 GB | 153k256k with q8 cache | 10tok/sest. | 549 tok/s | chat |
| Devstral Small 2507 Mistral AI | Q5_K_M | fits | 27.1 GB | 86k128k with q8 cache | 7tok/sest. | 289 tok/s | – |
| Magistral Small 2507 Mistral AI | Q5_K_M | fits | 27.1 GB | 40k | 7tok/sest. | 289 tok/s | – |
| Magistral Small 2509 Mistral AI | Q5_K_M | fits | 27.1 GB | 86k128k with q8 cache | 7tok/sest. | 285 tok/s | – |
| DeepSeek R1 Distill Qwen 14B DeepSeek | Q8_0 | fits | 28.1 GB | 77k128k with q8 cache | 6tok/sest. | 321 tok/s | – |
| Magistral Small 2506 Mistral AI | Q6_K | fits | 29.5 GB | 40k | 6tok/sest. | 289 tok/s | – |
| Qwen 3.5 122B-A10B Alibaba Qwen | UD-Q2_K_XL | offload | 42.1 GB | – | 13tok/sest. | 502 tok/s | chat |
| Qwen3 Coder Next Alibaba Qwen | Q4_K_M | offload | 48.2 GB | – | 14tok/sest. | 1083 tok/s | chat |
| Nemotron 3 Super 120B-A12B NVIDIA | UD-Q2_K_XL | offload | 53.0 GB | – | 9tok/sest. | 519 tok/s | – |
| Mistral Small 4 119B Mistral AI | UD-Q2_K_XL | offload | 40.3 GB | – | 18tok/sest. | 505 tok/s | chat, coding |
| GLM 4.5 Air Z.ai | Q2_K | offload | 55.2 GB | – | 3tok/sest. | 133 tok/s | – |
| Qwen3 Next 80B-A3B Alibaba Qwen | Q4_K_M | offload | 48.2 GB | – | 17tok/sest. | 1236 tok/s | chat, coding |
| Nemotron 3.5 Lightning 30B-A3B NVIDIA | UD-Q6_K_XL | offload | 34.5 GB | – | 23tok/sest. | 2003 tok/s | chat, coding, long documents |
| Qwen3 32B Alibaba Qwen | Q4_K_M | offload | 35.9 GB | 40k | 4tok/sest. | 134 tok/s | – |
| DeepSeek R1 Distill Qwen 32B DeepSeek | Q4_K_M | offload | 36.0 GB | 43k80k with q8 cache | 4tok/sest. | 166 tok/s | – |
| granite 4.2 30b IBM | Q4_K_M | offload | 34.3 GB | 50k93k with q8 cache | 4tok/sest. | 200 tok/s | – |
| DeepSeek V4 Flash DeepSeek | UD-Q2_K_XL | won't run | 97.0 GB | – | – | – | – |
| GLM 5.3 Flash Z.ai | UD-Q2_K_XL | won't run | 103.5 GB | – | – | – | – |
| Qwen 3.8 Flash Next Alibaba Qwen | UD-Q2_K_XL | won't run | 76.5 GB | – | – | – | – |
| MiniMax M2.7 MiniMax | UD-Q2_K_XL | won't run | 87.1 GB | – | – | – | – |
| gpt-oss 120B OpenAI | Q4_K_M | won't run | 62.2 GB | – | – | – | – |
| Llama 3.3 70B Meta | Q4_K_M | won't run | 61.1 GB | – | – | – | – |
| DeepSeek R1 DeepSeek | Q4_K_M | won't run | 382.4 GB | – | – | – | – |
| DeepSeek R1 0528 DeepSeek | Q4_K_M | won't run | 382.9 GB | – | – | – | – |
| DeepSeek R1 Distill Llama 70B DeepSeek | Q4_K_M | won't run | 61.1 GB | – | – | – | – |
| DeepSeek V3 0324 DeepSeek | Q4_K_M | won't run | 382.4 GB | – | – | – | – |
| DeepSeek V3.1 DeepSeek | Q4_K_M | won't run | 383.3 GB | – | – | – | – |
| DeepSeek V3.2 DeepSeek | Q4_K_M | won't run | 383.3 GB | – | – | – | – |
| DeepSeek V4 Flash 0731 DeepSeek | MXFP4 | won't run | 152.5 GB | – | – | – | – |
| DeepSeek V4 Flash Vision Exp DeepSeek | MXFP4 | won't run | 161.3 GB | – | – | – | – |
| Devstral 2 123B Instruct 2512 Mistral AI | Q8_0 | won't run | 147.2 GB | – | – | – | – |
| GLM 4.5V Z.ai | Q4_K_M | won't run | 72.2 GB | – | – | – | – |
| GLM 4.6V Z.ai | Q4_K_M | won't run | 78.6 GB | – | – | – | – |
| GLM 4.7 Z.ai | Q4_K_M | won't run | 226.1 GB | – | – | – | – |
| GLM 5.1 Z.ai | MXFP4 | won't run | 426.9 GB | – | – | – | – |
| GLM 5.2 Z.ai | UD-Q4_K_M | won't run | 440.8 GB | – | – | – | – |
| GLM 5.3 Z.ai | Q8_0 | won't run | 753.3 GB | – | – | – | – |
| Kimi K2 Moonshot AI | Q4_K_M | won't run | 583.9 GB | – | – | – | – |
| Kimi K2 Instruct 0905 Moonshot AI | Q4_K_M | won't run | 584.3 GB | – | – | – | – |
| Kimi K2 Thinking Moonshot AI | Q4_K_M | won't run | 584.3 GB | – | – | – | – |
| Kimi K2.5 Moonshot AI | Q4_K_M | won't run | 584.3 GB | – | – | – | – |
| MiniMax M2 MiniMax | Q4_K_M | won't run | 145.8 GB | – | – | – | – |
| MiniMax M2.1 MiniMax | Q4_K_M | won't run | 145.8 GB | – | – | – | – |
| MiniMax M2.5 MiniMax | MXFP4 | won't run | 132.2 GB | – | – | – | – |
| MiniMax M3 MiniMax | UD-Q4_K_M | won't run | 255.7 GB | – | – | – | – |
| Mistral Medium 3.5 128B Mistral AI | Q4_K_M | won't run | 93.2 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?
- Qwen 3.8 27B (Q6_K) fits fully in its memory at 64k context and generates at about 6 tok/s, an estimate from its memory bandwidth. 49 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 97 GB at 64k 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 64k context. See the best hardware for local AI at every budget.