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 39 of the 113 open models we track fully in memory; the most capable is Qwen 3.8 27B at UD-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 @128k | Prefill | Good for |
|---|---|---|---|---|---|---|---|
| Qwen 3.8 27B Alibaba Qwen | UD-Q6_K | fits | 30.5 GB | 132k232k with q8 cache | 6tok/sest. | 243 tok/s | – |
| Qwen 3.6 27B Alibaba Qwen | Q5_K_M | fits | 28.2 GB | 166k256k with q8 cache | 6tok/sest. | 243 tok/s | – |
| Gemma 4 31B | Q4_K_M | fits | 29.7 GB | 141k256k with q8 cache | 6tok/sest. | 231 tok/s | – |
| Qwen 3.6 35B-A3B Alibaba Qwen | UD-Q5_K_M | fits | 29.0 GB | 197k256k with q8 cache | 22tok/sest. | 1183 tok/s | chat, coding, agents, long documents |
| Qwen 3.5 27B Alibaba Qwen | Q5_K_M | fits | 28.3 GB | 165k256k with q8 cache | 6tok/sest. | 243 tok/s | – |
| Gemma 4 26B-A4B | Q8_0 | fits | 29.6 GB | 176k256k with q8 cache | 15tok/sest. | 1436 tok/s | chat, coding, long documents |
| Qwen 3.5 35B-A3B Alibaba Qwen | Q5_K_M | fits | 28.9 GB | 204k256k with q8 cache | 22tok/sest. | 1183 tok/s | chat, coding, agents, long documents |
| GLM 4.7 Flash Z.ai | Q5_K_M | fits | 28.4 GB | 169k198k with q8 cache | 10tok/sest. | 558 tok/s | chat, coding |
| gpt-oss 20B OpenAI | MXFP4 | fits | 16.1 GB | 128k | 19tok/sest. | 930 tok/s | chat, coding, agents, long documents |
| Gemma 4 12B | Q8_0 | fits | 16.0 GB | 256k | 12tok/sest. | 597 tok/s | – |
| Qwen 3.5 9B Alibaba Qwen | Q8_0 | fits | 14.8 GB | 256k | 13tok/sest. | 710 tok/s | chat |
| Gemma 3 27B | Q5_K_M | fits | 30.2 GB | 128k | 6tok/sest. | 298 tok/s | – |
| Ministral 3 14B Mistral AI | Q4_K_M | fits | 29.5 GB | 135k247k with q8 cache | 6tok/sest. | 280 tok/s | chat |
| Qwen3 14B Alibaba Qwen | Q4_K_M | fits | 30.2 GB | 40k | 6tok/sest. | 238 tok/s | chat |
| DeepSeek R1 0528 Qwen3 8B DeepSeek | Q8_0 | fits | 28.0 GB | 128k | 6tok/sest. | 360 tok/s | chat |
| Qwen3 8B Alibaba Qwen | Q8_0 | fits | 28.0 GB | 40k | 6tok/sest. | 360 tok/s | chat |
| Qwen 3.5 4B Alibaba Qwen | Q8_0 | fits | 10.1 GB | 256k | 20tok/sest. | 1106 tok/s | chat, coding, long documents |
| Ministral 3 8B Mistral AI | Q8_0 | fits | 27.3 GB | 153k256k with q8 cache | 7tok/sest. | 364 tok/s | chat |
| Granite 4.2 8B IBM | Q8_0 | fits | 30.6 GB | 128k | 6tok/sest. | 327 tok/s | chat |
| Gemma 4 E4B | Q8_0 | fits | 13.0 GB | 128k | 15tok/sest. | 995 tok/s | chat |
| LFM2.5 8B-A1B Liquid AI | Q8_0 | fits | 16.2 GB | 125k | 14tok/sest. | 1330 tok/s | chat, coding |
| Llama 3.1 8B Meta | Q6_K | fits | 24.0 GB | 128k | 8tok/sest. | 393 tok/s | chat |
| Qwen2.5 0.5B Alibaba Qwen | Q8_0 | fits | 3.8 GB | 32k | 83tok/sest. | 2989 tok/s | chat, coding, long documents |
| Qwen3.5 0.8B Alibaba Qwen | Q8_0 | fits | 4.1 GB | 256k | 73tok/sest. | 3985 tok/s | chat, coding, agents, long documents |
| Qwen3.5 2B Alibaba Qwen | Q5_K_M | fits | 4.7 GB | 256k | 58tok/sest. | 2549 tok/s | chat, coding, agents, long documents |
| DeepSeek R1 Distill Qwen 1.5B DeepSeek | Q8_0 | fits | 7.1 GB | 128k | 32tok/sest. | 1335 tok/s | chat, coding, long documents |
| Mellum2.1 12B A2.5B Thinking JetBrains | Q8_0 | fits | 15.7 GB | 128k | 23tok/sest. | 1539 tok/s | chat, coding, agents, long documents |
| MiniCPM5 2B openbmb | Q8_0 | fits | 9.6 GB | 128k | 22tok/sest. | 718 tok/s | chat, coding, agents, long documents |
| LFM2.5 2.6B Liquid AI | Q8_0 | fits | 12.0 GB | 128k | 16tok/sest. | 921 tok/s | chat, coding, agents, long documents |
| LFM2.5 VL 3B Liquid AI | Q8_0 | fits | 12.0 GB | 32k | 16tok/sest. | 886 tok/s | chat, coding, agents, long documents |
| Kimi VL A3B Moonshot AI | mlx-8bit | fits | 22.2 GB | 128k | 15tok/sest. | 966 tok/s | chat, coding |
| granite vision 4.1 4b IBM | Q8_0 | fits | 15.2 GB | 128k | 12tok/sest. | 568 tok/s | chat, coding |
| granite 4.2 3b IBM | Q8_0 | fits | 15.5 GB | 128k | 12tok/sest. | 580 tok/s | chat, coding |
| GLM 4.6V Flash Z.ai | Q8_0 | fits | 16.2 GB | 128k | 12tok/sest. | 312 tok/s | chat |
| DeepSeek R1 Distill Qwen 7B DeepSeek | Q8_0 | fits | 16.4 GB | 128k | 11tok/sest. | 477 tok/s | chat |
| GLM 4.1V 9B Thinking Z.ai | mlx-8bit | fits | 17.8 GB | 64k | 10tok/sest. | 312 tok/s | – |
| LFM2.5 24B A2B Liquid AI | Q5_K_M | fits | 29.1 GB | 128k | 9tok/sest. | 777 tok/s | chat, coding |
| DeepSeek R1 Distill Llama 8B DeepSeek | Q8_0 | fits | 25.8 GB | 128k | 7tok/sest. | 393 tok/s | chat |
| Ministral 3 8B Reasoning 2512 Mistral AI | Q8_0 | fits | 27.3 GB | 153k256k with q8 cache | 7tok/sest. | 364 tok/s | chat |
| Qwen 3.5 122B-A10B Alibaba Qwen | UD-Q2_K_XL | offload | 44.0 GB | – | 9tok/sest. | 352 tok/s | chat |
| Qwen3 Coder Next Alibaba Qwen | Q4_K_M | offload | 50.1 GB | – | 10tok/sest. | 729 tok/s | chat |
| Nemotron 3 Super 120B-A12B NVIDIA | UD-Q2_K_XL | offload | 53.9 GB | – | 8tok/sest. | 446 tok/s | – |
| Mistral Small 4 119B Mistral AI | UD-Q2_K_XL | offload | 42.1 GB | – | 12tok/sest. | 312 tok/s | chat, coding |
| Qwen3 Next 80B-A3B Alibaba Qwen | Q4_K_M | offload | 50.1 GB | – | 11tok/sest. | 797 tok/s | chat, coding |
| Devstral Small 2 24B Mistral AI | Q4_K_M | offload | 35.2 GB | 100k183k with q8 cache | 4tok/sest. | 200 tok/s | – |
| Nemotron 3.5 Lightning 30B-A3B NVIDIA | UD-Q6_K_XL | offload | 35.2 GB | – | 20tok/sest. | 1464 tok/s | chat, coding, long documents |
| Kimi Linear 48B-A3B Moonshot AI | Q4_K_M | offload | 30.9 GB | 122k167k with q8 cache | 36tok/sest. | 886 tok/s | chat, coding, long documents |
| Qwen3 Coder 30B-A3B Alibaba Qwen | Q4_K_M | offload | 31.1 GB | 124k223k with q8 cache | 7tok/sest. | 342 tok/s | chat, coding |
| Qwen3 32B Alibaba Qwen | Q4_K_M | offload | 52.3 GB | 40k | 2tok/sest. | 73 tok/s | – |
| Mistral Small 3.2 24B Mistral AI | Q4_K_M | offload | 35.2 GB | 100k128k with q8 cache | 4tok/sest. | 200 tok/s | – |
| DeepSeek R1 Distill Qwen 32B DeepSeek | Q4_K_M | offload | 52.3 GB | 43k80k with q8 cache | 2tok/sest. | 97 tok/s | – |
| Phi-4 14B Microsoft | Q4_K_M | offload | 35.1 GB | 16k | 4tok/sest. | 220 tok/s | chat |
| DeepSeek R1 Distill Qwen 14B DeepSeek | Q4_K_M | offload | 34.2 GB | 110k128k with q8 cache | 4tok/sest. | 197 tok/s | chat |
| Devstral Small 2507 Mistral AI | Q4_K_M | offload | 35.2 GB | 100k128k with q8 cache | 4tok/sest. | 202 tok/s | – |
| Magistral Small 2506 Mistral AI | Q4_K_M | offload | 35.2 GB | 40k | 4tok/sest. | 202 tok/s | – |
| Magistral Small 2507 Mistral AI | Q4_K_M | offload | 35.2 GB | 40k | 4tok/sest. | 202 tok/s | – |
| Magistral Small 2509 Mistral AI | Q4_K_M | offload | 35.2 GB | 100k128k with q8 cache | 4tok/sest. | 200 tok/s | – |
| granite 4.2 30b IBM | Q4_K_M | offload | 50.6 GB | 50k93k with q8 cache | 2tok/sest. | 118 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 | – | – | – | – |
| gpt-oss 120B OpenAI | Q4_K_M | won't run | 64.8 GB | – | – | – | – |
| GLM 4.5 Air Z.ai | Q2_K | won't run | 67.1 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 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 V3.2 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 | Q8_0 | won't run | 169.6 GB | – | – | – | – |
| GLM 4.5V Z.ai | Q4_K_M | won't run | 84.1 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 5.1 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 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 | UD-Q4_K_M | won't run | 263.6 GB | – | – | – | – |
| Mistral Medium 3.5 128B Mistral AI | Q4_K_M | won't run | 115.6 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?
- Qwen 3.8 27B (UD-Q6_K) fits fully in its memory at 128k context and generates at about 6 tok/s, an estimate from its memory bandwidth. 39 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 103 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.