NVIDIA GeForce RTX 4060 Ti 16GB
16 GB · 288 GB/s · 165 W
Prices
Every retailer we track in the US, cheapest first. Buy links may earn us a commission.
| Retailer | Condition | Price | Stock | Checked | Buy |
|---|---|---|---|---|---|
| AmazonASUS ProArt GeForce RTX 4060 Ti 16GB OC Edition GDDR6 Graphics Card | New | $770 | In stock | 21 min ago | Buy |
| AmazonPNY GeForce RTX 4060 Ti 16GB XLR8 Gaming Verto OC Dual Fan Graphics Card DLSS 3 | New | $799 | In stock | 21 min ago | Buy |
| AmazonMSI GeForce RTX 4060 Ti Ventus 3X 16G OC Graphics Card -NVIDIA RTX 4060 Ti | New | $918 | In stock | 21 min ago | Buy |
| AmazonMSI GeForce RTX 4060 Ti Gaming X Slim 16G Graphics Card | New | $1,364 | In stock | 21 min ago | Buy |
| AmazonGigabyte GeForce RTX 4060 Ti AERO OC 16G Graphics Card | New | $1,439 | In stock | 21 min ago | Buy |
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 |
| 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 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 | MXFP4 | won't run | 65.4 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 | – | – | – | – |
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