NVIDIA GeForce RTX 4060 Ti 16GB
16 GB · 288 GB/s · 165 W
Prices
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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 @32k | Prefill | Good for |
|---|---|---|---|---|---|---|---|
| Qwen 3.8 27B Alibaba Qwen | Q8_0 | fits | 30.5 GB | 36k64k with q8 cache | 6tok/sest. | 319 tok/s | – |
| Qwen 3.6 27B Alibaba Qwen | Q8_0 | fits | 30.1 GB | 42k74k with q8 cache | 6tok/sest. | 319 tok/s | – |
| Gemma 4 31B | Q6_K | fits | 28.0 GB | 64k122k with q8 cache | 6tok/sest. | 285 tok/s | – |
| Qwen 3.6 35B-A3B Alibaba Qwen | UD-Q6_K | fits | 29.3 GB | 92k144k with q8 cache | 34tok/sest. | 2280 tok/s | chat, coding, agents, long documents |
| Qwen 3.5 27B Alibaba Qwen | Q8_0 | fits | 30.1 GB | 42k74k with q8 cache | 6tok/sest. | 319 tok/s | – |
| Gemma 4 26B-A4B | Q8_0 | fits | 27.1 GB | 176k256k with q8 cache | 21tok/sest. | 2028 tok/s | chat, coding, long documents |
| Qwen 3.5 35B-A3B Alibaba Qwen | Q6_K | fits | 28.8 GB | 109k170k with q8 cache | 34tok/sest. | 2280 tok/s | chat, coding, agents, long documents |
| GLM 4.7 Flash Z.ai | Q6_K | fits | 25.9 GB | 116k198k with q8 cache | 18tok/sest. | 1190 tok/s | chat |
| Devstral Small 2 24B Mistral AI | Q8_0 | fits | 29.6 GB | 39k71k with q8 cache | 6tok/sest. | 337 tok/s | – |
| Kimi Linear 48B-A3B Moonshot AI | Q4_K_M | fits | 29.6 GB | 122k167k with q8 cache | 50tok/sest. | 1965 tok/s | chat, coding, long documents |
| gpt-oss 20B OpenAI | MXFP4 | fits | 13.3 GB | 128k | 33tok/sest. | 1702 tok/s | chat, coding, agents, long documents |
| Qwen3 Coder 30B-A3B Alibaba Qwen | Q6_K | fits | 27.7 GB | 63k113k with q8 cache | 19tok/sest. | 1049 tok/s | chat |
| Qwen3 32B Alibaba Qwen | Q4_K_M | fits | 27.7 GB | 40k | 6tok/sest. | 198 tok/s | – |
| Gemma 4 12B | Q8_0 | fits | 13.9 GB | 256k | 13tok/sest. | 741 tok/s | – |
| Qwen 3.5 9B Alibaba Qwen | Q8_0 | fits | 11.2 GB | 256k | 17tok/sest. | 923 tok/s | chat |
| Gemma 3 27B | Q6_K | fits | 24.8 GB | 102k128k with q8 cache | 7tok/sest. | 339 tok/s | – |
| DeepSeek R1 Distill Qwen 32B DeepSeek | Q4_K_M | fits | 27.8 GB | 43k80k with q8 cache | 6tok/sest. | 228 tok/s | – |
| Mistral Small 3.2 24B Mistral AI | Q8_0 | fits | 29.6 GB | 39k71k with q8 cache | 6tok/sest. | 337 tok/s | – |
| Ministral 3 14B Mistral AI | Q8_0 | fits | 19.7 GB | 100k183k with q8 cache | 9tok/sest. | 513 tok/s | – |
| Qwen3 14B Alibaba Qwen | Q8_0 | fits | 20.9 GB | 40k | 9tok/sest. | 461 tok/s | – |
| DeepSeek R1 0528 Qwen3 8B DeepSeek | Q8_0 | fits | 13.9 GB | 128k | 13tok/sest. | 760 tok/s | chat |
| Qwen3 8B Alibaba Qwen | Q8_0 | fits | 13.9 GB | 40k | 13tok/sest. | 760 tok/s | chat |
| Phi-4 14B Microsoft | Q8_0 | fits | 22.0 GB | 16k | 8tok/sest. | 463 tok/s | – |
| Qwen 3.5 4B Alibaba Qwen | Q8_0 | fits | 6.5 GB | 256k | 32tok/sest. | 1727 tok/s | chat, coding, long documents |
| Ministral 3 8B Mistral AI | Q8_0 | fits | 13.9 GB | 153k256k with q8 cache | 13tok/sest. | 734 tok/s | chat |
| Granite 4.2 8B IBM | Q8_0 | fits | 15.0 GB | 128k | 12tok/sest. | 699 tok/s | chat |
| Gemma 4 E4B | Q8_0 | fits | 9.8 GB | 128k | 20tok/sest. | 1159 tok/s | chat |
| LFM2.5 8B-A1B Liquid AI | Q8_0 | fits | 11.2 GB | 125k | 40tok/sest. | 3792 tok/s | chat, coding |
| Llama 3.1 8B Meta | Q6_K | fits | 11.4 GB | 128k | 16tok/sest. | 803 tok/s | chat |
| Qwen 3.5 122B-A10B Alibaba Qwen | UD-Q2_K_XL | offload | 41.1 GB | – | 16tok/sest. | 632 tok/s | chat |
| Qwen3 Coder Next Alibaba Qwen | Q4_K_M | offload | 47.3 GB | – | 18tok/sest. | 1416 tok/s | chat |
| Nemotron 3 Super 120B-A12B NVIDIA | UD-Q2_K_XL | offload | 52.6 GB | – | 9tok/sest. | 564 tok/s | – |
| Mistral Small 4 119B Mistral AI | UD-Q2_K_XL | offload | 39.4 GB | – | 22tok/sest. | 719 tok/s | chat, coding |
| GLM 4.5 Air Z.ai | Q2_K | offload | 49.2 GB | – | 4tok/sest. | 214 tok/s | – |
| Qwen3 Next 80B-A3B Alibaba Qwen | Q4_K_M | offload | 47.3 GB | – | 22tok/sest. | 1686 tok/s | chat, coding |
| Nemotron 3.5 Lightning 30B-A3B NVIDIA | UD-Q6_K_XL | offload | 34.1 GB | – | 24tok/sest. | 2444 tok/s | chat, coding, long documents |
| Llama 3.3 70B Meta | Q4_K_M | offload | 50.9 GB | – | 2tok/sest. | 81 tok/s | – |
| DeepSeek V4 Flash DeepSeek | UD-Q2_K_XL | won't run | 94.2 GB | – | – | – | – |
| GLM 5.3 Flash Z.ai | UD-Q2_K_XL | won't run | 103.0 GB | – | – | – | – |
| Qwen 3.8 Flash Next Alibaba Qwen | UD-Q2_K_XL | won't run | 75.6 GB | – | – | – | – |
| MiniMax M2.7 MiniMax | UD-Q2_K_XL | won't run | 79.2 GB | – | – | – | – |
| gpt-oss 120B OpenAI | MXFP4 | won't run | 61.5 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