Best local AI models for NVIDIA RTX 4060 Ti 16GB

The largest models that fit fully

The 30 largest of the 155 models that fit; every smaller model in the catalog fits too. Best quant means the highest quality compression whose weights and 4k context both sit inside the memory.

Close, but only with CPU offload

These need more than the card holds at their smallest practical quant, so part of the model runs from system memory (figures assume 32 GB of it). They work, several times slower.

How to read this

The NVIDIA RTX 4060 Ti features 16 GB of GDDR6 memory. This specific memory capacity determines which local AI models you can run entirely on your graphics hardware. When a model fits completely within this 16 GB limit, your system processes tokens at maximum speed. If a model exceeds this limit, you must offload parts of it to your system RAM, which reduces processing speeds.

To fit larger models into the 16 GB memory space, you must use quantized versions. Quantization reduces the precision of model weights to save space. The quantization column shows the optimal format for each model. For example, the 21B models gpt-oss-20b and Reka Flash 3 fit at Q4_K_M quantization using 15.4 GB of memory. Smaller models like Gemma 3 12B, Gemma 4 12B, Mistral NeMo 12B, and Pixtral 12B can run at higher precision Q8_0 quantization using 15.3 GB of memory.

Vision and image models also fit within this hardware envelope. Qwen-Image and Qwen-Image-Edit run at Q4_K_M quantization using 14.6 GB of memory. CogVLM2 fits at Q4_K_M using 13.9 GB of memory. HunyuanImage 2.1 / 3.0 fits at Q5_K_M using 14.5 GB of memory. For image generation, FLUX.1 dev runs at FP8 / optimized quantization using 14.4 GB of memory, while FLUX.1 schnell uses 15.3 GB of memory at Q8_0 quantization.

If you have 32 GB of system RAM, you can run larger models using CPU offloading. This method splits the model between your graphics card and your system memory. Solar Pro and Codestral 22B require 16.1 GB of memory at Q4_K_M quantization and need 18.1 GB of system RAM. Mistral Small 3.2, Magistral Small, and Devstral Small 1.1 require 17.6 GB of memory at Q4_K_M quantization and need 19.6 GB of system RAM. Gemma 3 27B requires 19.8 GB of memory at Q4_K_M quantization and needs 21.8 GB of system RAM.

Be aware of the memory cost of context windows. The memory usage figures listed here are calculated using a baseline 4k context window. If you increase the context window to process longer documents, the system requires more memory for the active session. This extra memory requirement can push a model that normally fits within the 16 GB limit into CPU offload territory, which slows down response times.