Best local AI models for NVIDIA RTX 5060 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 5060 Ti features 16 GB of GDDR7 memory. This dedicated video memory determines which local AI models you can run entirely on your graphics hardware. When a model fits completely within this 16 GB limit, it runs at maximum speed because the GPU can access the parameters directly. If a model exceeds this limit, you must use CPU offloading to system memory, which reduces performance.

To fit larger models into the available memory, you must use quantized versions. The quantization column shows the optimal format for each model. For example, the 21B models gpt-oss-20b and Reka Flash 3 fit in 15.4 GB of memory using the Q4_K_M quantization. Models like DeepSeek-Coder-V2 16B / 236B and Kimi-VL A3B use the Q6_K quantization, which requires 15.7 GB of memory. Smaller models like Gemma 3 12B and Mistral NeMo 12B can run at the higher Q8_0 quantization, using 15.3 GB of memory.

The memory figures listed represent the space required to load the model weights. They do not include the extra memory needed for the context window during active use. Running a model with a large context window like 4k tokens requires additional memory. If you use long context lengths, you may need to choose a smaller model size or a lower quantization level to prevent out of memory errors.

If you want to run models that exceed 16 GB, you can offload some layers to your system RAM. This approach assumes you have at least 32 GB of system RAM. For example, Solar Pro and Codestral 22B require 16.1 GB 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 at Q4_K_M, which needs 19.6 GB of system RAM. This offloading process allows you to run these larger models, but the processing speed will be slower.

Even larger models can be run using this offloading method. Aria requires 18.3 GB at Q4_K_M and needs 20.3 GB of system RAM. Gemma 4 26B-A4B and Gemma 4 (all sizes) require 19 GB at Q4_K_M, which needs 21 GB of system RAM. The largest options like Gemma 3 27B and Gemma 3 4B/12B/27B (vision) require 19.8 GB at Q4_K_M and need 21.8 GB of system RAM. Your system RAM will handle the overflow while your GPU processes the rest.