Best local AI models for NVIDIA RTX 5070 Ti Laptop

The largest models that fit fully

The 30 largest of the 147 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 5070 Ti Laptop GPU comes equipped with 12 GB of GDDR7 memory. This dedicated video memory determines the maximum size of the artificial intelligence models you can run entirely on your graphics hardware. Keeping a model within this limit ensures fast generation speeds because the GPU does not have to wait for slower system memory.

The quantization column shows the compression level used to fit these models into your hardware. For example DeepSeek-Coder-V2 16B and Kimi-VL A3B use the Q4_K_M quantization to fit within 11.7 GB of video memory. Models like Phi-4 and Wan 2.2 T2I use the Q5_K_M quantization which requires 11.9 GB. Smaller models like Gemma 3 12B and Mistral NeMo 12B can run at the higher quality Q6_K quantization using 11.8 GB.

When a model exceeds your 12 GB video memory you must offload parts of it to your system RAM. This process requires a system with at least 32 GB of system RAM to work smoothly. For example running FLUX.1 dev at FP8 requires 14.4 GB of memory which uses 16.4 GB of system RAM. Running Solar Pro or Codestral 22B requires 16.1 GB of memory which uses 18.1 GB of system RAM. Offloading allows you to run larger models but it reduces your generation speed.

You must also consider the memory cost of your context window. The memory figures listed for these models assume a standard base context. If you increase your context window to 4k tokens or higher the GPU must allocate more memory to store the conversation history. This extra memory usage can push a model that normally fits perfectly over your 12 GB limit.

For optimal local performance you can choose models that fit entirely within your hardware limits. Nemotron Nano 9B and GLM-4 9B run at the high quality Q8_0 quantization using 11.4 GB of video memory. These configurations maximize your generation speed while keeping your system responsive.