Best local AI models for NVIDIA GT 710

The largest models that fit fully

The 28 largest of the 28 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 GT 710 is an entry level graphics card equipped with 1 GB of DDR3 video memory. This limited memory size defines the maximum scale of any artificial intelligence model you can run entirely on the hardware. To fit within this 1 GB boundary, models must be small or highly compressed. Running out of video memory will cause the execution to fail or slow down significantly.

The quantization column shows the specific compression level required to fit each model into your video memory. Quantization reduces the precision of model weights to save space. For example, Tango 2 at 1.4B parameters fits using a Q4_K_M quantization which uses exactly 1 GB of video memory. Smaller models like Qwen3 0.6B can use a higher quality Q8_0 quantization while consuming only 0.8 GB of video memory. The smallest models like StyleTTS 2 at 0.15B can run at full FP16 precision using 0.4 GB of video memory.

You can run slightly larger models by offloading parts of the workload to your system RAM. This approach assumes you have 32 GB of system RAM available. For example, Stable Diffusion 1.5 has a size of 1.07B and needs 1.3 GB at FP8 or optimized configuration, which requires 3.3 GB of system RAM to function. Similarly, StableLM 2 1.6B needs 1.2 GB at Q4_K_M quantization and requires 3.2 GB of system RAM. Offloading allows these models to run but it comes with a performance cost because system RAM is much slower than video memory.

When running text models, you must also consider the context window size. Running a model like Llama 3.2 1B at Q6_K quantization uses 1 GB of video memory. This leaves no extra room for processing long conversations. If you increase the context window to 4k tokens, the system will require additional memory for the active chat history. This extra memory demand will exceed the 1 GB limit of the card and force the system to slow down.

For audio and speech tasks, several specialized models fit within the hardware limits. You can run XTTS v2 at 0.5B parameters using Q8_0 quantization with 0.6 GB of video memory. F5-TTS at 0.3B parameters runs at FP16 precision using 0.7 GB of video memory. These compact models allow you to perform text to speech and voice generation locally without upgrading your graphics hardware.