Best local AI models for NVIDIA GT 740M

The largest models that fit fully

The 30 largest of the 56 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 740M is an entry level mobile graphics card equipped with 2 GB of DDR3 memory. This dedicated video memory is the main limiting factor for running local artificial intelligence models. To run a model entirely on this hardware, the model files and active memory must fit within this 2 GB limit. If a model exceeds this capacity, your system must use alternative execution methods.

The quantization column indicates the compression level used to shrink these models. Quantization reduces the precision of model weights to save space. For example, the Allegro 2.8B model fits in 2 GB of memory when compressed to the Q4_K_M quantization level. Smaller models like the SmolLM2 1.7B can run at a higher quality Q6_K quantization while using 1.7 GB of memory. TinyLlama 1.1B can run at the high quality Q8_0 quantization using only 1.4 GB of memory.

When a model is too large for the 2 GB video memory, you can use CPU offloading. This method splits the workload between your graphics card and your system RAM. We assume your system has 32 GB of system RAM for these calculations. Offloading allows you to run larger models like the SmolLM3 3B or Replit Code v1.5 3B. These models need 2.2 GB of video memory at Q4_K_M quantization and require an additional 4.2 GB of system RAM.

CPU offloading comes with a performance cost. Moving data between your system RAM and the GT 740M graphics card over the system bus is much slower than keeping data inside the video memory. While offloading lets you run larger tools like Stable Diffusion XL or SDXL Turbo, the generation speeds will be significantly slower than running smaller models that fit entirely within the 2 GB video memory limit.

You must also consider the context window size when running local models. The memory numbers listed here assume a standard 4k context window. If you increase the context window to process longer documents or larger conversations, the memory requirements will rise. This extra memory usage might force a model that normally fits within your 2 GB video memory to spill over into your system RAM.