Best local AI models for AMD FirePro W4190M

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 AMD FirePro W4190M is an entry level mobile workstation graphics card. It features 2 GB of GDDR5 video memory. This memory size is the main factor that limits the size of the artificial intelligence models you can run locally. To run a model entirely on this hardware, the model files and the active workspace must fit completely within this 2 GB limit.

To make models fit into this small memory space, developers use quantization. The quant column shows the compression level applied to each model. For example, the Allegro 2.8B model fits into 2 GB of video memory when using the Q4_K_M quantization. Smaller models like SmolLM2 1.7B can use the higher quality Q6_K quantization while using 1.7 GB of video memory. Very small models like TinyLlama 1.1B can run at the highest quality Q8_0 quantization while using only 1.4 GB of video memory.

When a model is slightly too large for the 2 GB video memory, you can use CPU offload. This technique splits the workload between your graphics card and your system memory. We assume your computer has 32 GB of system RAM for these calculations. For example, running the SmolLM3 3B model requires 2.2 GB of video memory at Q4_K_M quantization, which spills over your hardware limit and requires 4.2 GB of system RAM to function.

Using CPU offload allows you to run larger models like MusicGen or SDXL Turbo. However, this offloading process comes with a performance cost. System RAM is much slower than the GDDR5 video memory on your graphics card. When layers of the model are processed by the CPU and system RAM, the generation speed drops significantly.

You must also consider the context window size when planning your memory usage. The memory figures listed on this page are calculated using a basic 4k context window. If you increase the context window to process longer documents or larger conversations, the memory usage will rise. This extra memory demand can push a model past the 2 GB limit and force your system to use slow CPU offloading.