Best local AI models for AMD FirePro W5130M

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 W5130M is a mobile workstation graphics card equipped with 2 GB of GDDR5 memory. This dedicated video memory determines the maximum size of the artificial intelligence models you can run entirely on the hardware. To run a model locally without system slowdowns, the model files must fit within this 2 GB limit.

Quantization is a compression method that reduces the memory footprint of neural networks. In the model listings, the best quant column shows the optimal balance of size and quality for this GPU. For example, a Q4_K_M quantization level allows larger models like Allegro 2.8B or Open-Sora Plan 2.7B to fit into 2 GB of memory. Higher quantizations like Q8_0 provide better precision but are restricted to smaller models like TinyLlama 1.1B or Whisper Large v3.

When a model exceeds the 2 GB physical limit of your graphics card, you must use CPU offloading. This process splits the workload between your GPU memory and your system RAM. Running a 3B model like SmolLM3 or Kandinsky 3.1 requires 2.2 GB of video memory at Q4_K_M quantization and an additional 4.2 GB of system RAM. Larger models like Stable Diffusion XL require 4.1 GB of video memory and 6.1 GB of system RAM.

CPU offloading allows you to run more capable models, but it comes with a performance cost. Transferring data between the AMD FirePro W5130M GDDR5 memory and the system RAM is much slower than keeping the model entirely on the GPU. This transfer bottleneck will significantly reduce your generation speeds.

You must also consider the memory cost of context windows. Running a model with a standard 4k context window requires extra memory space for processing user inputs and history. If you load a model that uses exactly 2 GB of memory, like SmolVLM or Stable Diffusion 3 Medium, you will not have enough remaining space for context, which can cause the system to crash or force offloading.