Best local AI models for AMD R5 M230

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 Radeon R5 M230 is an entry level graphics card equipped with 2 GB of DDR3 video memory. This dedicated memory size is the main limiting factor for running local artificial intelligence models. To run a model entirely on this GPU, 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 strategies.

The quantization column indicates the compression level applied to each model. Quantization reduces the precision of model weights to save space. For example, the Allegro 2.8B model uses a Q4_K_M quantization to fit into 2 GB of video memory. Smaller models like SmolLM2 1.7B can run at a higher Q6_K quantization while using 1.7 GB of video memory. TinyLlama 1.1B can run at Q8_0 quantization using only 1.4 GB of video memory.

When a model size exceeds the 2 GB video memory limit, you can use CPU offloading. This process splits the workload between your graphics card and your system RAM. Assuming your computer has 32 GB of system RAM, you can run larger models by sharing the load. For instance, SmolLM3 3B requires 2.2 GB of video memory at Q4_K_M quantization and needs an additional 4.2 GB of system RAM to function.

Other models also utilize this offloading method to bypass the physical limits of the graphics card. MusicGen requires 2.4 GB of video memory at Q4_K_M and 4.4 GB of system RAM. Stable Diffusion XL requires 4.1 GB of video memory at FP8 or optimized settings along with 6.1 GB of system RAM. SDXL Turbo and SDXL Lightning both require 2.6 GB of video memory at Q4_K_M and 4.6 GB of system RAM.

Running models at or near the video memory limit introduces a context window caveat. The memory figures listed only cover the base model weights. Generating longer text or processing larger inputs increases memory consumption. If you use a standard 4k context window, the active memory usage will rise. This extra demand can easily push a 2 GB model over the limit and cause slow performance.