Best local AI models for Intel Arc A530M

The largest models that fit fully

The 30 largest of the 123 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 Intel Arc A530M is a mobile graphics processing unit equipped with 8 GB of GDDR6 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 smoothly, the model files and the active working memory must fit within this 8 GB limit. If a model exceeds this capacity, the system must use alternative memory management strategies.

Quantization is a method used to compress artificial intelligence models so they use less memory. The quant column indicates the highest quality compression level that safely fits within the hardware limits. For example, Gemma 2 9B fits at the Q4_K_M quantization level using 8 GB of memory. Models like Llama 3.1 8B can run at the higher quality Q5_K_M quantization level using 7.4 GB of memory. Smaller models like Mistral 7B can run at the Q6_K quantization level using 7.4 GB of memory.

When a model is too large for the 8 GB video memory, you can use CPU offload. This technique splits the model between the graphics card and your system RAM. We assume a standard system configuration with 32 GB of system RAM for these scenarios. Offloading allows you to run larger models like FLUX.1 dev which requires 14.4 GB of memory at FP8 or optimized settings and 16.4 GB of system RAM. You can also run Gemma 3 12B which needs 8.8 GB of memory at Q4_K_M and 10.8 GB of system RAM.

CPU offload comes with a performance cost. Sharing data between the system RAM and the graphics memory is much slower than keeping everything on the graphics card. Models like Vicuna 13B require 9.5 GB of memory at Q4_K_M and 11.5 GB of system RAM. While offloading makes running these larger models possible, the generation speed will be noticeably slower than running a fully contained model.

Memory calculations for these models are based on a standard 4k context window. The context window is the amount of text the model can remember during a conversation. If you increase the context window beyond 4k, the model will require more memory. This extra memory usage might force you to use a lower quantization level or rely on CPU offload to prevent running out of video memory.