Best local AI models for Intel Arc A310

The largest models that fit fully

The 30 largest of the 81 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 A310 graphics card features 4 GB of GDDR6 video memory. This hardware memory size determines which artificial intelligence models can run directly on your GPU. When a model fits entirely within this 4 GB limit, it executes quickly because the graphics processor has direct high speed access to all parameters.

The quantization column shows the best compression format for each model. Quantization reduces the size of model weights to save memory. For example, Lumina-Next and CogVideoX 2B / 5B fit at the Q4_K_M quantization level using 3.7 GB of memory. Models like Qwen3 4B, Gemma 3 4B, and Phi-4-mini-instruct run at the Q6_K level, while smaller models like SmolLM3 3B and Kandinsky 3.1 can use the higher quality Q8_0 quantization.

If a model exceeds the 4 GB video memory limit, you must use CPU offload. This process splits the workload between your graphics card and your system memory. We assume a standard system configuration with 32 GB of system RAM for these offload scenarios. Offloading allows you to run larger models, but it costs performance because transferring data between system RAM and the GPU is much slower than using dedicated video memory.

Several popular models require CPU offload on this hardware. Mistral 7B needs 5.7 GB of memory at Q4_K_M quantization and uses 7.7 GB of system RAM. Qwen2.5 7B, OLMo 2 7B, Falcon 3 7B, Command R7B, and OpenHermes 2.5 each need 5.1 GB at Q4_K_M and use 7.1 GB of system RAM. Stable Diffusion XL needs 4.1 GB at FP8 or optimized settings and uses 6.1 GB of system RAM.

Memory usage calculations for text models are based on a standard 4k context window. Generating longer text or processing larger inputs increases memory consumption. If you exceed the 4k context limit, the model may run out of video memory and fail to respond. You must monitor your context length closely on 4 GB hardware.