Best local AI models for Intel Arc Pro B50

The largest models that fit fully

The 30 largest of the 155 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 Pro B50 workstation graphics card features 16 GB of GDDR6 dedicated memory. This memory capacity determines the maximum size of the artificial intelligence models you can run entirely on the hardware. When a model fits completely within this local video memory, it achieves the fastest possible processing speeds.

To fit larger models into the 16 GB memory space, developers use quantization. The quantization column shows the compression level used to reduce model size. For example, gpt-oss-20b and Reka Flash 3 are 21B models that fit into 15.4 GB of memory using the Q4_K_M quantization. Other models like DeepSeek-Coder-V2 16B / 236B and Kimi-VL A3B use the Q6_K quantization to fit into 15.7 GB of memory. Models like Gemma 3 12B and Mistral NeMo 12B can run at the higher quality Q8_0 quantization using 15.3 GB of memory.

Running these models at their limit requires careful attention to context window sizes. The memory figures listed are calculated using a standard 4k context window. If you increase the context window to process longer documents or conversations, the system will require more memory. This extra memory demand can cause a model that normally fits to exceed the 16 GB limit.

When a model exceeds the 16 GB video memory of the Intel Arc Pro B50, you must use CPU offload. This process splits the model between your graphics card and your system memory. CPU offload allows you to run larger models, but it reduces processing speed because system RAM is slower than GDDR6 memory.

If your computer has 32 GB of system RAM, you can offload several larger models. Solar Pro and Codestral 22B require 16.1 GB at Q4_K_M quantization and use 18.1 GB of system RAM. Mistral Small 3.2, Magistral Small, and Devstral Small 1.1 are 24B models that require 17.6 GB at Q4_K_M quantization and use 19.6 GB of system RAM. The largest offload options include Gemma 3 27B, which requires 19.8 GB at Q4_K_M quantization and uses 21.8 GB of system RAM.