Best local AI models for AMD PRO W6400

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 AMD Radeon PRO W6400 is an entry level professional workstation graphics card equipped with 4 GB of GDDR6 dedicated video 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 4 GB frame buffer, it executes with the fastest possible processing speeds because the graphics processor has direct high speed access to all the parameters.

To fit larger models into this hardware limit, you must use quantized versions. Quantization is a compression technique that reduces the precision of the model weights. The best quantization column shows the optimal balance between model accuracy and memory footprint. For example, a 4B model like Qwen3 4B or Gemma 3 4B can run at a high quality Q6_K quantization using 3.9 GB of video memory. Larger models like Lumina-Next 5B or CogVideoX 5B require a lower Q4_K_M quantization to fit within 3.7 GB of memory.

If you want to run models that exceed the 4 GB video memory limit, you must use CPU offloading. This technique splits the model layers between your graphics card and your system memory. We assume a standard system configuration with 32 GB of system RAM for these scenarios. Offloading allows you to run larger architectures, but it introduces a speed penalty because data must travel across the slower system bus.

With CPU offloading, you can run popular 7B parameter architectures. Models such as Mistral 7B require 5.7 GB of memory at Q4_K_M quantization and need 7.7 GB of system RAM. Similarly, Qwen2.5 7B, OLMo 2 7B, Falcon 3 7B, Command R7B, and OpenHermes 2.5 require 5.1 GB of memory at Q4_K_M quantization and need 7.1 GB of system RAM to operate. Even specialized tools like Magicoder-S-DS 6.7B can run by using 4.9 GB at Q4_K_M quantization with 6.9 GB of system RAM.

You must also consider the memory cost of the context window. The memory figures listed for these models represent the base requirements for the model weights. Running a standard 4k context window or generating longer sequences requires additional memory for the activation data. When running close to the 4 GB physical limit of the AMD PRO W6400, this extra context data can exceed your remaining capacity and trigger system slowdowns.