Best local AI models for AMD RX 6400

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 RX 6400 graphics card features 4 GB of GDDR6 memory. This memory size determines which local AI models can run entirely on your hardware. When a model fits inside this 4 GB limit, it runs at maximum speed because the graphics processor can access all parameters instantly. If a model exceeds this limit, you must use system RAM to assist the graphics card.

The quantization column shows the compression level used to fit these models into memory. Quantization reduces the precision of model weights to save space. For example, a Q4_K_M quant uses four bit quantization to compress models like Lumina-Next or CogVideoX 2B / 5B down to 3.7 GB. A Q6_K quant offers higher precision for models like Qwen3 4B and Phi-4-mini-instruct, which use 3.9 GB and 3.7 GB of memory. The highest precision listed is Q8_0, which allows models like SmolLM3 3B or Stable Diffusion 3.5 Medium to run with minimal quality loss.

For larger models, CPU offloading is necessary. This process splits the workload between your graphics card and your system RAM. If you have 32 GB of system RAM, you can run models that exceed 4 GB. For example, Mistral 7B requires 5.7 GB of memory at Q4_K_M, which uses 7.7 GB of system RAM during offload. Similarly, Qwen2.5 7B and Falcon 3 7B require 5.1 GB of memory at Q4_K_M, which uses 7.1 GB of system RAM.

CPU offloading comes with a performance cost. Moving data between your graphics card and system RAM is much slower than keeping everything in GDDR6 memory. While offloading allows you to run larger models like Command R7B or OpenHermes 2.5, your generation speed will drop significantly compared to running smaller models entirely on the graphics card.

You must also consider the memory used by the context window. The memory figures listed are calculated with a standard 4k context window. If you increase the context length to process longer documents, the model will require more memory. This extra memory usage can push a model over the 4 GB limit and trigger slow CPU offloading.