Best local AI models for AMD RX 7400

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 AMD Radeon RX 7400 graphics card features 8 GB of GDDR6 dedicated video memory. This memory pool determines which artificial intelligence models can run entirely on your graphics hardware. When a model fits completely within this VRAM, you get the fastest generation speeds. If a model exceeds this limit, you must offload parts of it to your system RAM.

To fit larger models into the 8 GB limit, we use quantized versions. Quantization reduces the precision of model weights to save space. The quant column shows the best balance of size and quality for each model. For example, Mochi 1 is a 10B model that fits into 7.3 GB of VRAM using the Q4_K_M quantization. Gemma 2 9B utilizes the exact 8 GB limit at the Q4_K_M quantization level.

Many popular models fit comfortably within the 8 GB boundary. You can run Llama 3.1 8B at Q5_K_M quantization using 7.4 GB of VRAM. Other models like Granite 3.3 8B, Ministral 8B, and InternLM 3 8B run at Q6_K quantization using 7.9 GB of VRAM. Standard 7B models like Mistral 7B use 7.4 GB at Q6_K, while Qwen2.5 7B and Falcon 3 7B use 6.9 GB at Q6_K.

When you run models close to the 8 GB limit, you must consider the context window. These memory figures are calculated with a standard 4k context window. If you input very long prompts or generate long responses, the memory usage will increase. This extra data can push the model over your VRAM limit and slow down performance.

If you want to run larger models, you can offload layers to your system RAM. This process requires a system with at least 32 GB of system RAM. Offloading allows you to run Gemma 3 12B or Mistral NeMo 12B, which require 8.8 GB at Q4_K_M and 10.8 GB of system RAM. You can also run Vicuna 13B, which needs 9.5 GB at Q4_K_M and 11.5 GB of system RAM.

Offloading comes with a performance cost. System RAM is much slower than the GDDR6 memory on your graphics card. While offloading lets you run larger models like FLUX.1 dev at FP8 which needs 14.4 GB of VRAM and 16.4 GB of system RAM, your generation speeds will be significantly lower than running fully in VRAM.