Best local AI models for AMD RX 6650 XT

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 6650 XT graphics card features 8 GB of GDDR6 video memory. This onboard memory determines the size of the artificial intelligence models you can run directly on your hardware. For local execution, the entire model weights and the active context data must fit within this physical limit to maintain fast processing speeds.

To fit larger models into the 8 GB limit, developers use quantization. The quant column indicates the compression level applied to the model weights. For example, the Mochi 1 10B model fits within 7.3 GB of video memory when using the Q4_K_M quantization level. Other models like Gemma 2 9B can run at the Q4_K_M level using exactly 8 GB of video memory.

Many popular models can run at higher precision levels on this hardware. The Llama 3.1 8B model fits within 7.4 GB of video memory at the Q5_K_M quantization level. You can also run Granite 3.3 8B, Ministral 8B, and InternLM 3 8B at the Q6_K quantization level, which uses 7.9 GB of video memory. Standard 7B models like Mistral 7B use 7.4 GB at Q6_K, while Qwen2.5 7B uses 6.9 GB at the same Q6_K level.

When a model exceeds the 8 GB video memory limit, you must offload parts of the workload to your system RAM. This process requires a system with sufficient memory, such as 32 GB of system RAM. Offloading allows you to run larger models like Gemma 3 12B or Mistral NeMo 12B, which require 8.8 GB of memory at Q4_K_M and 10.8 GB of system RAM. However, offloading costs processing speed because system RAM is much slower than GDDR6 video memory.

You must also consider the memory cost of your context window. The memory usage figures listed for these models assume a standard 4k context window. If you increase the context length to process longer documents, the memory requirements will rise. This extra demand can push a model past the 8 GB limit and force slow system RAM offloading.