Best local AI models for AMD RX 460

The largest models that fit fully

The 30 largest of the 56 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 460 graphics card features 2 GB of GDDR5 memory. This onboard memory capacity determines which artificial intelligence models can run directly on your hardware. To run a model entirely on the graphics card, the model size must fit within this 2 GB limit. If a model exceeds this limit, it will require system memory offloading to function.

The quantization column indicates the compression level applied to each model. Quantization reduces the size of a model so it uses less memory. For example, the Allegro 2.8B model fits in 2 GB of memory when using the Q4_K_M quantization. Similarly, the SmolVLM 2B and Stable Diffusion 3 Medium models can run on this card using the Q6_K quantization. Smaller models like the TinyLlama 1.1B can use the higher quality Q8_0 quantization while consuming only 1.4 GB of memory.

Running models with larger context windows like a 4k context requires extra memory. The memory figures listed here represent the base model size. When you generate longer text or process larger inputs, the memory usage will increase. You must keep this caveat in mind because a model that fits at startup might run out of memory during long conversations.

You can run larger models by offloading parts of them to your system RAM. This page assumes you have 32 GB of system RAM installed. Offloading allows you to run models like the SmolLM3 3B or the Replit Code v1.5 3B. These 3B models need 2.2 GB of graphics memory at Q4_K_M quantization and 4.2 GB of system RAM. Offloading makes larger models accessible but it reduces processing speed because system RAM is slower than GDDR5 graphics memory.

Image generation models also support offloading. The Stable Diffusion XL model has 3.417B parameters and needs 4.1 GB of memory at FP8 or optimized settings, which requires 6.1 GB of system RAM. The SDXL Turbo and SDXL Lightning models both have 3.5B parameters. These models need 2.6 GB of graphics memory at Q4_K_M quantization along with 4.6 GB of system RAM to run on your system.