Best local AI models for AMD R7 A360

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 Radeon R7 A360 is an entry level graphics card equipped with 2 GB of DDR3 dedicated video memory. This specific VRAM capacity determines which artificial intelligence models can run directly on your hardware. When running models locally, the entire active weight set must ideally reside within this 2 GB boundary to maintain acceptable processing speeds.

To fit larger models into this compact memory space, developers use quantization. The quant column indicates the compression level applied to the model weights. For example, a Q4_K_M quantization uses approximately four bits per parameter, which allows a 2.8B model like Allegro or a 2.7B model like Open-Sora Plan to fit into 2 GB of VRAM. Higher quants like Q6_K or Q8_0 offer better precision but require more memory per parameter, limiting you to smaller models like the 2B Stable Diffusion 3 Medium or the 1.1B TinyLlama.

If a model exceeds the 2 GB VRAM limit, you must use CPU offload. This technique splits the model layers between your graphics card and your system RAM. For instance, running the 3B SmolLM3 or the 3.5B SDXL Turbo requires offloading. While this allows you to run larger architectures, it introduces a performance cost. Data must travel over the system bus, which is much slower than the onboard memory of the graphics card.

System RAM requirements increase when offloading. Running a 3B model like Kandinsky 3.1 or Orpheus TTS at Q4_K_M requires 2.2 GB of VRAM and an additional 4.2 GB of system RAM. Larger models like Stable Diffusion XL require 4.1 GB of VRAM and 6.1 GB of system RAM when optimized at FP8. You must ensure your computer has enough system memory, such as a standard 32 GB system RAM configuration, to handle these split workloads.

You must also consider the 4k context caveat when running text models. The memory figures listed for models like LFM2 2.6B or SmolLM2 1.7B only cover the model weights themselves. Generating long responses or processing large prompts increases memory usage. If you use a full 4k context window, the active memory will exceed the baseline weight size, which can cause slow performance or out of memory errors on a 2 GB card.