Best local AI models for AMD R7 A265

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 A265 is an entry level graphics card equipped with 2 GB of DDR3 video memory. This onboard memory pool is the primary factor that determines which artificial intelligence models you can run locally. Because the hardware has 2 GB of physical VRAM, any model you load entirely onto the graphics card must fit within this strict limit to avoid severe performance slowdowns.

The quantization column indicates the compression level applied to each model. Quantization reduces the precision of model weights to save space. For example, the Allegro 2.8B model fits in 2 GB of VRAM using the Q4_K_M quantization. Smaller models like SmolLM2 1.7B can run at a higher quality Q6_K quantization while using 1.7 GB of VRAM. TinyLlama 1.1B can run at the high quality Q8_0 quantization level while using only 1.4 GB of VRAM.

When a model exceeds the 2 GB VRAM limit, you must use CPU offloading. This technique splits the model weights between your graphics card and your system RAM. Assuming your computer has 32 GB of system RAM, you can run larger models by offloading the extra layers. For example, the SmolLM3 3B model needs 2.2 GB of memory at Q4_K_M quantization, which requires utilizing 4.2 GB of system RAM alongside your VRAM.

Other models also rely on CPU offloading to function on this hardware. MusicGen requires 2.4 GB of memory at Q4_K_M quantization and uses 4.4 GB of system RAM. Stable Diffusion XL requires 4.1 GB of memory at FP8 or optimized settings, which utilizes 6.1 GB of system RAM. This offloading process allows you to run larger architectures, but it introduces a speed penalty because system RAM is much slower than dedicated video memory.

You must also consider the context window when running local text models. The memory numbers listed here represent the model weights alone. Running a model with a standard 4k context window requires additional memory to store the active conversation history. If you fill the context window, the memory usage will rise, which may push a model that fits perfectly at startup over the 2 GB VRAM limit.