Best local AI models for AMD R7 260

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 260 graphics card features 2 GB of GDDR5 memory. This physical memory size is the strict limit for running local AI models directly on the hardware. When a model fits entirely within this 2 GB boundary, the system achieves the fastest processing speeds. If a model exceeds this limit, it cannot run on the graphics card alone. You must understand how model sizes and quantization levels interact to make the best use of this hardware.

Quantization is a method that compresses the weights of an artificial intelligence model. The quant column shows the specific compression level needed to fit each model into the available memory. For example, the Allegro 2.8B model requires a Q4_K_M quantization to fit within 2 GB of used memory. Smaller models like Moondream 2 can run at a higher Q6_K quantization while using 1.9 GB of memory. Higher quantization levels like Q8_0 preserve more original model quality but require more memory space.

When a model is too large for the 2 GB physical memory, you must use CPU offload. This technique splits the workload between your graphics card and your system RAM. We assume your computer has 32 GB of system RAM for these scenarios. For instance, running the SmolLM3 3B model requires 2.2 GB of video memory at Q4_K_M quantization and also needs 4.2 GB of system RAM. Using CPU offload allows you to run larger architectures, but it significantly reduces processing speed.

Many different model types can run on this hardware configuration. You can run image generation models like Stable Diffusion 3 Medium which uses 2 GB of memory at Q6_K quantization. Audio models like Parler-TTS fit within 1.9 GB of memory using the Q5_K_M quantization. Even text models like TinyLlama 1.1B run comfortably using 1.4 GB of memory at Q8_0 quantization. Each model type has different memory demands based on its parameter count and quantization level.

There is an important caveat regarding the 4k context window limit. Running text models with a context window of 4000 tokens requires extra memory for the active session history. This active data is stored in the key value cache during generation. If you run a model close to the 2 GB limit, like the Canary 1B / Qwen-2.5B at 1.8 GB, a long context window can easily exceed your remaining memory. You must monitor your context length to prevent out of memory errors.