Best local AI models for AMD R7 M265

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 M265 is an entry level laptop graphics card equipped with 2 GB of DDR3 dedicated video memory. This memory capacity dictates the size of the artificial intelligence models you can run locally. Because the hardware relies on slower DDR3 memory rather than modern GDDR6 memory, keeping the model files within the physical limits of the graphics card is critical to maintain usable processing speeds.

The model size and quantization level determine how much memory a model uses. Quantization is a compression method that reduces the precision of model weights. A Q4_K_M quant represents a four bit quantization that saves significant space while preserving most of the model accuracy. Higher quants like Q6_K and Q8_0 offer better output quality but require more memory. For example, Allegro at 2.8B fits in the 2 GB limit using a Q4_K_M quant, while the smaller SmolLM2 1.7B can run at a higher quality Q6_K quant using 1.7 GB of memory.

When a model exceeds the 2 GB video memory limit, you must use CPU offloading. This process splits the model layers between your graphics card and your system RAM. Assuming your computer has 32 GB of system RAM, you can run larger models like SmolLM3 3B or MusicGen. SmolLM3 3B requires 2.2 GB of video memory at Q4_K_M and needs an additional 4.2 GB of system RAM. Offloading allows you to run these larger models, but the transfer of data between the system RAM and the graphics card over the system bus will slow down the generation speed.

Running image generation models also requires careful memory management. Stable Diffusion 3.5 Medium at 2.5B fits in 1.8 GB of video memory using a Q4_K_M quant. Larger image models like Stable Diffusion XL at 3.417B require 4.1 GB of memory at FP8 or optimized settings, which forces 6.1 GB of data onto your system RAM. While offloading makes these advanced image generators run on your system, the generation times will be much longer than smaller native models.

You must also consider the memory cost of context length in text models. The memory figures listed for models like Qwen3 1.7B or TinyLlama 1.1B represent the base model size. As you type longer prompts and receive longer answers, the context window consumes additional video memory. Running a model close to the 2 GB limit of your AMD R7 M265 means that a long conversation history can quickly exceed your video memory and trigger slow system RAM offloading.