Best local AI models for AMD R7 M465

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 M465 is an entry level mobile graphics card equipped with 2 GB of GDDR5 video memory. This hardware configuration dictates the size of the artificial intelligence models you can run locally. Because the onboard memory is limited to 2 GB, selecting the correct model size and quantization level is critical to prevent system slowdowns.

Quantization is a method that compresses model weights to save space. In our lists, the best quant column shows the optimal compression level for each model. For example, the Allegro 2.8B model fits into 2 GB of video memory when compressed to the Q4_K_M quantization. Other models like SmolVLM 2B can run at a higher quality Q6_K quantization while still utilizing exactly 2 GB of video memory.

When a model exceeds the 2 GB limit, you must use CPU offloading. This process splits the workload between your graphics card and your system memory. If you have 32 GB of system RAM, you can run larger models like SmolLM3 3B or Replit Code v1.5 3B. These models require 2.2 GB of video memory at Q4_K_M quantization and an additional 4.2 GB of system RAM to function.

Image generation models also run on this hardware with careful planning. Stable Diffusion 3.5 Medium fits within 1.8 GB of video memory using the Q4_K_M quantization. If you want to run larger generation tools like Stable Diffusion XL, you will need to utilize CPU offloading. Stable Diffusion XL requires 4.1 GB of video memory at FP8 or optimized settings along with 6.1 GB of system RAM.

Audio and speech models are highly compatible with this GPU. You can run Whisper Large v3 at Q8_0 quantization using 2 GB of video memory. Parler-TTS fits well at Q5_K_M quantization using 1.9 GB of video memory. These configurations allow you to perform speech recognition and text to speech tasks locally without relying on cloud services.

Keep in mind that context window size affects memory usage. Running a model with a large 4k context window increases the memory footprint. The memory figures listed here represent the base requirements for the models. If you generate very long responses or input large documents, the system may exceed the 2 GB video memory limit and slow down.