Best local AI models for AMD R7 M465X

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 R7 M465X is an entry level graphics card equipped with 2 GB of GDDR5 memory. This dedicated video memory determines the maximum size of the artificial intelligence models you can run entirely on the hardware. When a model fits completely inside this 2 GB limit, it runs at the maximum speed possible on this hardware. If a model exceeds this limit, you must use system memory to run it.

To fit models into the limited memory space, developers use quantization. The quant column shows the specific compression level used for each model. For example, a Q4_K_M quant uses four bit quantization to compress the weights. This allows larger models like Allegro 2.8B or Open-Sora Plan 2.7B to fit within 2 GB of video memory. Higher quants like Q8_0 offer better accuracy but require more memory space.

Models like LFM2 2.6B and Playground v2.5 use 1.9 GB of video memory at Q4_K_M. You can also run Stable Diffusion 3.5 Medium or Canary 2.5B at Q4_K_M using 1.8 GB of memory. For models with higher quantization levels, you can run Parler-TTS 2.2B at Q5_K_M using 1.9 GB of memory. You can also run SmolVLM 2B or Stable Diffusion 3 Medium at Q6_K using the full 2 GB of video memory.

When a model is too large for the 2 GB video memory, you must offload parts of it to your system RAM. This offload process allows you to run larger models but reduces the processing speed. For example, SmolLM3 3B or Kandinsky 3.1 require 2.2 GB of memory at Q4_K_M. To run these models, you need 4.2 GB of system RAM alongside your graphics card. Running Stable Diffusion XL requires 4.1 GB at FP8 and needs 6.1 GB of system RAM.

You must also consider the context window size when running these models. The memory figures listed are calculated for a standard four kilobyte context window. If you increase the context window to process longer text, the memory usage will rise. This extra memory requirement can push a model past the 2 GB limit of your graphics card and force the system to use slower system RAM.