Best local AI models for AMD RX 6500M

The largest models that fit fully

The 30 largest of the 81 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 RX 6500M is a mobile graphics processor equipped with 4 GB of GDDR6 dedicated video memory. This memory capacity determines the size of the artificial intelligence models you can run locally. To load and execute a model entirely on the graphics hardware, the model files and active memory must fit within this 4 GB boundary.

The quantization column indicates the compression level applied to each model. Quantization reduces the precision of model weights to save space. For this hardware, models up to 5B parameters like Lumina-Next or CogVideoX 2B / 5B can run entirely in video memory at a Q4_K_M quantization, consuming 3.7 GB of space. Models with 4B parameters such as Qwen3 4B, Gemma 3 4B, Gemma 4 E4B, MiniCPM 3 4B, Danube 3 4B, and Fish Speech 1.5 / OpenAudio S1 can run at a higher Q6_K quantization, using 3.9 GB.

Smaller models allow even higher precision levels. Models in the 3B range, including SmolLM3 3B, Replit Code v1.5 3B, Kandinsky 3.1, Voxtral Mini / Small, Orpheus TTS, and Higgs Audio v2, can run at Q8_0 quantization while using 3.8 GB of video memory. Other options like Allegro at 2.8B, Open-Sora Plan at 2.7B, LFM2 1.2B / 2.6B at 2.6B, Playground v2.5 at 2.6B, and Stable Diffusion 3.5 Medium at 2.5B also fit comfortably within the 4 GB limit using Q8_0 quantization.

When a model exceeds the 4 GB video memory limit, you must use CPU offload. This process splits the model layers between your graphics card and your system RAM. Offloading allows you to run larger models like Mistral 7B or Qwen2.5 0.5B / 1.5B / 3B / 7B, but it reduces processing speed because data must travel between the system RAM and the graphics processor.

For CPU offload scenarios, we assume your system has 32 GB of system RAM. Under this setup, a model like Mistral 7B requires 5.7 GB of memory at Q4_K_M quantization and needs 7.7 GB of system RAM. Similarly, Qwen2.5 7B, OLMo 2 1B / 7B, Falcon 3 1B / 3B / 7B, Command R7B, and OpenHermes 2.5 require 5.1 GB at Q4_K_M quantization and 7.1 GB of system RAM.

Memory consumption estimates are based on a standard 4k context window. Generating longer responses or processing larger inputs increases memory usage. If you exceed the 4 GB video memory limit during generation, the system will slow down or fail to complete the task.