Best local AI models for AMD R9 M385

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 R9 M385 is a mobile graphics card equipped with 4 GB of GDDR5 video memory. This hardware limit determines the size of the artificial intelligence models you can run locally on your system. To load a model entirely into your graphics memory, the model size and its active workspace must not exceed this 4 GB threshold. Running models directly on your video memory ensures the fastest processing speeds for text generation, image creation, and audio synthesis.

To fit within this 4 GB limit, models use quantization to reduce their file size. The quantization level represents how much the model weights are compressed. For example, a Q6_K quant offers high precision but requires more memory, while a Q4_K_M quant compresses the model further to save space. Our data shows the best quantization level that fits your hardware. This allows you to run models like the Lumina-Next 5B or CogVideoX 5B at a Q4_K_M quantization, which uses 3.7 GB of your video memory.

Other models can run at higher precision levels on your hardware. You can run Qwen3 4B, Gemma 3 4B, Gemma 4 E4B, MiniCPM 3 4B, Danube 3 4B, and Fish Speech 1.5 at a Q6_K quantization using 3.9 GB of video memory. The Phi-4-mini-instruct and Phi-3.5 Mini models also fit at Q6_K quantization, using 3.7 GB. For smaller models like SmolLM3 3B, Replit Code v1.5 3B, and Kandinsky 3.1, you can use the highly precise Q8_0 quantization, which fits within 3.8 GB of video memory.

When a model is too large for your 4 GB video memory, you can use CPU offloading. This technique splits the model weights between your graphics card and your system memory. If you have 32 GB of system RAM, you can run larger models by accepting slower processing speeds. For example, Mistral 7B requires 5.7 GB of memory at Q4_K_M quantization, which uses 7.7 GB of system RAM. Similarly, Qwen2.5 7B, OLMo 2 7B, Falcon 3 7B, Command R7B, and OpenHermes 2.5 require 5.1 GB at Q4_K_M, using 7.1 GB of system RAM.

You must also consider the context window when planning your memory usage. The context window is the amount of text the model can remember during a conversation. A standard 4k context window requires a specific amount of working memory. If you increase the context length beyond 4000 tokens, the memory usage will rise quickly. This extra memory demand can exceed your 4 GB limit and force the system to slow down or fail to respond.