Best local AI models for AMD R7 M445

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 R7 M445 is an entry level mobile graphics card equipped with 4 GB of GDDR5 video memory. This dedicated memory pool determines the maximum size of the artificial intelligence models you can run entirely on the graphics hardware. To execute a model without performance penalties, the model files and the active working memory must fit completely within this 4 GB limit.

Quantization is a compression method that reduces the size of model files by lowering the precision of their weights. In our tables, the best quant column shows the optimal balance between model accuracy and memory consumption. For example, 4B models like Qwen3 4B, Gemma 3 4B, and Gemma 4 E4B can run at a high quality Q6_K quantization using 3.9 GB of video memory. Larger 5B models like Lumina-Next and CogVideoX 5B require a more aggressive Q4_K_M quantization to fit within 3.7 GB of video memory.

When a model exceeds the 4 GB video memory limit, you must use CPU offloading. This technique splits the model layers between your graphics card and your system memory. Assuming your computer has 32 GB of system RAM, you can run larger models with a performance slowdown. For instance, 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, and Command R7B require 5.1 GB of memory at Q4_K_M quantization, which uses 7.1 GB of system RAM.

Image generation and multimodal models also fit within these strict memory boundaries. You can run Stable Diffusion 3.5 Medium at Q8_0 quantization using 3.2 GB of video memory. SDXL Turbo and SDXL Lightning fit at Q6_K quantization using 3.4 GB of video memory. If you choose to offload, Stable Diffusion XL at FP8 or optimized settings requires 4.1 GB of memory and uses 6.1 GB of system RAM.

You must monitor your context window usage when running local models on this hardware. The memory figures listed are calculated using a standard 4k context window. If you increase the context length to process longer documents or extended conversations, the memory requirements will rise quickly. This extra memory usage can overflow the 4 GB video memory limit and force the system to slow down.