Best local AI models for AMD Pro 5600M

The largest models that fit fully

The 30 largest of the 123 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 Pro 5600M graphics card features 8 GB of high bandwidth HBM2 memory. This dedicated video memory determines the size of the artificial intelligence models you can run locally. To run a model entirely on the graphics processor, the model files and its working memory must fit within this 8 GB limit. If a model exceeds this capacity, it cannot run solely on the graphics card.

Quantization is a method that compresses model files to save space. The quant column shows the best compression level for each model on this hardware. For example, Mochi 1 is a 10B model that fits into 7.3 GB of memory using the Q4_K_M quant. Smaller models like Mistral 7B can run at the higher quality Q6_K quant while using 7.4 GB of memory. Higher quants preserve more original model accuracy but require more memory.

When a model is slightly too large for the 8 GB video memory, you can offload parts of it to your system RAM. This process requires a computer with sufficient system memory, such as 32 GB of system RAM. For instance, the Gemma 3 12B model requires 8.8 GB of video memory at the Q4_K_M quant and needs an additional 10.8 GB of system RAM to function. Offloading allows you to run larger models like Vicuna 13B, which needs 9.5 GB at Q4_K_M and 11.5 GB of system RAM.

Using CPU offload comes with a performance cost. Transferring data between the AMD Pro 5600M graphics card and the system RAM is much slower than keeping everything inside the HBM2 memory. Models like FLUX.1 dev require 14.4 GB at FP8 or optimized settings and need 16.4 GB of system RAM. These offloaded models will generate tokens or images at a slower rate than models that fit completely within the 8 GB video memory limit.

You must also consider the memory cost of context length. The memory figures listed are calculated using a standard 4k context window. If you increase the context window to process longer documents or chat histories, the model will require more memory. Running a model close to the 8 GB limit, such as Gemma 2 9B at Q4_K_M using 8 GB, leaves no room for context expansion and may cause out of memory errors.