Best local AI models for AMD HD 8850M

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 HD 8850M is a mobile 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 graphics processor. When a model fits completely within this 2 GB limit, it executes with the fastest possible processing speeds because the hardware does not need to fetch data from slower system memory.

To make larger models fit inside this limited memory space, developers use quantization. The quantization column shows the optimal compression level for each model. For example, Q4_K_M represents a four bit quantization that balances model accuracy and memory usage. As model sizes decrease, you can use higher quality quantizations like Q6_K or Q8_0 which preserve more of the original model intelligence while still staying under the 2 GB hardware limit.

For models that exceed the local video memory, you must use CPU offload. This technique splits the workload between your graphics card and your system RAM. If you have 32 GB of system RAM, you can run larger models like SmolLM3 3B or Replit Code v1.5 3B. However, CPU offload comes with a performance cost. Transferring data between the system RAM and the graphics card slows down generation speeds significantly.

Several highly optimized models can run directly on the 2 GB frame buffer of the AMD HD 8850M. The largest fitting options include Allegro 2.8B and Open-Sora Plan 2.7B using the Q4_K_M quantization. Other compatible options include LFM2 2.6B, Playground v2.5, and Stable Diffusion 3.5 Medium. You can also run smaller models like SmolLM2 1.7B or TinyLlama 1.1B at higher quantization levels like Q6_K and Q8_0 for better output quality.

When running these local models, you must monitor your context window size. Running a model at a standard 4k context window increases memory consumption beyond the base model size. If your active memory usage exceeds the 2 GB limit of your AMD HD 8850M, the system will automatically offload the remaining data to your system RAM, which reduces processing speeds.