Best local AI models for AMD FirePro M6100

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 FirePro M6100 is a legacy mobile workstation graphics card equipped with 2 GB of GDDR5 memory. This dedicated memory size dictates the maximum size of the artificial intelligence models you can run entirely on the hardware. To run local models successfully, the model weights and the active working memory must fit within this 2 GB limit. If a model exceeds this boundary, the system must use alternative execution strategies.

The quantization column indicates the compression level applied to each model to make it fit your hardware. Quantization formats like Q4_K_M, Q5_K_M, Q6_K, and Q8_0 reduce the precision of the model weights to save space. Lower quantizations like Q4_K_M allow larger models such as the Allegro 2.8B or the Open-Sora Plan 2.7B to run using exactly 2 GB of memory. Higher quantizations like Q8_0 preserve more original model quality but limit you to smaller models like the TinyLlama 1.1B which uses 1.4 GB of memory.

When a model is slightly too large for the 2 GB GDDR5 memory, you can use CPU offload. This technique splits the workload between your graphics card and your system RAM. For example, running the SmolLM3 3B or the Replit Code v1.5 3B at Q4_K_M requires 2.2 GB of video memory and 4.2 GB of system RAM. This approach allows you to run larger models like SDXL Turbo at 3.5B, but the transfer of data between system RAM and your graphics card will slow down the generation speed.

Context window size also heavily impacts your memory usage. The memory figures listed for these models assume a standard base context. If you increase the context window to 4k tokens or higher, the memory required for the active session history will grow rapidly. This extra memory demand can easily push a model that fits at startup over the 2 GB limit, which will trigger slow system RAM fallback or cause execution errors.

For optimal performance without offloading, you should target models that stay safely under the memory limit. Models like the Qwen3 1.7B or the SmolLM2 1.7B use 1.7 GB of memory at Q6_K, leaving a small safety margin for system overhead. If you require maximum output quality, smaller models like the Whisper Large v3 at 1.55B can run at Q8_0 quantization while utilizing the full 2 GB of your hardware memory.