Best local AI models for AMD FirePro W600

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 W600 is equipped with 2 GB of GDDR5 memory. This hardware specification determines the maximum size of the artificial intelligence models you can run locally. To fit within this memory limit, models must be compressed using quantization. Quantization reduces the precision of model weights to save space. The quant column shows the best available quantization level that allows each model to fit on this specific hardware.

For models that fit entirely on the card, the Allegro 2.8B model is the largest option at a Q4_K_M quantization. This configuration uses exactly 2 GB of memory. Other options like the Open-Sora Plan 2.7B model also run at Q4_K_M quantization and utilize 2 GB of memory. Smaller models like the LFM2 2.6B and Playground v2.5 use 1.9 GB of memory at the same Q4_K_M quantization level.

As model sizes decrease, you can use higher precision quantization levels. The SmolVLM 2B, Stable Diffusion 3 Medium, and Pyramid Flow 2B models can run at a higher Q6_K quantization while using 2 GB of memory. For even smaller models like the StableLM 2 1.6B, Sana 1.6B, and Whisper Large v3 1.55B, you can use the Q8_0 quantization level which fits within the 2 GB memory limit.

When a model is too large for the 2 GB video memory, you can use CPU offload. This technique splits the model between your graphics card and your system RAM. For example, running the SmolLM3 3B or Kandinsky 3.1 model requires 2.2 GB of memory at Q4_K_M quantization. This setup uses your 2 GB of video memory and offloads the remaining 4.2 GB to your system RAM.

Larger models require more system RAM when offloading. The MusicGen 3.3B model needs 2.4 GB of memory at Q4_K_M quantization and requires 4.4 GB of system RAM. The Stable Diffusion XL 3.417B model requires 4.1 GB of memory at FP8 or optimized settings and needs 6.1 GB of system RAM. Offloading allows you to run these larger models but it will slow down processing speeds.

You must also consider the memory cost of context length. Running models with a standard 4k context window requires additional memory for the active conversation history. This extra memory requirement is not included in the base model file sizes listed above. If you experience out of memory errors, you may need to reduce your context window size or choose a smaller model.