Best local AI models for AMD FirePro W5000

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 W5000 is an entry level professional graphics card equipped with 2 GB of GDDR5 video memory. This onboard memory capacity determines which artificial intelligence models can run directly on the hardware. When running models locally, the entire active weight set must fit within this 2 GB limit to avoid severe performance slowdowns.

To fit larger models into this limited space, developers use quantization. The quant column indicates the compression level applied to the model weights. For example, a Q4_K_M quant uses approximately four bits per weight, which allows models like the Allegro 2.8B or Open-Sora Plan 2.7B to fit within 2 GB of video memory. Higher quality quants like Q6_K or Q8_0 provide better precision but require more memory per parameter, limiting you to smaller models like the 2B parameter Stable Diffusion 3 Medium or the 1.55B parameter Whisper Large v3.

If a model exceeds the 2 GB video memory limit, you must use CPU offloading. This technique splits the model weights between your graphics card and your system RAM. For instance, running the 3B parameter SmolLM3 or Kandinsky 3.1 requires 2.2 GB of video memory at Q4_K_M, which exceeds the onboard capacity. By offloading, the system utilizes 4.2 GB of your system RAM to handle the overflow. While offloading allows you to run larger models like the 3.5B parameter SDXL Turbo, it significantly reduces processing speed because system RAM is much slower than GDDR5 video memory.

When deploying text models, you must also account for the context window. The memory figures listed cover the base model weights only. As you input longer prompts or generate longer responses, the active context history consumes additional video memory. Running a model near the 2 GB limit, such as the LFM2 2.6B at Q4_K_M using 1.9 GB, leaves almost no room for context. For stable text generation, you may need to restrict your context window to 4k tokens or switch to smaller models like the TinyLlama 1.1B which uses only 1.4 GB of video memory.

For specialized tasks, this card can run diverse architectures within its limits. You can run audio models like Parler-TTS 2.2B at Q5_K_M using 1.9 GB, or vision models like Moondream 2 1.9B at Q6_K using 1.9 GB. By matching the model size and quantization level to the 2 GB GDDR5 frame buffer, you can maintain local execution without relying on cloud services.