Best local AI models for AMD FirePro W4300

The largest models that fit fully

The 30 largest of the 81 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 W4300 is a low profile workstation graphics card equipped with 4 GB of GDDR5 memory. This dedicated memory size determines which artificial intelligence models can run entirely on the hardware. When a model fits completely within this 4 GB limit, it processes data much faster because it does not rely on slower system memory. Keeping your model size below this threshold is key for local deployment.

To fit larger models into this memory envelope, you must use quantized versions. The quantization column shows the compression level used to shrink the model weights. For example, a Q4_K_M quant uses four bit quantization to fit the Lumina-Next 5B model into 3.7 GB of memory. Higher precision quants like Q6_K and Q8_0 offer better output quality but require more memory. The Q6_K quant fits 4B models like Qwen3 4B and Gemma 3 4B into 3.9 GB of memory.

If a model exceeds the 4 GB limit, you must use CPU offloading. This technique splits the workload between your graphics card and your system RAM. Assuming your computer has 32 GB of system RAM, you can run larger models by sending some layers to the CPU. For example, running Mistral 7B at Q4_K_M requires 5.7 GB of total space, which uses 7.7 GB of system RAM. Offloading lets you run these larger models, but it significantly reduces processing speed.

Many popular models can run with CPU offloading on this setup. The Qwen2.5 7B, OLMo 2 7B, Falcon 3 7B, Command R7B, and OpenHermes 2.5 models all need 5.1 GB at Q4_K_M and utilize 7.1 GB of system RAM. Similarly, the Magicoder-S-DS 6.7B model needs 4.9 GB at Q4_K_M and uses 6.9 GB of system RAM. Even multimodal options like Phi-4-multimodal can run by using 4.1 GB at Q4_K_M and 6.1 GB of system RAM.

For pure graphics card execution, you must select smaller models. The Phi-4-mini-instruct and Phi-3.5 Mini models both use 3.7 GB of memory at Q6_K. Image generation models like SDXL Turbo and SDXL Lightning fit into 3.4 GB of memory at Q6_K. Audio models like MusicGen medium use 3.2 GB of memory at Q6_K. If you prefer Q8_0 quantization, SmolLM3 3B and Kandinsky 3.1 fit into 3.8 GB of memory.

You must monitor your context window size during local execution. The memory numbers listed are calculated using a baseline 4k context window. If you increase the context window to process longer documents, the system will require more memory. This extra memory usage can push a model over the 4 GB limit, which will trigger slow CPU offloading automatically.