Best local AI models for AMD FirePro R5000

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 R5000 is an older professional graphics card equipped with 2 GB of GDDR5 onboard memory. This hardware memory limit dictates the size of the artificial intelligence models you can run locally. To load a model entirely onto the graphics card, the model files and the active memory space must not exceed this 2 GB threshold. Running models locally on your hardware ensures complete data privacy and eliminates external subscription costs.

Model quantization is a method that compresses the weight parameters of neural networks to save memory. The quantization column shows the best format for each model, such as Q4_K_M, Q5_K_M, Q6_K, or Q8_0. Lower quantization levels like Q4_K_M reduce the memory footprint to fit larger models like the Allegro 2.8B or Open-Sora Plan 2.7B into the 2 GB frame. Higher quantization levels like Q8_0 offer better precision but require more memory, limiting you to smaller models like the TinyLlama 1.1B.

When a model exceeds the 2 GB onboard memory, you can offload parts of the workload to your system RAM. This process requires a system with ample memory, such as 32 GB of system RAM. Offloading allows you to run larger models like the SmolLM3 3B or MusicGen, but it introduces a performance cost. Transferring data between the graphics card and system RAM is much slower than using GDDR5 memory directly, which reduces the generation speed.

For fully local execution, several compact models fit within the 2 GB limit of the AMD FirePro R5000. The LFM2 2.6B and Playground v2.5 models run at Q4_K_M quantization while using 1.9 GB of memory. You can also run the Stable Diffusion 3.5 Medium and Canary 2.5B models at Q4_K_M quantization with 1.8 GB of memory usage. Highly optimized models like the SmolVLM 2B and Stable Diffusion 3 Medium run at Q6_K quantization using the full 2 GB of memory.

Audio and speech models are also compatible with this hardware. The SeamlessM4T v2 2.3B model fits using 2 GB of memory at Q5_K_M quantization. Whisper Large v3 fits at Q8_0 quantization using 2 GB of memory, while Whisper Large v2 fits at Q8_0 quantization using 1.9 GB of memory. For text to speech tasks, the Parler-TTS 2.2B model runs at Q5_K_M quantization while using 1.9 GB of memory.

When running text models, you must consider the context window size. The standard 4k context window requires additional memory during active generation. If you use a model close to the 2 GB limit, processing long text prompts or generating long responses can exceed your hardware memory. To prevent crashes, you may need to reduce the context length or use a smaller model like the SantaCoder 1.1B which uses 1.4 GB of memory at Q8_0 quantization.