Best local AI models for AMD Pro 555X

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 Radeon Pro 555X is a dedicated graphics card equipped with 4 GB of GDDR5 memory. This hardware memory limit dictates the size of the artificial intelligence models you can run locally. To load and execute a model entirely on the graphics processor, the model files and the active working memory must fit within this 4 GB boundary. Running models directly on the video memory ensures the fastest possible processing speeds.

To fit larger models into this hardware limit, developers use quantization. The quant column indicates the compression level applied to the model weights. For example, a Q4_K_M quant represents a four bit medium quantization, while a Q6_K or Q8_0 quant represents six bit or eight bit precision. Higher quantization levels like Q8_0 preserve more original model accuracy but require more memory. Lower levels like Q4_K_M allow larger models to fit into the limited 4 GB space.

For models that fit entirely on the card, you can run Lumina-Next or Lumina-Image 2.0 at 5B parameters using the Q4_K_M quant which uses 3.7 GB of memory. CogVideoX 2B or 5B at 5B parameters also fits using the Q4_K_M quant with 3.7 GB used. DeepSeek-VL2 at 4.5B parameters fits using Q5_K_M at 3.8 GB. DeepFloyd IF at 4.3B parameters uses 3.7 GB at Q5_K_M. Phi-3.5-vision at 4.2B parameters runs at Q5_K_M using 3.6 GB. Qwen3 4B, Gemma 3 4B, Gemma 4 E4B, MiniCPM 3 4B, Danube 3 4B, and Fish Speech 1.5 or OpenAudio S1 at 4B parameters all fit using the Q6_K quant which uses 3.9 GB of memory.

Other fully fitting options include Phi-4-mini-instruct and Phi-3.5 Mini at 3.8B parameters using the Q6_K quant with 3.7 GB used. OmniGen or OmniGen2 at 3.8B parameters also uses 3.7 GB at Q6_K. SD Cascade (Würstchen v3) at 3.6B parameters uses 3.5 GB at Q6_K. SDXL Turbo, SDXL Lightning, and ACE-Step at 3.5B parameters use 3.4 GB at Q6_K. MusicGen small/medium/large at 3.3B parameters uses 3.2 GB at Q6_K. SmolLM3 3B, Replit Code v1.5 3B, Kandinsky 3.1, Voxtral Mini or Small, Orpheus TTS, and Higgs Audio v2 at 3B parameters fit using the Q8_0 quant which uses 3.8 GB of memory. Allegro at 2.8B parameters uses 3.6 GB at Q8_0. Open-Sora Plan at 2.7B parameters uses 3.4 GB at Q8_0. LFM2 1.2B or 2.6B at 2.6B parameters and Playground v2.5 at 2.6B parameters use 3.3 GB at Q8_0. Stable Diffusion 3.5 Medium at 2.5B parameters uses 3.2 GB at Q8_0.

When a model is too large for the 4 GB video memory, you can use CPU offload. This technique splits the workload between your graphics card and your system RAM. We assume a system with 32 GB of system RAM for these cases. Offloading allows you to run larger models, but it comes with a speed penalty because system RAM is much slower than GDDR5 graphics memory.

Using CPU offload, you can run Stable Diffusion XL at 3.417B parameters which needs 4.1 GB at FP8 or optimized settings and 6.1 GB of system RAM. Phi-3 Mini at 3.8B parameters needs 4.4 GB at Q4_K_M and 6.4 GB of system RAM. Phi-4-multimodal at 5.6B parameters needs 4.1 GB at Q4_K_M and 6.1 GB of system RAM. Magicoder-S-DS 6.7B at 6.7B parameters needs 4.9 GB at Q4_K_M and 6.9 GB of system RAM. Mistral 7B at 7B parameters needs 5.7 GB at Q4_K_M and 7.7 GB of system RAM. Qwen2.5 0.5B or 1.5B or 3B or 7B at 7B parameters, OLMo 2 1B or 7B at 7B parameters, Falcon 3 1B or 3B or 7B at 7B parameters, Command R7B at 7B parameters, and OpenHermes 2.5 at 7B parameters all need 5.1 GB at Q4_K_M and 7.1 GB of system RAM.

Be aware of the context limit when running text models. The memory figures listed above are calculated using a basic 4k context window. If you increase the context length to process longer documents or chat histories, the memory requirements will rise. This extra memory usage might force a model that normally fits on the card to require CPU offloading.