Best local AI models for AMD FirePro W8000

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 W8000 is a workstation graphics card equipped with 4 GB of GDDR5 memory. This memory size determines which local AI models can run entirely on the hardware. To fit within this limit, models must use quantization. Quantization reduces the size of the model weights. The quant column shows the best quality level that fits inside the onboard memory. If a model exceeds the available space, it cannot load completely on the graphics card.

For models that fit entirely on the card, you can run options up to 5B parameters. Lumina-Next or Lumina-Image 2.0 at 5B parameters fits using the Q4_K_M quant and uses 3.7 GB of memory. CogVideoX 2B or 5B also fits at 5B parameters using the Q4_K_M quant with 3.7 GB used. DeepSeek-VL2 at 4.5B parameters fits using the Q5_K_M quant and uses 3.8 GB. DeepFloyd IF at 4.3B parameters uses 3.7 GB at the Q5_K_M quant. Phi-3.5-vision at 4.2B parameters uses 3.6 GB at the Q5_K_M quant.

Several 4B parameter models run at the Q6_K quant level. Qwen3 4B, Gemma 3 4B, Gemma 4 E4B, MiniCPM 3 4B, Danube 3 4B, and Fish Speech 1.5 or OpenAudio S1 all use 3.9 GB of memory. Phi-4-mini-instruct, Phi-3.5 Mini, and OmniGen or OmniGen2 at 3.8B parameters use 3.7 GB of memory at the Q6_K quant level. SD Cascade, also known as Würstchen v3, at 3.6B parameters uses 3.5 GB. SDXL Turbo, SDXL Lightning, and ACE-Step at 3.5B parameters use 3.4 GB. MusicGen small/medium/large at 3.3B parameters uses 3.2 GB.

Smaller models can run at the higher Q8_0 quant level. SmolLM3 3B, Replit Code v1.5 3B, Kandinsky 3.1, Voxtral Mini or Small, Orpheus TTS, and Higgs Audio v2 at 3B parameters use 3.8 GB of memory. Allegro at 2.8B parameters uses 3.6 GB. Open-Sora Plan at 2.7B parameters uses 3.4 GB. LFM2 1.2B or 2.6B at 2.6B parameters uses 3.3 GB. Playground v2.5 at 2.6B parameters uses 3.3 GB. Stable Diffusion 3.5 Medium at 2.5B parameters uses 3.2 GB.

When a model is too large for the onboard memory, you must use CPU offload. This process splits the model between your graphics card and your system RAM. CPU offload requires a system with at least 32 GB of system RAM. Offloading allows you to run larger models, but it costs processing speed because system RAM is much slower than GDDR5 memory.

Several models require this offload setup. Stable Diffusion XL at 3.417B parameters needs 4.1 GB at FP8 or optimized settings and uses 6.1 GB of system RAM. Phi-3 Mini at 3.8B parameters needs 4.4 GB at the Q4_K_M quant and uses 6.4 GB of system RAM. Phi-4-multimodal at 5.6B parameters needs 4.1 GB at the Q4_K_M quant and uses 6.1 GB of system RAM. Magicoder-S-DS 6.7B needs 4.9 GB at the Q4_K_M quant and uses 6.9 GB of system RAM. Mistral 7B needs 5.7 GB at the Q4_K_M quant and uses 7.7 GB of system RAM.

Other 7B models also require CPU offload. 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, and OpenHermes 2.5 all need 5.1 GB at the Q4_K_M quant and use 7.1 GB of system RAM. All memory calculations assume a standard 4k context window. Increasing the context window size will require more memory and may prevent these models from loading.