The AI Stack

Flexibility and future-proofing. When RTX 5090/6090 drops, just swap the GPU. The Mini PC handles CPU tasks, the eGPU handles AI inference. Plus, Mini PCs are silent, portable, and take up minimal desk space.

Yes! Both RTX 3090 and 4090 have 24GB VRAM. With 4-bit quantization (Q4_K_M), 70B models fit comfortably. The 4090 will be about 2x faster for inference, but the 3090 is perfectly capable.

These builds crush SDXL and Flux. The 4090 generates images in seconds. Even the 3090 handles 1024x1024 SDXL images in under 10 seconds. ComfyUI, Automatic1111, Forge - all work perfectly.

Yes, significantly. OCuLink provides PCIe 4.0 x4 speeds (64 Gbps) vs Thunderbolt 4's 40 Gbps. That's 60% more bandwidth. For AI workloads that move lots of data, OCuLink is the clear winner.

The RTX 5090 has 32GB VRAM vs 24GB, which matters for larger models. But it's $4,500+ and availability is limited. The 4090 is proven, available, and handles 99% of local AI tasks today. Your call.

eGPU builds use a discrete graphics card with dedicated VRAM (24GB on RTX 3090/4090). Faster per-operation but limited memory. The Unified Build (EVO-X2) uses AMD's Strix Halo architecture where CPU and GPU share 128GB of system RAM - you can allocate up to 96GB as VRAM. Slower per-token, but can run much larger models (100B+) that won't fit on 24GB cards.

Both work, but differently. RTX 4090 runs 70B at Q4 quantization faster. EVO-X2 runs 70B at Q8 quantization (higher quality) because it has more VRAM. If speed matters most: eGPU. If model quality matters most: Unified.