If you're evaluating AI capabilities for a display-focused device, you'll eventually face a decision: should the intelligence live on a general-purpose CPU, a powerful GPU, or a specialized AI accelerator like the Hailo 8?
This question is central to the design choices behind the ALC-4096-AIH board, which brings together a Raspberry Pi CM5, an integrated Hailo 8 accelerator, and a 4K LCD controller in a compact system. Each processing unit plays a role, but understanding their strengths and trade-offs can clarify how to architect your solution.
Comparison Table
Here we look at a simple comparison between the Hailo 8, a CPU, and an example GPU:
| Feature | Hailo-8 | CPU (e.g. Intel i7) | GPU (e.g. NVIDIA RTX 3050) |
|---|---|---|---|
| Purpose | AI inference at the edge | General-purpose computing | AI training & inference (broad use) |
| Architecture | Dataflow / tensor-centric | Von Neumann (sequential) | SIMD (parallel GPU cores) |
| TOPS (Performance) | ~26 TOPS (INT8) | ~0.2–0.5 TOPS (INT8 emulated) | ~8–10+ TOPS (FP16/INT8, consumer-level) |
| Power Consumption | ~2.5W | ~15–45W (mobile) / 65–125W (desktop) | ~35–150W+ depending on model |
| Latency | Very low (ms-scale real-time) | Moderate to high | Low, but requires more power and cooling |
| Size | Compact (M.2 or mini PCIe form factor) | Larger (embedded or PC) | Large (desktop PCIe) |
| Cost | Mid-range | High (if for embedded CPUs) | Varies from mid to high, plus system overhead |
| Training Capabilities | None (inference only) | Possible but very slow | Yes, good for training |
| Edge Suitability | Excellent | Poor to Moderate | Poor to Moderate (power and cooling limits) |
| Typical Use Cases | Cameras, drones, industrial controllers | Admin tasks, simple analytics | Desktops, data centers, some edge servers |
Contact us to discuss your project requirements further.