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The first edge accelerator built to run LLMs and VLMs on-device.
Pros
- Brings true generative AI to low-power edge devices
- Direct DDR lets it scale beyond on-chip-only accelerators
- Strong efficiency vs GPU-based alternatives
Cons
- Newer product with a smaller ecosystem than NVIDIA
- 40 TOPS still modest for very large models
- Requires a capable host and memory configuration
✓ Where it shines / best for
- OEMs adding local generative AI to PCs/mini-PCs via M.2
- Edge developers deploying private on-device LLM/VLM inference
- Upgrading existing devices with an M.2 slot for AI offload
✕ Not the best fit for
- Model training or cloud-scale inference
- Frontier-scale LLMs needing large memory pools
- Consumers wanting a turnkey, no-integration product
Features
- ✓ On-device / offline
- ✓ Edge AI
- ✓ Real-time
- ✓ Low-power / efficient
- ✓ Generative AI
- ✓ LLM Inference
- ✓ M2 Module
- ✓ Transformer
- ✓ Vision Language
Pricing
| Plan | Price | Billing | Notes |
|---|---|---|---|
| Hailo-10H M.2 Module | Contact vendor | one-time | M.2 2242/2280 module; pricing via Hailo/distributors, eval kits separate |
Pricing verified from the official source. Prices change often — confirm on the vendor's site before buying.
Specifications
| power | Low-power edge (sub-5W, efficiency-optimized) |
| memory | External DDR (configurable) |
| interface | PCIe (M.2) |
| architecture | Hailo-10H neural processor with direct DDR interface |
| ai_performance | 40 INT4 TOPS |
Sponsored
A full review is being generated for this product and will appear here shortly.