Hi everyone
I’m looking for practical cooling advice for a field deployment.
I’ll be running a Raspberry Pi 5 with the Hailo-8 AI HAT+ (26 TOPS, first version) inside a weatherproof enclosure outdoors. I’m already using the official Raspberry Pi 5 Active Cooler (Pi 5 → Active Cooler → AI HAT+).
Has anyone deployed a similar stack in a weatherproof box and found a reliable way to reduce heat / avoid throttling? I’d love recommendations on:
enclosure approach (vented vs sealed, membrane vents, etc.)
using a metal backplate/enclosure as a heatsink
heatsink/cooling block options for the AI HAT+ and safe mounting
Any “worked / didn’t work” experiences or parts you recommend would be really helpful.
The power efficiency of Hailo devices allows passive cooling in most use cases.
If you are using a Hailo-8 M.2 module, we provide the Hailo Integration Tool, which enables you to validate your thermal solution. Hailo Integration Tool v1.20 requires HailoRT v4.20.
The thermal test runs synthetic networks while monitoring power consumption and temperature using the sensors on the M.2 module over a defined period. It then generates a report that includes the system’s thermal coefficient and indicates the ambient temperature range and performance levels that your thermal solution can support.
Just to clarify: I’m using the Raspberry Pi AI HAT+ (Hailo-8, 26 TOPS) on a Pi 5 — not an M.2 module. So I can’t use the Integration Tool workflow you mentioned.
As I understand, the main concern is heat build-up when the whole stack runs inside a weatherproof (potentially mostly sealed) outdoor enclosure.
For the HAT+, do you recommend:
any specific heatsink/cooling block on top of the Hailo area (e.g., a small copper/aluminium heatsink + thermal pad), and if yes, any “known good” dimensions/pad thickness?
or is it better to focus purely on enclosure thermal design (vented IP65 box / membrane vent / mounting to a metal backplate as a heatsink)?
Any guidance on what’s worked in similar deployments would be really appreciated.
One key issue identified is high CPU utilization on the Raspberry Pi.
The RPi lacks robust hardware video encoders (e.g., H.264), causing the GStreamer pipeline to rely heavily on the CPU.
Due to this bottleneck, using a dedicated NPU provides limited practical benefit.
Processing raw formats (e.g., YUV2) is also not feasible because of bandwidth constraints.
Overall, the RPi is not suitable for serious or industrial deployments.
Additionally, the RPi AI HAT uses a proprietary connector, making it incompatible with most industrial-grade SBCs that already include dedicated encoders.
Hailo M.2 modules are only available through distributors (typically one per country), each adding their own commission, increasing cost and complexity.
The RPi 5 + AI HAT combination appears more suitable for toy or proof-of-concept implementations rather than production systems.
As a result, the team is considering moving back to the Jetson Nano (Super) platform:
Offers industrial-scale carrier boards
Slightly higher power consumption is acceptable given the ecosystem and maturity
Finally, Hailo sales/support responsiveness has been poor, with queries largely unanswered.
Thank you for your detailed and thoughtful post. We sincerely appreciate the time you took to share these insights and provide feedback on your experience.
I would like to emphasize that our team strives to address as many inquiries as possible across this forum and our various other support channels. It is clear that, in this instance, we have not met the standards you expect, and that we set for ourselves.
We take this feedback seriously and will be conducting an internal review to identify where the communication gap occurred between our team and the regional distributors. Our goal is to ensure a more seamless support experience moving forward.
Thank you for your patience and for being a part of the Hailo community.
The best option is likely to mount the board inside the enclosure so that it’s easy to thermally connect the top of the Hailo-8 to the housing. Ideally, you would only need a thermal pad to bridge the gap.
I believe Hailo has built an excellent and highly scalable platform, particularly with the new Hailo-10H, which effectively combines vision and LLM workloads on one of the best low-power, on-edge, inference-only platforms available today.
However, there are two distinct user groups:
Enthusiasts / developers
OEMs and startups aiming for tight integration and large-scale deployment, especially in defense, smart city, and smart factory applications
For the latter group, long-term product support (5–10 years) is a critical requirement.
In this context, it may be beneficial to consider:
A dedicated OEM / industrial user community, or
Some form of direct initial engagement with the Hailo technical and sales teams to support early-stage integration
Following this initial phase, regional distributors could then handle bulk procurement and logistics more effectively.
The Hailo Community allows us to support customers more efficiently by sharing knowledge. For projects with larger volume or strategic value we can also work directly with customers without the public forum. If this applies to your project you can send us a private message and we will involve our sales team.
We do work with specialist distributors that have trained Field Applications Engineers and sales teams. They can support your design process from evaluation to production.