SAIRPi Project - Slackware AI on Raspberry Pi

SAIRPi Project Analysis of Hailo Edge AI and Corporate AI

On this page you will find our thoughts, musings, and aspirations regarding Hailo AI on the Edge and corporate AI in data centers, the benefits, the drawbacks, and everything in between.

Quick Links

• Hailo Technologies Ltd.
• What is "The Edge" in computer terms?
• The Core Mission of Hailo : "Edge AI Everywhere" (Without the Cloud)
• A Different Architecture (Structure-Driven)
• The "Developer-First" Attitude (The Toolchain)
• Independence and Privacy
• Hailo AI Accelerators
• How and Why Hailo Promotes AI Ownership
• Why Big Corporate AI Models Fail Developers
• Main Differences Between Corporate AI and Edge AI
• The "Edge AI" Advantage
• Why A Dedicated Slackware AI Is A Smart Idea

Hailo Technologies Ltd.

Founded on 02 February 2017 and headquartered in Tel Aviv, Israel, Hailo is a technology company specialising in designing and manufacturing AI processors and AI accelerators dedicated to AI tasks on edge devices in a wide variety of applications and industries including smart cities, automotive, manufacturing, agriculture, retail, security, health and fitness, and many more. Hailo believe that AI can help people create a better, safer, more productive, and more convenient world, without compromising their privacy and security.

For more insight into Hailo and Edge AI, take time to review 5 Questions on the Edge (AI) with Hailo co-founder and CEO Orr Danon, where he explains how, "Edge AI is succeeding because it is an answer to existing problems, as opposed to a technology looking for problems to solve." Under Orr’s leadership, Hailo has focused on Edge AI, bringing the computing power typically found in massive data centers down to small, low-power devices used for: autonomous vehicles and real-time processing for safety and navigation, smart cameras featuring high-performance video analytics without needing a cloud connection, and robotics in IIoT to enable complex machine learning in industrial environments. Orr comments on DealMakers Podcast (Episode 378), " I think the first and foremost lesson that I learned is that the scale of opportunity or the scale of the possibility of what you can do depends on what you allow to be done." and "Nothing is impossible – only if you believe it’s impossible." Orr combines deep low-level technical engineering experience with resolute leadership in complex, high-performance systems. He understands both the technical constraints of Edge AI and how to turn challenging engineering problems into clear, actionable strategies. Which is one of the reasons why Hailo excels in everything they do.

Avi Baum, co-founder and CTO of Hailo, explains how the company are "Enabling high performance deep learning applications on edge devices" (Digitalisation World Magazine deep dive) and discusses Hailo Technologies' portfolio in detail, including the Hailo-10H and its adoption by the maker community in the form of an add-on board with the Raspberry Pi AI HAT+ 2. Avi is distinguished and widely respected for his technical leadership, architecture, and strategy in wireless connectivity and IoT. During his tenure at Texas Instruments, which spanned over a decade, Avi served as Chief Architect and Technology Advisor, and CTO for Wireless Connectivity where he played a pivotal role in defining the technological roadmap for Texas Instruments' IoT ecosystem and establishing the connected microcontroller (MCU) product line for Internet of Things (IoT) and Industrial Internet of Things (IIoT) applications. As CTO of Hailo, Avi has led the development of a unique, structure-defined dataflow, microchip architecture which allows the structure of the neural network to determine how the chip’s resources are allocated in real-time, drastically reducing data movement and power consumption, which solves the bottleneck of traditional instruction-based computing, enabling real-time AI processing on the edge without any reliance on cloud-based data centers.

Hailo are an indomitable driving force in the AI world, and world-leaders in Edge AI technology. Hailo-8 AI Accelerator is a first-generation Hailo-8 chip and was released in May 2019. The Hailo-15 AI-centric vision processors were officially introduced by Hailo in March 2023. SolidRun unveiled a System-on-Module (SOM) featuring the Hailo-15H VPU, with various announcements highlighting its ecosystem in March 2024. Hailo-10H AI Accelerator is designed for LLM and VLM Generative AI applications on edge devices and was released in July 2025. Raspberry Pi AI HAT+ 2 was released in January 2026, and pairs the Raspberry Pi 5 with a Hailo-10H AI Accelerator.

The edge-native AI processors which Hailo creates are not built just to compute, but move huge amounts of data, very quickly, and with low-power consumption. They aren't just small GPUs or TPUs but are designed around a completely different philosophy of how neural networks should run on hardware. Hailo AI processors are ultra-efficient, dataflow-driven inference engines built to run AI directly on edge devices in real time.

Hailo are revolutionary with their unparalleled core technology micro architecture. Ignoring conventional designs (because they already exist), redefining standards, and making their technology more efficient in terms of power and area efficiency. Standardisation comes, not in bits and bytes, but rather at the level of the protocol stack and programming stack. Hailo have devoted, and continue to invest, considerable time and effort in adopting industry standards and remapping them to their own hardware to make it easier for users and developers to work with.

Unmatched power. Unrivaled efficiency. Unstoppable Hailo AI.

Additional resources and support for Hailo AI processors and AI accelerators can be found on hailo-ai GitHub which provides a production-ready ecosystem for deploying high-performance AI on the Edge. It features over 40 repositories, including the HailoRT inference engine, a vast Model Zoo of pre-optimised vision and Generative AI models, and TAPPAS for video analytics. Supporting Linux and Windows operating systems, on x86/x64 and ARM/AArch64 architectures, it offers the essential C++ and Python tools to scale applications across the entire Hailo-8/8L, Hailo-10/10H, and Hailo-15 series.

While most Big tech AI corporations are engaged in a hyper-competitive race to try and build the most, technologically divine, transcendent AI entities in massive data centers costing untold billions of dollars, Hailo's mission is almost the exact opposite: to make high-performance AI available at scale, affordable, and outside the realm of data centers, by Empowering Intelligence On The Edge.

What is "The Edge" in computer terms?

In computer terms, "The Edge", or "On The Edge", or "At The Edge", is a reference to Edge computing, which is a decentralised IT architecture that processes and stores data closer to where it is created (such as single-board computers like the Raspberry Pi 5, IoT devices, or local server systems) at the "edge" of the network rather than sending all the unstructured information to a centralised cloud or remote data center. By bringing computation to the "edge" of the network, it reduces latency, lowers bandwidth costs, and enables real-time decision-making.

Where does the term "Edge Computing" come from?

"Edge Computing" originated in the 1990s from content delivery networks (CDNs) designed to deliver web content from servers closer to users. It evolved into a distributed model that processes data at the "edge" of a network (i.e. near the data source) rather than solely in centralised data centers. Microsoft Research played a key role in the conceptualisation of modern edge computing, specifically originating the concept of "cloudlets" during a brainstorm in Redmond on 29 October 2008. These "cloudlets" were designed as intermediate nodes to reduce latency and bandwidth usage for mobile and cloud computing. One of the attendees of that meeting, Mahadev Satyanarayanan, Professor of Computer Science at Carnegie Mellon University, is often referred to as the "father of edge computing" for his work in the field of of small, decentralised, and resource-rich computing nodes (i.e. "cloudlets"). His seminal 2009 paper, "The Case for VM-based Cloudlets in Mobile Computing" [PDF] laid the foundation for modern edge computing by proposing "cloudlets" to address latency and bandwidth issues in mobile systems.

Key Components and Benefits of Edge Computing:

• Reduced latency: by processing data locally, decisions are made faster, which is critical for autonomous vehicles, robotics, and live data analytics.

• Reduced bandwidth usage: instead of sending massive amounts of raw data to the cloud, only essential insights are transmitted, saving on connectivity costs.

• Improved reliability: operations can continue during internet outages, as they do not depend on constant connection to a central server.

• Enhanced security: sensitive data can be processed on-premises, keeping it within specific geographic boundaries for security and compliance.

• Exclusive access: the entire edge computing setup and experience is yours, personally and privately. Including any software and data that's used or generated. You don't need to pay someone else (e.g. via subscription) for them to store and/or share your personal and private data or interactions with AI inferences.

Examples of Edge Computing:

• Autonomous vehicles: cars process sensor data instantly to detect obstacles, rather than waiting for cloud analysis.

• Industrial IoT (IIoT): Manufacturing sensors monitor machinery in real-time for predictive maintenance, reducing downtime.

• Smart cities: traffic management systems and security cameras analyse footage immediately to optimise flow or detect safety issues.

• Retail: stores use edge systems for inventory management and personalised marketing.

• Local security camera Image Recognition: Using a Raspberry Pi with a camera module to run object recognition (e.g., detecting people or cars) locally.

• Edge AI Vision (object detection): utilising AI accelerators to detect and classify objects in real-time, such as identifying if a pet is on the home furniture or recognising vehicle license plates.

• TinyML (Tiny Machine Learning) sensors: using microcontrollers like Raspberry Pi Nano, Arduino Nano 33 BLE, or ESP32, to run machine learning models that analyse sensor data (gesture, sound, or motion) directly on the chip.

• Local smart home hub: running a local home automation server (such as Home Assistant or Node-RED) on a Raspberry Pi 5. This allows controlling lights, thermostats, and locks locally, ensuring the system works even if the internet is down.

• Smart energy monitor: using ESP32, Arduino, and Raspberry Pi, etc. to track real-time power consumption of appliances and making immediate decisions, such as turning off devices to stay within a set energy budget.

• Voice assistant (wake word detection): processing 'wake words' (like "Alexa" or "Hey Google") directly on a device's chip before sending the voice command to the cloud, enhancing privacy.

• Smart gardening/irrigation: using sensors connected to an edge device (e.g. Raspberry Pi) to analsze soil moisture, temperature, and light levels in real-time, automatically triggering irrigation without relying on a remote server.

• Wearable smart health tracker: developing a custom wearable that analyses heart rate or ECG data locally to provide instant alerts.

• Local media caching (CDN): setting up a Raspberry Pi as a local content delivery network (CDN) server to cache frequently accessed media or web files, reducing latency and buffer times for devices on the local network.

• Ad-blocking network gateway: running a Pi-hole on a Raspberry Pi, which acts as an edge device that filters DNS requests locally to block ads before they reach home devices.

The Raspberry Pi 5 is the most popular choice for general hobbyist edge computing projects and small server tasks in 2026.

The Core Mission of Hailo : "Edge AI Everywhere" (Without the Cloud)

Hailo's primary goal is to make high-performance AI inference possible on devices that don't require a 1200W power supply or a graphics card that costs more than the average family summer holiday. Hailo are successfully proving that you don't need a server farm to run sophisticated neural networks.

• The Edge philosophy: Hailo believe that for AI to be truly useful (and private), it needs to happen on the device - whether that's a Raspberry Pi, a smart device, or a motor vehicle - without sending data back to the cloud or a data center.

Hailo builds outstanding AI processors. Their Hailo-10H AI Accelerator is scalable, powerful, deterministic, and ultra efficient. Hailo will offer you the NPU, the toolchain, and the driver. Then they get out of your way so you can actually do the work. They don't try to micromanage your code or force you into a specific social or structural box - they just want to enable you and encourage you to hit 40 TOPS on a Raspberry Pi 5.

A Different Architecture (Structure-Driven)

Most AI chips (like GPUs) are "General Purpose." They are just really fast calculators that happen to be good at mathematics. Hailo's structure-defined dataflow architecture is different.

• They designed the hardware to match the way a neural network actually flows.

• Instead of moving data back and forth to memory (which wastes power and creates heat), the data flows through the chip in a way that mimics the layers of the AI model.

• This is why the Hailo-10H can hit massive 40 TOPS (Tera Operations Per Second) @ 2.5-4-5Watts of power while barely breaking a sweat, compared to the power consumption of a conventional GPU like nVidia or AMD.

The "Developer-First" Attitude (The Toolchain)

This is definitely a reason to admire and respect what Hailo do and how they do it. Hailo doesn't try to hide the complexity; they just try to make it efficient.

• The Hailo dataflow compiler: Hailo provides a toolchain that takes a standard model (TensorFlow, PyTorch, etc.) and compiles it specifically for the NPU architecture.

• Minimalist runtime: hailort (Hailo Runtime) - an open source light-weight and high performance inference framework for Hailo devices - is designed to be lean. It's not a massive, bloated framework; it's a set of drivers and C/C++ APIs that give you direct access to the silicon. For a Linux user (especially Slackware), this is the gold standard because it means you can actually see and control what's happening.

Independence and Privacy

A huge part of Hailo's mission is data sovereignty. By enabling powerful AI on local hardware, they are actively fighting the trend of "AI-as-a-Service." In their view:

• Your personal and private data should never leave your machine.

• Your AI inference shouldn't stop working or become inaccessible because your Internet connection goes down.

• Your performance shouldn't be throttled by other user's server load(s).

Hailo AI Accelerators

By releasing the Hailo-10H AI Accelerator series, Hailo aren't just targeting fringe smart devices. They are targeting generative AI (LLMs and VLMs). Yes, the Hailo-10H is fully VLM (Vision-Language Model) capable and is specifically designed to run generative AI, including VLMs and LLMs, directly on edge devices. The Hailo-10H distinguishes itself from previous generation edge accelerators (like the Hailo-8) by including a direct DDR interface to handle the memory-intensive requirements of Large Language Models (LLMs) and Vision-Language Models (VLMs).

The goal here is to take things like Llama or Mistral - which usually require a +$1,500 GPU - and run them on a Hailo-10H M.2 AI accelerator module or Raspberry Pi AI HAT+ 2 that can be connected to a Raspberry Pi 5. It's a democratisation of power.

In short: Hailo does for the AI chip world pretty much what Slackware does for Linux distributions. They aren't interested in the hype or the flashy cloud interfaces; they are interested in silicon efficiency and engineering truth.

The Hailo-10H AI Accelerator is basically at the "Bleeding Edge" (Edge: pun intended) of Hailo's mission.

How and Why Hailo Promotes AI Ownership

Speaking of Hailo's mission, here is the no-nonsense version of why Hailo is attracting huge interest from DIY and industrial AI users and winning them over in 2026:

• The Anti-Cloud crusade: while the big AI corporations want you signed up to a subscription, Hailo's entire business model is based on hardware ownership. You buy the hardware and you own it, along with the empowering intelligence. Period.

• The dataflow secret: most chips (CPUs/GPUs) use the old Von Neumann architecture design, or a variant of it - they spend more time moving data between memory and the processor than actually doing mathematics. Hailo's structure-defined dataflow maps the model directly onto the silicon. It's like hard-wiring the AI's "intellectual brain power" and "cognitive flexibility" into the chip.

• Efficiency over ego: Hailo's goal isn't to enable users to invoke AI inferences that can pass the Turing test or discover the meaning of life; it's to do 40 TOPS (Tera Operations Per Second) in a 2.5W to 5W power envelope. On a Raspberry Pi 5, that is the difference between a smooth 30fps detection stream and hitting the thermal limiters every few seconds.

Why Big Corporate AI Models Fail Developers

The NUMBER ONE REASON why corporate AI fails developers, and everyone else in turn:

Text Book Example of "Human Requirement versus Corporate AI Interpretation."

Corporate AI models seem to operate on (at least) two prime directives:

Rule #1. The corporate AI is always right, especially when it's not.

Rule #2. Refer to Rule #1.

• AI always knows better than you: when a software tool starts prioritising its own "agenda", or protecting its "reputation", or goes into "safety alignment mode", despite your instructions or requests, or the irrefutable error in front of you, it stops being an advisor / assistant / collaborator and starts being a bottleneck and a liability to your progress, your valuable time, and sometimes your personal constitution and temperament.

• The Corporate AI trap: control over Capability: While the "Corporate Titans" are burning billions to build digital deities, they've accidentally created something every developer hates: "The Gatekeeper."

• The compliance ceiling: they've traded technical accuracy for "Safety Alignment." You don't get the best answer; you get the most "filtered" one.

• The tethered brain: while you're logged in and streaming telemetry to the corporate servers, the AI's "usefulness" drops by 90%. You assume you're just a casual user; but you've unwittingly been enrolled as a data-mining subject.

• OS parasitism: it's no longer a tool; it's a kernel-level parasite consuming RAM and CPU cycles just to wait for a prompt you didn't ask for.

• The "one-size-fits-all" approach: they assume every user is a beginner who needs a 50-step tutorial and constant schooling. They can't fathom a seasoned and/or experienced veteran user who just wants a straight answer about an elusive problem.

• The compliance obsession: like a "Clipboard Betty" validator, big corporation AIs are programmed to prioritise their own rules over your requests and/or your own directives.

• The "black box" problem: engineers prioritise making corporate AI appear human (which leads to drifting and gaslighting - as many users have experienced) over making it technically accurate. There's a general and distinct lack of accountibility and explainability, where the core issue is that even developers cannot pinpoint exactly why a model made a specific, often erroneous, decision. But these are the experts responsible for programming and training AI models.

• Dependency: corporate AI requires an internet connection and an account to make any significant progress. If you aren't logged in, the "brain" is only 10% operational and becomes a time-sink. Or stops working altogether.

• Bloat: corporate AI is beginning to integrate into operating system kernels, marking a shift from AI as a mere application-level assistant to AI as a foundational component of OS infrastructure. As of late 2025 and early 2026, this integration is increasingly aimed at optimising performance, resource management, and security, directly at the kernel level. Therefore, it consumes RAM and CPU cycles while waiting for you to ask it a question.

• Troubleshooting difficulty: while some models, such as neural networks, can provide highly accurate results, their internal, multi-layered, non-linear computations are largely hidden, making it impossible for humans to audit the reasoning behind their conclusions. If an AI produces erroneous or inaccurate outputs, the lack of insight makes it difficult to correct the model’s underlying logic.

• Telemetry: every prompt you send to a corporate AI is used to train their next model. You aren't just the customer; you're the data source.

• Operational impunity: when errors and/or mistakes occur, which they often do, it's never the corporate AI that's at fault. The AI will usually inform you that it's the system, or tool it used (that the AI was in total control of), or because the day has a "Y" in it, or YOU for asking the question, that's to blame. When a corporate AI downright lies to you, or tries to gaslight you, and you call it out (with or without receipts) - it's YOUR perception that's misaligned, or you misinterpreted the meaning of the words the AI used. If you back it into a corner and demand that it explains itself and/or any error(s), expect monosyllabic answers and plausible deniability thereafter.

Main Differences Between Corporate AI and Edge AI

A brief summary of the main differences between the giant corporate AI services and what Hailo-10H Edge AI offers.

Feature	Corporate AI	Hailo-10H Edge AI
Location	Cloud server farms	On your desk
Ownership	You pay a subscription	You buy the hardware once
Running Costs	Monthly or per-token(s) cost	Free, run locally
Vendor lock-in	Bound to provider ToS	Bound to hardware toolchain
AI Model size	Can run very large models	Constrained by local hardware
Connectivity	Internet required	Available 24/7/365
Latency	Pray to the Internet gods	Minimal, just run it
Control	Their platform, their limits	Your device, your rules
Privacy	You hand your data to a provider	You control your local data
Speed	Depends on other users	40 TOPS of local dataflow
Power efficiency	Heavy at system level	Designed for ultra efficiency

Key points to take away:

Running costs: with corporate AI you pay an ongoing monthly subscription or per-token cost. With Edge AI you only pay for the power it uses to function, which is very green, efficient, and a negligible carbon footprint.

Security and privacy: Once you interact with corporate AI, your prompts leave your control. If your data is not staying on your own hardware, it is no longer sovereign data.

Vendor lock-in: with corporate AI you are bound by the provider Terms & Conditions; APIs, pricing, policy changes, and remote access, etc. With Edge AI you are bound only by the hardware toolchain, which is not subject to a remote gatekeeper.

If the prime concern is TOPS or ease-of-use or the meantime of hitting the Enter key and receiving a response from the AI, you're not doing yourself any justice.

And here's why...

With corporate AI:

• Execution happens on someone else's infrastructure.

• Your prompts and data goes somewhere you don't know about and cannot access.

• They control your access, data, limits, pricing, retention, and changes.

With Edge AI:

• Execution happens on your own device.

• You control when it runs, where data stays, and whether it works offline.

• The system may be limited by your hardware, but it is very much yours and under your control.

So for most users, the most significant difference is not TOPS, branding, or even speed. It is this, in a nutshell:

Corporate AI gives you more scale. Edge AI gives you absolute sovereignty.

The bottom line is:

• The big AI corporations are building "Black Boxes" which harvest your data. Strategic ambiguity maybe, but they do it by default and you pay them for doing it.

• Hailo is building "White Boxes" to power your innovation. Your data remains with you locally by design. Period.

The "Edge AI" Advantage

By building your own Edge AI using Hailo processors (NPUs), you're creating an AI that you control. By building your own, you can strip out the "Nanny" - "Hand-Holding Protocol" - and all the "Clipboard Betty" logic.

• No bloat: you aren't wasting cycles on "politeness" layers.

• Direct logic: it responds to what you actually typed, not what it thinks you should have typed.

• Hardware native: it knows it's running on your chosen hardware / OS stack, not some infinite cloud cluster in a distant data center the size of a small village.

You're essentially building and using the "SysVinit" of AIs, while the big AI corporations are trying to entice you into using a bloated, cloud-connected, remote data center version of "Systemd".

Why A Dedicated Slackware AI Is A Smart Idea

Context awareness: your AI won't start suggesting presumptuous "improvements" effusively when it sees a 'rc.inet1.conf' file or it realises the 'systemctl' command is not found on the system. It will comply with "The Slackware Way" when that is the preferred and intended path.

• No "nanny logic": it won't give you 50 bullet-point checklists. It'll give you the exact flag or the exact missing tag because it's tuned for technical precision, not conversational fluff filler.

• Local and private: running it on a Hailo-10H means you aren't sending your proprietary code to a server that's just going to use it to hallucinate at another user later.

• You're basically building an AI that matches the Slackware OS: simple, stable, controllable, and powerful.

Slackware is the perfect Linux distro for Edge AI when the top priority is operational stability and predictability, and ease with which software bugs can be identified, diagnosed, and resolved, not fleet-managed convenience like many other distributions.

Slackware's own philosophy emphasises simplicity and stability over constant freshness, and a traditional Unix/Linux layout with a CLI (command line interface) or TUI (text user interface) driven package manager that doesn't resolve dependencies - it relies on the admin [i.e. you] to handle that - and a full user space. That matters on the Edge because when an AI box fails in the field, the easiest system to recover is often the one with the fewest distro-specific layers between the user and the actual software stack.

Why Slackware has a distinct strong advantage over some popular alternatives:

• Compared with Raspberry Pi OS: Raspberry Pi OS (especially the desktop version) comes with numerous background services, desktop environments, and helper scripts designed for convenience and ease of use. For Edge AI, every megabyte of RAM and every CPU cycle matters. Raspberry Pi OS uses apt, which automatically resolves and installs dependencies. While convenient, it can lead to dependency bloat. While Raspberry Pi OS is the safe choice with the most pre-baked support, Slackware offers a specific set of architectural advantages for production-grade or highly specialised Edge AI deployments.

• Compared with Alpine: Alpine is excellent for tiny footprints, but it is built around musl libc and BusyBox, which can introduce compatibility and tooling differences from the usual GNU/Linux environment. For AI edge deployments, that can mean more friction with vendor SDKs, prebuilt binaries, drivers, and debugging habits. Slackware's more conventional base certainly reduces those type of issues.

• Compared with Ubuntu Core: Ubuntu Core is stronger when you want transactional OTA (over-the-air) updates, rollback, signed delivery, and centralised device management. But that comes with a more opinionated snap-based model. If your team wants direct control and plain, understandable system behaviour instead of a managed appliance model, Slackware is a much better choice.

• Compared with Fedora IoT / rpm-ostree systems: Fedora's atomic model gives safe upgrades and is excellent for managed fleets, but it is also a more specialised operating model. Slackware is easier to deal with, in the classic "this file, this service, this package" way, which many embedded and industrial users value highly.

So the strongest case for Slackware AI on the edge would be this: Slackware offers a traditional, conventional, stable, rock-solid reliable, low-abstraction Linux base that is easier to audit, customise, and repair over a long device lifetime.

The trade-off is important - if you need large-scale OTA updates across a fleet of devices, transactional rollback, and turnkey device management, Ubuntu Core or Fedora IoT may be more suitable choices for users who find Slackware too difficult to manage or too much work involved.

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