Raspberry Pi AI HAT 2: Competing with Apple M4 Performance

Introduction

The Raspberry Pi AI HAT Plus 2 represents the next generation of edge AI acceleration, bringing enterprise-level artificial intelligence capabilities to the maker community. From KevsRobots, this comprehensive review examines Raspberry Pi’s latest AI accelerator add-on board, designed to run large language models (LLMs) and large vision models (LVMs) entirely locally. Unlike cloud-dependent AI solutions, the AI HAT 2 enables users to break free from subscription tethers while maintaining full control over their data and processing power.

As noted in Glasp’s research on local AI trends, the shift toward local AI processing represents “enhanced privacy and reduced latency, as data no longer needs to be transmitted to remote servers for processing.” The AI HAT 2 exemplifies this growing movement toward data sovereignty and edge computing. Watch the Video.

All about Raspberry Pi AI HAT Plus 2

What is the Raspberry Pi AI HAT Plus 2?

The AI HAT Plus 2 is Raspberry Pi’s second-generation AI accelerator add-on board that transforms your Raspberry Pi into a powerful edge AI computing device. Unlike its predecessor, this version can run Large Language Models (LLMs) and Large Vision Models (LVMs) entirely locally, without requiring cloud connectivity or subscriptions.

Key Concept: Local Generative AI

The AI HAT Plus 2 enables what’s called “local generative AI” – meaning all AI processing happens on your device rather than in the cloud. This provides:

• Complete data privacy – your information never leaves your device
• No internet dependency – works offline
• Zero latency from network delays
• Freedom from main CPU/RAM – dedicated AI processing

As Glasp research highlights, this shift toward local AI processing represents a major trend: “users can experience enhanced privacy and reduced latency, as data no longer needs to be transmitted to remote servers for processing.”

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Technical Specifications

Hardware Details

Specification	Details
Processing Power	40 TOPS (INT4 inference)
Dedicated RAM	8GB onboard for AI models
Processor	Hailo NPU (Neural Processing Unit)
Interface	PCIe Gen 3
Power Consumption	+2W under load (idle baseline)
Comparison	Outperforms Apple M4 (38 TOPS)
Vision Performance	Equivalent to 26× original AI HAT Plus

What is a “TOP”?

TOPS = Trillion Operations Per Second – a measure of AI processing performance. The AI HAT 2’s 40 TOPS means it can perform 40 trillion mathematical operations every second when processing AI models.

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How It Works: Architecture & Design

Independent Processing Architecture

The AI HAT Plus 2’s revolutionary design uses a completely independent processing architecture:

┌─────────────────────────────────────┐
│     Raspberry Pi Main Board         │
│  • Runs OS & Applications           │
│  • CPU stays ~0% during AI tasks    │
│  • RAM free for other work          │
│  • 2GB+ RAM sufficient              │
└──────────────┬──────────────────────┘
               │ PCIe Gen 3
               ↓
┌─────────────────────────────────────┐
│       AI HAT Plus 2                 │
│  • 8GB dedicated RAM                │
│  • Hailo NPU processor              │
│  • Handles all AI computation       │
│  • Model storage & execution        │
└─────────────────────────────────────┘

Key Advantage: Zero Main System Impact

During testing in the video, the reviewer demonstrated:

• 2GB RAM Raspberry Pi running smoothly
• CPU usage: ~0% during LLM inference
• Memory usage: Minimal on main system
• All processing isolated to the HAT

This means your Raspberry Pi remains free to run web servers, home automation, data logging, or any other tasks simultaneously with AI processing.

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Large Language Model Capabilities

Supported LLM Framework: Ollama

The AI HAT 2 uses a specially optimized version of Ollama (called Hailo Ollama) designed specifically for the Hailo processor. According to Glasp’s guide on Ollama, this framework provides “a range of pre-trained language models” with straightforward installation and management.

Pre-loaded Models

The system ships with several production-ready models:

Model	Size	Best For	Speed
DeepSeek R1 distilled Qwen	1.5B parameters	Detailed analysis, code review	Fast
Llama 3.2	3B parameters	Deep understanding, complex tasks	Medium
Qwen 2.5 Instruct	Varies	General instruction following	Fast
Arena	–	Model comparison	–

Real-World Performance Testing

Code Generation Test

Task: “Write me a Python FastAPI app that can store birthdays of friends in a database”

Results:

• ✅ Generated complete, working FastAPI application
• ✅ Created proper database models (Birthday class)
• ✅ Set up correct routing (POST endpoints)
• ✅ Real-time text generation visible
• ⏱️ Response time: Near-instantaneous once model loaded

Documentation Generation

Task: “Write a README file for this code”

Results:

• ✅ Comprehensive setup instructions
• ✅ Database configuration details
• ✅ Dependencies list
• ✅ Run commands and boilerplate
• ⏱️ Speed: Comparable to code generation

Code Review & Analysis

Task: “Review this code for any issues and suggest improvements”

DeepSeek Model Performance:

• ✅ Extremely detailed explanations
• ✅ Identified incomplete implementations
• ✅ Suggested practical improvements
• ✅ Impressive depth of analysis
• 💡 Speed: Quick text generation

Llama 3.2 (3B) Performance:

• ✅ Even deeper understanding
• ✅ More comprehensive analysis
• ⚠️ Noticeably slower (model is 2× larger)
• 📝 Reviewer note: “Would use a different model for coding due to speed”

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Vision AI Capabilities

Large Vision Model (VLM) Demonstrations

The AI HAT 2 includes powerful vision capabilities through its VLM Chat application, which analyzes live video or images in real-time.

Live Image Recognition Test

Setup: Reviewer held up a toy object in front of camera

AI Response:

“The image shows a person in a blue t-shirt with a design on a hat that includes a castle and a dragon. The individual is holding a toy that resembles a dragon. The background appears to be an indoor setting, possibly a workshop or a room with shelves and storage containers. The person is smiling. The overall look suggests a playful or creative atmosphere.”

Accuracy Assessment:

• ✅ Correctly identified person
• ✅ Accurately described clothing
• ✅ Recognized workshop environment
• ✅ Detected emotional state (smiling)
• ✅ Understood creative/playful context
• ⚠️ Minor hallucination (saw “castle” – actually birds on shirt)

Interactive Follow-up Questions

Question 1: “How many people are in the picture?”

• Response: “There’s one person in the picture”
• ⏱️ Speed: “Almost instantaneous”

Question 2: “Is the person happy or sad?”

• Response: “The person appears to be neutral or slightly sad as indicated by their expression of body language”

Additional Recognition: The AI spontaneously identified:

• “A small circuit board-like device that appears to be a Raspberry Pi”
• Explained: “The Raspberry Pi is a micro computer that is often used for home automation, robotics, or low-cost computing”
• Context: “The image appears to represent a humorous or creative scene with the individual engaged in a DIY project that involves Raspberry Pi”

Reviewer Reaction: “That’s pretty much spot on, isn’t it?”

Traditional Computer Vision Features

Beyond LVMs, the AI HAT 2 maintains full support for:

• Object Detection – Identifying objects in images/video
• Image Segmentation – Separating image into distinct regions
• Pose Estimation – Detecting human body positions
• Depth Perception – Understanding 3D spatial relationships (reviewer’s favorite)

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Setup & Installation

Hardware Requirements

Compatible Raspberry Pi Models:

• Raspberry Pi 5 (recommended)
• Raspberry Pi Compute Modules
• Any Raspberry Pi with PCIe interface support

Minimum System Requirements:

• As demonstrated: 2GB RAM is sufficient
• SD card for OS storage
• Power supply adequate for +2W additional load

Software Installation

According to the video:

1. Boot Raspberry Pi with latest OS
2. Install Hailo Ollama (special optimized version)
3. Model installation is straightforward
4. Automatic detection – Latest Raspberry Pi OS auto-detects the AI HAT Plus 2’s NPU
5. Camera support – Built-in RPi Cam apps work out of the box

Note: The reviewer was testing beta software, so couldn’t show installation steps, but confirmed: “It’ll be very straightforward once the board comes out.”

Demo Applications

The AI HAT 2 includes several demo applications:

• VLM Chat – Live image description and analysis
• Hailo Ollama interface – LLM interaction
• Vision demos – Object detection, segmentation, etc.
• Custom application support

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Model Management & Switching

Model Loading Process

When switching between AI models:

1. Unload current model from 8GB RAM
2. Load new model from storage (SD card)
3. Ready for inference

Loading Times:

• Small models (1-2B): ~10-15 seconds
• Large models (3B+): ~20-30 seconds
• Storage bottleneck: SD card is the limiting factor

Storage Limitation

⚠️ Important Constraint: Because the AI HAT 2 uses the PCIe Gen 3 interface, you cannot store the Raspberry Pi OS on an NVMe drive. You must use:

• SD card (standard option, slower)
• eMMC (Compute Module only, faster than SD)
• Cannot use: NVMe SSD for boot drive

Impact: Slower model loading times compared to SSD storage

Workaround Suggestions:

• Use Compute Module with eMMC
• Pre-load commonly used models
• Minimize model switching in workflows

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Power Consumption & Efficiency

Power Usage Testing

The reviewer conducted careful power measurements:

State	Power Draw
Idle	Baseline consumption
Under Load	Baseline + 2W
Difference	Only 2W increase

Efficiency Assessment: “Pretty efficient for an AI hat”

Why This Matters

For always-on applications like:

• Home automation systems
• Security camera analysis
• Voice assistants
• Network services

A mere 2W increase makes the AI HAT 2 extremely practical for 24/7 operation.

Cost Impact:

• 2W × 24 hours = 48Wh per day
• 48Wh × 365 days = 17.5 kWh per year
• At $0.15/kWh ≈ $2.60 per year in electricity

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Practical Use Cases

1. Home Automation Integration

Example: N8N Workflow Automation

The reviewer specifically mentions: “I’ll be building this into some projects such as N8N for home automation”

Possibilities:

• Voice-controlled home devices
• Smart camera analysis (detect packages, people, pets)
• Natural language control interfaces
• Automated task generation from conversations
• Local voice assistant (no cloud required)

2. Development & Coding Assistant

Demonstrated Capabilities:

• Real-time code generation
• Documentation creation
• Code review and improvement suggestions
• Multi-language support
• Debugging assistance

Workflow Example:

Developer → Asks for FastAPI code
         ↓
AI HAT 2 → Generates complete application
         ↓
Developer → Requests code review
         ↓
AI HAT 2 → Provides detailed analysis
         ↓
Result → Production-ready code + documentation

3. Computer Vision Projects

Applications:

• Security systems with intelligent alerts
• Wildlife camera analysis
• Quality control in manufacturing
• Accessibility tools (image description for visually impaired)
• Augmented reality projects

4. Privacy-Sensitive Applications

Ideal for:

• Medical image analysis (HIPAA compliance)
• Legal document review
• Financial analysis
• Personal journaling with AI assistance
• Any scenario where data cannot leave premises

5. Educational Projects

Learning Opportunities:

• AI/ML education without cloud costs
• Robotics with natural language control
• Research projects with data sovereignty
• Student projects with predictable costs
• Teaching responsible AI use

6. Edge Computing Deployments

Scenarios:

• Remote locations with limited internet
• IoT device intelligence
• Real-time processing requirements
• Bandwidth-constrained environments
• Offline-first applications

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AI Philosophy: Responsible Use

The “AI Slop” Problem

The reviewer dedicates significant time to discussing responsible AI usage, addressing what he calls “AI slop” – low-quality AI-generated content flooding the internet.

Core Principle

“AI is a tool like a pen or a paintbrush. How you use it decides whether you create a fine art masterpiece, a cartoon, or a doodle. It’s up to you.”

Root Causes of Low-Quality AI Content

1. Corporate Pressure

• Companies rushing to add AI features
• “First to market” mentality
• Keeping up with competition
• Mandatory AI inclusion

2. Low-Effort Users

• Creating content with minimal effort
• Expecting value without investment
• No tailoring for audience
• No intent or craft in output

Fundamental Law:

“AI will help you generate things with low effort, but the value from that will probably be equally low.”

Appropriate LLM Use Cases

✅ Where LLMs Excel:

1. Summarizing large or complex texts
2. Brainstorming ideas and concepts
3. Providing structure for initial frameworks
4. Grammar/spelling – “like a spelling check on steroids”

✅ Where Vision Models Excel:

1. Image descriptions – adding alt text at scale
2. Visualization from descriptions
3. Pre-visualization for creative projects
4. Reducing tedious annotation work

Raspberry Pi’s Responsible Approach

Why AI HAT 2 Is Different:

1. Optional & Modular

• Not mandatory – user choice
• Add-on design philosophy
• No forced AI features

2. Local & Private

• No cloud dependency
• No subscription requirement
• Complete data ownership

3. Resource Independent

• Doesn’t consume main system resources
• Frees up other machines
• Dedicated AI processing

Cost Comparison:

• AI HAT 2: £130 one-time
• Cloud AI (reviewer’s example): £90/month for Claude AI Max
• Break-even: ~1.5 months
• After break-even: Pure savings forever

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Pricing & Value Proposition

Cost Analysis

Initial Investment: £130 / $130 (one-time purchase)

Compare to Cloud Subscriptions:

Service	Monthly Cost	Annual Cost	2-Year Cost
AI HAT 2	£0 (after purchase)	£0 (after purchase)	£130 total
ChatGPT Plus	~$20	$240	$480
Claude Pro/Max	~£90	£1,080	£2,160
GitHub Copilot	~$10	$120	$240

Break-Even Timeline:

• vs. ChatGPT Plus: ~6.5 months
• vs. Claude Max: ~1.5 months
• vs. Multiple services: Even faster

Value Considerations

✅ One-Time Purchase Benefits:

• No recurring fees
• Unlimited inference
• No per-token charges
• No usage limits
• No feature gating
• Privacy benefits (priceless)

📊 Total Cost of Ownership (5 years):

• AI HAT 2: £130
• Cloud AI subscription: £5,400+ (at £90/month)
• Savings: £5,270+

🏠 Home Lab Value: The reviewer states: “If you have a home lab, I would say this is actually an essential.”

Reasons:

• Dedicated AI machine frees other computers
• Local processing for all projects
• No internet dependency
• Privacy for sensitive work

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Advantages & Benefits

✅ Major Advantages

1. True Data Privacy

• All processing happens locally
• No data transmitted to cloud
• GDPR/HIPAA compliant architecture
• Complete data sovereignty

2. Performance Leadership

• 40 TOPS outperforms Apple M4 (38 TOPS)
• Faster than many laptops
• Dedicated AI processing
• Consistent performance (no cloud throttling)

3. Zero Main System Impact

• CPU stays at ~0% during AI tasks
• RAM remains available
• Works with minimal Raspberry Pi (2GB RAM)
• True parallel processing

4. Cost-Effective Long-Term

• One-time purchase
• No subscriptions
• No hidden fees
• No usage limits

5. Offline Capability

• Works without internet
• No cloud downtime issues
• Consistent availability
• Remote location support

6. Modular Design

• Optional add-on
• User choice
• Easy to upgrade
• Standard form factor

7. Energy Efficient

• Only +2W under load
• Suitable for 24/7 operation
• Low operating costs
• Environmentally friendly

8. Integration-Friendly

• Works with N8N
• Standard Ollama interface
• Python library support
• Open ecosystem

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Limitations & Challenges

⚠️ Key Limitations

1. Initial Cost Barrier

• £130 upfront may be steep for hobbyists
• More expensive than SD card or case
• Requires budget planning
• Not suitable for casual experimentation

2. Storage Performance Constraints

• Cannot use NVMe for OS (PCIe occupied)
• SD card bottleneck for model loading
• 10-30 second model switching delays
• eMMC option only for Compute Modules

3. Technical Complexity

• Model conversion requires knowledge
• Limited official examples
• Learning curve for optimization
• Not as simple as cloud services

4. Model Switching Overhead

• Cold start delays when changing models
• Workflow interruptions
• Planning required for model selection
• Storage speed dependent

5. Model Size Limitations

• 8GB RAM constrains largest models
• Cannot run biggest LLMs
• Trade-offs between model size and capability
• Quantization may be required

6. Limited Documentation

• Beta software during review
• Examples “a little bit limited”
• Community still developing
• Fewer tutorials than cloud platforms

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Who Should Buy?

✅ Strongly Recommended For:

1. Home Lab Enthusiasts

• Reviewer quote: “If you have a home lab, I would say this is actually an essential”
• Building comprehensive home infrastructure
• Multiple AI-integrated projects
• Technical experimentation

2. Privacy-Conscious Users

• Sensitive data processing requirements
• GDPR/HIPAA compliance needs
• No trust in cloud providers
• Data sovereignty requirements

3. Current Cloud AI Subscribers

• Paying £50+ monthly for AI services
• High usage patterns
• Break-even in 2-3 months
• Long-term cost savings

4. Developers & Engineers

• Local code generation and review
• Offline development environments
• Custom AI application development
• Learning AI/ML implementation

5. Home Automation Builders

• Integrating with N8N
• Smart home projects
• Voice control systems
• Security camera analysis

6. Educators & Students

• Teaching AI/ML concepts
• Student projects with fixed costs
• Research without cloud expenses
• Hands-on learning

7. Makers & Robotics

• Embedded AI for robots
• IoT intelligence
• Real-time processing needs
• Prototype development

❌ Consider Alternatives If:

1. Budget is Primary Constraint

• £130 upfront is prohibitive
• Need lowest possible entry cost
• Uncertain about AI usage

Alternative: Start with cloud free tiers

2. Minimal AI Usage

• Occasional queries only
• Don’t need dedicated hardware
• Pay-per-use more economical

Alternative: ChatGPT free tier or pay-as-you-go

3. Need Latest/Largest Models

• Require GPT-4 level capability
• Need models >8GB
• State-of-the-art is essential

Alternative: Cloud services (for now)

4. Non-Technical User

• Uncomfortable with Linux
• No interest in configuration
• Want plug-and-play experience

Alternative: Cloud-based AI services

5. No Raspberry Pi Ecosystem

• Don’t own Raspberry Pi 5
• Not planning other Pi projects
• Need standalone solution

Alternative: Consider other local AI solutions

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Comparison with Alternatives

vs. Cloud AI Services

Feature	AI HAT 2	Cloud AI
Privacy	Complete	Limited
Cost (1 year)	£130	£240-£1,080+
Internet Required	No	Yes
Latency	Minimal	Variable
Model Size	Up to 8GB	Unlimited
Setup	Technical	Simple

vs. Apple M4 Mac

Feature	AI HAT 2	M4 Mac
AI Performance	40 TOPS	38 TOPS
Price	£130	£1,000+
Dedicated AI	Yes	No
Power Draw	+2W	20-50W system
Form Factor	Add-on board	Complete computer

vs. NVIDIA Jetson

Feature	AI HAT 2	Jetson Orin Nano
Price	£130	$499+
Ecosystem	Raspberry Pi	NVIDIA
Software	Ollama	Full CUDA stack
Power	+2W	5-15W
Learning Curve	Moderate	Steep

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Future Projects & Integration

Reviewer’s Planned Implementations

1. N8N Integration

• Workflow automation
• Home automation triggers
• AI-enhanced task automation
• Voice command processing

2. Home Lab Infrastructure

• Central AI processing hub
• Multi-device support
• Shared resource for all projects
• Local AI API server

3. Various Maker Projects

• Robotics with natural language
• Smart camera systems
• Voice-controlled devices
• Custom AI applications

Community Use Cases

Based on the technology and local AI trends from Glasp:

Privacy-First Applications:

• Medical transcription without cloud
• Legal document analysis
• Financial advisory tools
• Personal journaling with AI

Edge Computing:

• Remote monitoring systems
• Offline-first applications
• IoT intelligence
• Real-time processing

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Tips for Optimization

Maximizing Performance

1. Model Selection Strategy

• Use smaller models (1.5B) for speed
• Reserve larger models (3B+) for complex tasks
• Pre-plan model sequences to minimize switching
• Cache frequently used models

2. Storage Optimization

• Consider Compute Module for eMMC
• Keep models on fastest available storage
• Minimize unnecessary model downloads
• Regular cleanup of unused models

3. Workflow Design

• Batch similar tasks together
• Avoid frequent model switching
• Use appropriate model for each task type
• Plan multi-step processes in advance

4. System Configuration

• Ensure adequate cooling
• Stable power supply
• Latest Raspberry Pi OS
• Regular software updates

Integration Best Practices

1. API Design

• Create wrapper APIs for common tasks
• Cache model outputs when possible
• Implement request queuing
• Monitor resource usage

2. Application Architecture

• Separate AI processing from main app
• Use async processing where possible
• Implement proper error handling
• Log performance metrics

3. Security Considerations

• Local network only (if privacy-critical)
• Implement authentication for remote access
• Regular security updates
• Monitor for unusual activity

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Video about Raspberry Pi AI HAT+2:

Conclusion & Final Thoughts

The Raspberry Pi AI HAT Plus 2 represents a significant advancement in accessible edge AI computing. By delivering 40 TOPS of processing power—exceeding even Apple’s M4—in a modular, privacy-respecting package, Raspberry Pi has created a compelling solution for users seeking independence from cloud AI services.

Reviewer’s Verdict

“If you have a home lab, I would say this is actually an essential. I’ve got this installed now on my home lab, and I’ll be building this into some projects such as N8N for home automation.”

Core Strengths

1. Performance Leadership: Genuinely outperforms Apple M4 in INT4 inference
2. True Local AI: Complete offline operation with no cloud dependencies
3. Resource Efficiency: Dedicated processing eliminates system conflicts
4. Economic Value: One-time purchase beats ongoing subscriptions
5. Modular Design: Optional integration respects user choice

Critical Success Factors

• Proper model selection for use case (balance speed vs. capability)
• Understanding of local AI benefits vs. limitations
• Appropriate storage solutions to minimize loading delays
• Integration planning for home lab environments

Who Should Buy

Strongly Recommended For:

• Home lab operators seeking local AI capabilities
• Privacy-focused individuals and small businesses
• Developers building AI-integrated applications
• Educators teaching AI and machine learning concepts
• Anyone currently paying for cloud AI subscriptions

Consider Alternatives If:

• You need state-of-the-art models exclusively (cloud may be better)
• Budget is primary constraint (£130 upfront cost)
• You lack technical background for model management
• Your use case requires minimal AI usage (pay-per-use may be cheaper)

Key Takeaways

1. Performance is Real

• 40 TOPS genuinely outperforms Apple M4
• Fast enough for production use
• Dedicated processing is a game-changer

2. Privacy Matters

• Complete local control
• No cloud dependencies
• Data sovereignty achieved

3. Economics Favor Long-Term

• One-time £130 investment
• No recurring fees
• Beats subscriptions after 1-2 months

4. Technical but Manageable

• Some learning curve required
• Benefits justify the effort
• Community support growing

5. Ecosystem Integration

• Works well with Raspberry Pi ecosystem
• Standard Ollama interface
• Growing software support

The Bigger Picture

The AI HAT Plus 2 represents a significant milestone in the democratization of AI technology. As noted in Glasp’s research, “the shift towards local AI processing aligns with a growing consumer demand for privacy and data security.”

This product proves that:

• High-performance AI doesn’t require cloud
• Privacy and capability can coexist
• One-time purchases beat subscriptions
• Edge computing is ready for mainstream

Final Recommendation

For home lab enthusiasts, privacy-conscious users, and anyone paying for cloud AI subscriptions: The AI HAT Plus 2 is an essential purchase that pays for itself within months while providing complete control over your AI infrastructure.

For casual users or those needing the absolute latest models: Cloud services may still be more appropriate, but keep watching this space as local AI rapidly improves.

Related References

Product Information

• Manufacturer: Raspberry Pi Foundation
• Product: AI HAT Plus 2
• Price: £130 / $130
• Processor: Hailo NPU
• Interface: PCIe Gen 3
• Memory: 8GB dedicated RAM

Software & Tools Mentioned

• Ollama: LLM runtime optimized for Hailo
• DeepSeek R1: 1.5B parameter model
• Llama 3.2: 3B parameter model
• Qwen 2.5: Instruct-tuned model
• N8N: Workflow automation platform
• Hailo SDK: Development tools for AI HAT 2

Technical Specifications

• Performance: 40 TOPS INT4 inference
• Vision Performance: Equivalent to 26× original AI HAT
• Power Consumption: +2W under load
• Comparison: Exceeds Apple M4 (38 TOPS)

Related Glasp Resources

• Local AI Processing and Privacy Benefits
• Ollama Local LLM Guide
• Self-Hosted LLM Comprehensive Guide
• Raspberry Pi Hardware Reviews