The Role of Edge Computing in Building a Carbon-Neutral AI Future

Artificial Intelligence (AI) is advancing faster than ever, but the price we pay in energy consumption is becoming impossible to ignore. Most AI workloads today rely on massive data centers that consume gigawatts of electricity and release enormous amounts of CO₂. To achieve a truly carbon-neutral AI future, we need smarter solutions and edge computing is leading the way.

Instead of sending all data to the cloud, edge devices like ESP32 microcontrollers, Raspberry Pi Zero, and NVIDIA Jetson Nano process AI tasks locally. These devices use far less power, require minimal cooling, and can even run on renewable energy sources. This shift is not just technical it’s environmental.

Why Edge Computing Powers Green AI

Processing data locally means fewer transmissions, lower bandwidth use, and drastically reduced energy consumption. When combined with renewable energy sources, edge computing creates a carbon-light AI ecosystem.

• Energy Efficiency: Runs AI models on milliwatts instead of kilowatts.
• Lower Carbon Footprint: Cuts reliance on high-emission data centers.
• Reduced E-Waste: Supports longer hardware lifespans.
• Scalability: Can be deployed everywhere—from remote farms to urban grids.

Real Edge Devices Driving Sustainability

ESP32 – The Low-Power IoT AI Enabler

• Use Case: Smart irrigation systems analyze soil and weather locally, activating pumps only when needed.
• Impact: Up to 25% water savings and minimal energy use.

Raspberry Pi Zero 2 W – Affordable AI at the Edge

• Use Case: Home energy management systems predict consumption and optimize appliance usage.
• Impact: Reduced household energy waste, contributing to lower emissions.

NVIDIA Jetson Nano – AI Power for Industrial Efficiency

• Use Case: Real-time defect detection in factories without cloud processing.
• Impact: Avoids production errors, reduces waste, and cuts energy losses.

Arduino Portenta H7 – Sustainable Industrial IoT

• Use Case: Water flow monitoring for irrigation and industry, processed directly on the device.
• Impact: Conserves water while minimizing network and power consumption.

Practical AI Models That Support These Devices

These edge devices rely on optimized AI models that balance performance with power efficiency. Here are real-world models that make edge AI sustainable:

1. MobileNet (TensorFlow Lite)

• Optimized For: Low-power image classification on ESP32 and Raspberry Pi.
• Example: Used in smart cameras to detect plant diseases in fields without needing cloud support.

2. YOLOv5 Nano

• Optimized For: Object detection on Jetson Nano and Raspberry Pi.
• Example: AI-enabled cameras for waste sorting, improving recycling rates while saving energy.

3. TinyML Anomaly Detection Models

• Optimized For: Real-time industrial monitoring on microcontrollers.
• Example: Vibration sensors using TinyML detect machinery faults early, preventing energy waste from breakdowns.

4. SensiML Gesture Recognition

• Optimized For: ESP32 and Arduino Portenta for local ML processing.
• Example: Smart wearable devices for energy-efficient gesture control in smart homes.

5. Edge Impulse Environmental Monitoring Models

• Optimized For: ESP32, Raspberry Pi, and Arduino boards.
• Example: Tiny ML models track air quality, helping cities optimize pollution control without massive cloud data.

Edge Computing + Renewable Energy = Carbon-Neutral AI

Pairing these devices with solar panels or other renewable energy solutions creates an ecosystem where AI runs with almost zero emissions. Imagine solar-powered AI irrigation, wind-powered edge sensors for smart grids, or battery-efficient wildlife tracking cameras—all contributing to sustainability without burdening the planet.

Why This Approach Works

Unlike traditional AI systems that require huge centralized resources, edge computing keeps computation close to the source, minimizing energy and emissions. When scaled globally, this could cut AI’s carbon footprint dramatically while making AI accessible to communities everywhere.

Edge devices like ESP32, Raspberry Pi Zero, and Jetson Nano show us that we don’t need to sacrifice the planet for progress. When combined with efficient AI models and renewable power, these technologies can help us build a truly carbon-neutral AI future.

Real-World Edge AI Case Studies: Tiny Models Powering Green AI Applications

The combination of edge computing, TinyML, and optimized AI models is already delivering measurable benefits—energy savings, reduced emissions, and smarter automation. Here are five compelling examples that show how devices like ESP32, Raspberry Pi, Jetson Nano, and Arduino boards are driving sustainable AI in the field.

1. ESP32-CAM for Local Object Detection

Use Case: As described in Sensors (2025), an object‑detection model runs directly on an ESP32-CAM module, performing image classification locally over MQTT—for example, detecting people or objects in monitoring scenarios.
Impact: Compared to sending images to the cloud, this setup significantly reduces bandwidth, latency, and energy use—ideal for solar-powered, off-grid deployments.
(MDPI)

2. TinyML Soil Moisture Prediction for Smart Farming

Use Case: A TinyML pipeline on ESP32 predicts daily soil moisture locally using pruned/quantized models, enabling precise irrigation control without cloud reliance.
Impact: This edge-only approach lowers water usage and eliminates transmission energy, making micro-farming both efficient and sustainable.
(IET Research Journals)

3. Jetson Nano for Smart Recycling Bins

Use Case: Researchers built a smart recycling bin using YOLOv4/K210 deployed on Jetson Nano, classifying waste types with 95–96% accuracy while consuming just ~4.7 W.
Impact: Waste sorting efficiency rises, with low power consumption and reduced cloud dependency—helping cities optimize recycling programs.
(arXiv)

---

4. Leaf Disease Detection on Raspberry Pi

Use Case: In a thermal-imaging study, MobileNetV1/V2 and VGG‑based models were pruned and quantized to run on Raspberry Pi 4B, detecting leaf disease in real time for farmers.
Impact: On-device disease classification was up to 2× faster than GPU inference, with much lower energy use, making crop monitoring more accessible.
(arXiv)

5. Smart Voice Assistants with TinyML in Home Automation

Use Case: A Nature (2025) study showed that voice assistant models on low-power devices (ESPs, wearables, or microcontrollers) can interpret commands and adjust home systems—all without constant internet access.
Impact: This reduces cloud energy costs and supports privacy, while enabling assistive tech in off-grid or low-bandwidth areas.
(Nature)

Why These Case Studies Show Green AI in Action

Feature	What It Delivers
Local Inference	Reduces need for cloud uploads and data transfers
Low Power Consumption	Uses watts or milliwatts, not kilowatts
Efficient Models	Uses pruning, quantization, TinyML for edge viability
Real-World Accuracy	Models maintain 80–96% accuracy, suitable for tasks
Sustainable Deployment	Compatible with solar or battery-powered setups

These real deployments prove that meaningful Green AI doesn’t need mega‑data centers—it can happen on tiny chips. From smart recycling in cities to sustainable farming systems and safe voice assistants, edge devices enable AI that respects planet and people. Their low energy demand, combined with optimized models, unlock sustainable AI adoption across remote, rural, and resource-constrained environments.

 Bibliography

• Edge Impulse. (2024). TinyML for IoT and Edge Devices. Retrieved from https://www.edgeimpulse.com
• Raspberry Pi Foundation. (2025). Raspberry Pi Zero 2 W Applications in AI and IoT. Retrieved from https://www.raspberrypi.com
• NVIDIA. (2025). Jetson Nano for Edge AI. Retrieved from https://developer.nvidia.com/embedded/jetson-nano
• Arduino. (2025). Portenta H7: Low Power AI for Industry 4.0. Retrieved from https://www.arduino.cc/pro
• International Energy Agency. (2025). AI and the Energy Transition. Retrieved from https://www.iea.org
• Chang, Y.-H., Wu, F.-C., & Lin, H.-W. (2025). Design and Implementation of ESP32-Based Edge Computing for Object Detection. Sensors, 25(6), 1656.(MDPI)
• Anonymous. (2024). TinyML-based moisture prediction for micro-farming on edge devices.(arXiv)
• Li, X., & Grammenos, R. (2022). Smart Recycling Bin Using Waste Image Classification at the Edge. arXiv.(arXiv)
• Silva, P. E. C. da, & Almeida, J. (2024). Leaf Disease Classification via Edge Computing and Thermal Imaging. arXiv.(arXiv)
• Chittepu, S., Martha, S., & Banik, D. (2025). Empowering Voice Assistants with TinyML for Real‑World Applications. Scientific Reports.(Nature)

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Run AI on ESP32: How to Deploy a Tiny LLM Using Arduino IDE & ESP-IDF (Step-by-Step Guide)

Introduction

What if I told you that your tiny ESP32 board the same one you use to blink LEDs or log sensor data could run a Language Model like a miniature version of ChatGPT? 

Sounds impossible, right? But it’s not.

Yes, you can run a Local Language Model (LLM) on a microcontroller!

Thanks to an amazing open-source project, you can now run a Tiny LLM (Language Learning Model) on an ESP32-S3 microcontroller. That means real AI inference text generation and storytelling running directly on a chip that costs less than a cup of coffee 

In this blog, I’ll show you how to make that magic happen using both the Arduino IDE (for quick prototyping) and ESP-IDF (for full control and performance). Whether you’re an embedded tinkerer, a hobbyist, or just curious about what’s next in edge AI this is for you.

Ready to bring AI to the edge? Let’s dive in!  

In this blog, you'll learn two ways to run a small LLM on ESP32:

1. Using Arduino IDE
2. Using ESP-IDF (Espressif’s official SDK)

Understanding the ESP32-S3 Architecture and Pinout

The ESP32-S3 is a powerful dual-core microcontroller from Espressif, designed for AIoT and edge computing applications. At its heart lies the Xtensa® LX7 dual-core processor running up to 240 MHz, backed by ample on-chip SRAM, cache, and support for external PSRAM—making it uniquely capable of running lightweight AI models like Tiny LLMs. It features integrated Wi-Fi and Bluetooth Low Energy (BLE) radios, multiple I/O peripherals (SPI, I2C, UART, I2S), and even native USB OTG support. The development board includes essential components such as a USB-to-UART bridge, 3.3V LDO regulator, RGB LED, and accessible GPIO pin headers. With buttons for boot and reset, and dual USB ports, the ESP32-S3 board makes flashing firmware and experimenting with peripherals effortless. Its advanced security features like secure boot, flash encryption, and cryptographic accelerators also ensure your edge AI applications stay safe and reliable. All of these capabilities together make the ESP32-S3 a perfect platform to explore and deploy tiny LLMs in real-time, even without the cloud.

What Is This Tiny LLM?

• Based on the llama2.c model (a minimal C-based transformer).
• Trained on TinyStories dataset (child-level English content).
• Supports basic token generation at ~19 tokens/sec.
• Model Size: ~1MB (fits in ESP32-S3 with 2MB PSRAM).

What You Need?

Item	Details
Board	ESP32-S3 with PSRAM (e.g., ESP32-S3FH4R2)
Toolchain	Arduino IDE or ESP-IDF
Model	tinyllama.bin (260K parameters)
Cable	USB-C or micro-USB for flashing

Method 1: Using Arduino IDE

Step 1: Install Arduino Core for ESP32

• Open Arduino IDE.

• Go to Preferences > Additional Board URLs

Add:

https://raw.githubusercontent.com/espressif/arduino-esp32/gh-pages/package_esp32_index.json

• Go to Board Manager, search and install ESP32 by Espressif.

Step 2: Download the Code

The current project is in ESP-IDF format. For Arduino IDE, you can adapt it or wait for an Arduino port (coming soon). Meanwhile, here's a simple structure.

• Create a new sketch: esp32_llm_arduino.ino
• Add this example logic:

#include <Arduino.h>
#include "tinyllama.h" // Assume converted C array of model weights

void setup() {
  Serial.begin(115200);
  delay(1000);
  Serial.println("Starting Tiny LLM...");
  // Initialize model
  llama_init();
}

void loop() {
  String prompt = "Once upon a time";
  String result = llama_generate(prompt.c_str(), 100);
  Serial.println(result);
  delay(10000); // Wait before next run
}

    

Note: You'll need to convert the model weights (tinyllama.bin) into a C header file or read from PSRAM/flash.

Step 3: Upload and Run

• Select your ESP32 board.
• Upload the code.
• Open Serial Monitor at 115200 baud.
• You’ll see the model generate a few simple tokens based on your prompt!

Method 2: Using ESP-IDF

Step 1: Install ESP-IDF

Follow the official guide: https://docs.espressif.com/projects/esp-idf/en/latest/esp32/get-started/

Step 2: Clone the Repo

git clone https://github.com/DaveBben/esp32-llm.git
cd esp32-llm

Step 3: Build the Project

idf.py set-target esp32s3
idf.py menuconfig  # Optional: Set serial port or PSRAM settings
idf.py build

Step 4: Flash to Board

idf.py -p /dev/ttyUSB0 flash
idf.py monitor

Output:

You’ll see generated text like:

Example Prompts and Outputs

Prompt: Once upon a time

→ Once upon a time there was a man who loved to build robots in his tiny shed.


Prompt: The sky turned orange and

→ The sky turned orange and the birds flew home to tell stories of the wind.

Prompt: In a small village, a girl

→ In a small village, a girl found a talking Cow who knew the future.

Prompt: He opened the old book and

→ He opened the old book and saw a map that led to a secret forest.

Prompt: Today is a good day to

→ Today is a good day to dance, to smile, and to chase butterflies.

Prompt: My robot friend told me

→ My robot friend told me that humans dream of stars and pancakes.

Prompt: The magic door appeared when

→ The magic door appeared when the moon touched the lake.

Prompt: Every night, the owl would

→ Every night, the owl would tell bedtime stories to the trees.

Prompt: Under the bed was

→ Under the bed was a box full of laughter and forgotten dreams.

Prompt: She looked into the mirror and

→ She looked into the mirror and saw a future full of colors and songs.

Tips to Improve

• Use ESP32-S3 with 2MB PSRAM.
• Enable dual-core execution.
• Use ESP-DSP for vector operations.
• Optimize model size using quantization (optional).

Demo Video

See it in action:
YouTube: Tiny LLM Running on ESP32-S3

 Why Would You Do This?

While it's not practical for production AI, it proves:

• AI inference can run on constrained hardware
• Great for education, demos, and edge experiments
• Future of embedded AI is exciting!

Link	Description
esp32-llm	Main GitHub repo
llama2.c	Original LLM C implementation
ESP-IDF	Official ESP32 SDK
TinyStories Dataset	Dataset used for training

Running an LLM on an ESP32-S3 is no longer a fantasy, it’s here. Whether you're an embedded dev, AI enthusiast, or maker, this project shows what happens when edge meets intelligence.

Bibliography / References

DaveBben / esp32-llm (GitHub Repository)
A working implementation of a Tiny LLM on ESP32-S3 with ESP-IDF
URL: https://github.com/DaveBben/esp32-llm
Karpathy / llama2.c (GitHub Repository)
A minimal, educational C implementation of LLaMA2-style transformers
URL: https://github.com/karpathy/llama2.c
TinyStories Dataset – HuggingFace
A synthetic dataset used to train small LLMs for children’s story generation
URL: https://huggingface.co/datasets/roneneldan/TinyStories
Espressif ESP-IDF Official Documentation
The official SDK and development guide for ESP32, ESP32-S2, ESP32-S3 and ESP32-C3
URL: https://docs.espressif.com/projects/esp-idf/en/latest/esp32/get-started/
Hackaday – Large Language Models on Small Computers
A blog exploring the feasibility and novelty of running LLMs on microcontrollers
URL: https://hackaday.com/2024/09/07/large-language-models-on-small-computers
YouTube – Running an LLM on ESP32 by DaveBben
A real-time demonstration of Tiny LLM inference running on the ESP32-S3 board
URL: https://www.youtube.com/watch?v=E6E_KrfyWFQ

Arduino ESP32 Board Support Package
Arduino core for ESP32 microcontrollers by Espressif
URL: https://github.com/espressif/arduino-esp32

Image Links:

https://www.elprocus.com/wp-content/uploads/ESP32-S3-Development-Board-Hardware.jpg

https://krishworkstech.com/wp-content/uploads/2024/11/Group-1000006441-1536x1156.jpg

https://www.electronics-lab.com/wp-content/uploads/2023/01/esp32-s3-block-diagram-1.png

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