AI Agents for EDGE AI

 

🌍 GenAI LLM-Based Agents on Edge AI: Why, When, and How?

🚀 Why Use GenAI LLMs on Edge AI?

Deploying Generative AI (GenAI) Large Language Models (LLMs) on Edge AI enables real-time, low-latency, and offline AI processing. Unlike cloud-based models, Edge AI provides:

✅ Low Latency: No network delays, instant responses.
✅ Privacy & Security: Data stays on the device, no external leaks.
✅ Offline Capability: Works without an internet connection.
✅ Reduced Costs: No cloud GPU costs, runs on local hardware.
✅ Energy Efficiency: Optimized execution for power-limited edge devices.

---

⏳ When to Use GenAI LLMs on Edge AI?

Use Edge AI for GenAI-based agents when:

🟢 Real-Time Decision-Making → Industrial IoT, Autonomous Vehicles.
🟢 Privacy-Critical Applications → Healthcare, Personal AI Assistants.
🟢 Offline Functionality Needed → Remote Locations, Defense, IoT Devices.
🟢 Cloud Costs are Too High → Local AI processing reduces dependency.

When NOT to use Edge AI:
🔴 If models require large GPUs (e.g., GPT-4, LLaMA-70B).
🔴 If real-time updates & cloud APIs are needed (e.g., SaaS AI apps).

---

⚙️ How to Deploy a GenAI LLM Agent on Edge AI?

1️⃣ Choose an Edge AI Device

• Jetson Orin/Nano (Best for CUDA & TensorRT) ✅
• Raspberry Pi 5 (Best for low-power LLMs) ✅
• Intel NPU (Neural Compute Stick 2) – OpenVINO Optimized ✅

---

2️⃣ Select a Lightweight LLM Model

🟢 Best Models for Edge AI:

• Phi-3 Mini (3.8B) – Best for small AI agents.
• Mistral-7B (4-bit Quantized) – Stronger AI reasoning.
• TinyLlama (1.1B) – Fastest for Raspberry Pi.
• LLaMA 2/3 (7B, Quantized) – Balanced size vs. accuracy.

📌 Use GGUF Quantization to optimize LLMs for Edge AI.

Yes, we can develop an LLM-based AI agent in Edge AI, but there are some challenges and optimizations required. Here’s how we can approach it:

---

1. Key Considerations for Edge AI with LLMs

• Hardware Constraints: Edge devices have limited compute power (CPU, GPU, RAM).
• Latency & Real-Time Processing: Edge AI should process requests quickly with minimal delay.
• Energy Efficiency: Edge devices may run on battery, requiring optimized models.
• Connectivity: Edge AI should work offline or with intermittent internet access.

---

2. Optimized LLM Models for Edge AI

We should use small and efficient models instead of large cloud-based ones. Some options:

• Phi-3 Mini (Microsoft) – Lightweight, optimized for edge.
• Gemma 2B (Google) – Efficient and runs on lower-end hardware.
• Mistral 7B (Quantized) – Can run on edge devices with optimizations.
• Llama 2 7B (4-bit Quantized) – Works with tools like GGUF, GPTQ.
• TinyLlama – Extremely small yet effective.

For very lightweight cases, consider distilled models like DistilBERT, MiniLM, or ALBERT.

---

3. Deployment Strategies

A. Running LLMs Directly on Edge

• Use ONNX Runtime or TensorRT for acceleration.
• Use quantization (4-bit, 8-bit) to reduce model size and computation.
• Frameworks:
  • GGUF (for quantized Llama models)
  • ONNX for model inference optimization
  • TensorFlow Lite (TFLite) for mobile & edge devices
  • TVM (Apache) for optimizing inference speed

B. Hybrid Edge-Cloud Approach

• Run a smaller model locally for basic queries.
• Offload complex queries to a cloud-based LLM when needed.
• Example: Use Phi-3 Mini on edge and call GPT-4 via API when required.

---

4. Hardware for Edge AI Agents

Depending on our use case, choose:

• Raspberry Pi 5 (for light AI tasks, use quantized models)
• NVIDIA Jetson Orin/Nano (best for AI/ML inference)
• Intel NPU or Myriad X (for optimized deep learning inference)
• Coral Edge TPU (Google) (for TFLite models)
• Apple M-Series chips (for ML acceleration on Mac/iOS)

---

5. Use Cases for Edge AI LLM Agents

• On-Device Chatbot (e.g., personal assistants, customer service bots)
• Autonomous Robots & Drones (real-time NLP processing)
• Healthcare AI Assistants (offline diagnosis/chat support)
• Industrial IoT (real-time monitoring with NLP)
• Smart Home Assistants (privacy-focused AI models)

---

6. Next Steps

• Choose an optimized model (Phi-3, TinyLlama, Mistral 7B quantized).
• Select a framework (GGUF, ONNX, TFLite).
• Deploy on an Edge AI device (Jetson, Raspberry Pi, NPU-based system).
• Optimize with quantization & acceleration (GPTQ, TVM, TensorRT).

Step-by-Step Guide to Deploy an LLM-Based AI Agent on Edge AI

We will set up a lightweight LLM (Phi-3 Mini, TinyLlama, or Mistral 7B quantized) on an Edge AI device (Jetson Orin, Raspberry Pi 5, or Intel NPU).

---

🛠 Step 1: Choose the Hardware

Based on our compute power and use case, select one:

• 🔹 Raspberry Pi 5 – Best for basic NLP tasks with a quantized model (TinyLlama, Phi-3).
• 🔹 NVIDIA Jetson Orin/Nano – For running LLMs with TensorRT acceleration.
• 🔹 Intel NPU (Neural Compute Stick 2) – If we need low-power deep learning inference.

For this guide, I’ll assume Raspberry Pi 5 (but can modify for Jetson/Intel if needed).

---

🛠 Step 2: Install Dependencies

1️⃣ Update & Install Required Packages

sudo apt update && sudo apt upgrade -y
sudo apt install -y python3-pip git wget curl

2️⃣ Install PyTorch (Optimized for Edge)

pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu

For Jetson, install the NVIDIA JetPack SDK.

3️⃣ Install Llama.cpp (for running quantized models)

git clone https://github.com/ggerganov/llama.cpp
cd llama.cpp
make

---

🛠 Step 3: Download a Lightweight LLM (Phi-3, TinyLlama, Mistral 7B Quantized)

🔹 For Phi-3 Mini (2.7B) or TinyLlama (1.1B)

wget -O model.gguf https://huggingface.co/microsoft/Phi-3-mini-4bit-GGUF/resolve/main/model.gguf

🔹 For Mistral 7B (Quantized, 4-bit GGUF)

wget -O model.gguf https://huggingface.co/TheBloke/Mistral-7B-GGUF/resolve/main/mistral-7b-instruct-v0.2.Q4_K_M.gguf

---

🛠 Step 4: Run the Model on the Edge Device

🔹 Run the model using llama.cpp for fast inference

./main -m model.gguf -p "Hello, how can I assist you?" -n 100

• -m model.gguf → Load the model
• -p "Hello..." → Prompt
• -n 100 → Limit response tokens

🔹 If using Jetson, optimize with TensorRT

pip install tensorrt
python3 run_llm.py --use-tensorrt

---

🛠 Step 5: Create an AI Agent API with FastAPI

If we want a REST API for our AI agent, use FastAPI.

1️⃣ Install FastAPI & Uvicorn

pip install fastapi uvicorn

2️⃣ Create app.py

from fastapi import FastAPI
import subprocess

app = FastAPI()

@app.get("/ask")
def ask(question: str):
    cmd = f'./main -m model.gguf -p "{question}" -n 100'
    response = subprocess.check_output(cmd, shell=True).decode()
    return {"response": response}

# Run with: uvicorn app:app --host 0.0.0.0 --port 8000

3️⃣ Run the API

uvicorn app:app --host 0.0.0.0 --port 8000

Now, test with:

curl "http://localhost:8000/ask?question=What%20is%20Edge%20AI?"

---

🛠 Step 6: Optimize Performance

1️⃣ Use GGUF 4-bit Quantization (Already Applied)

• Reduces memory usage from 16GB to ~4GB.
• Keeps acceptable performance on Raspberry Pi.

2️⃣ Use ONNX Runtime (For Further Speed Boost)

pip install onnxruntime

Modify our script:

import onnxruntime as ort
session = ort.InferenceSession("model.onnx")

3️⃣ Use TensorRT (For NVIDIA Jetson)

sudo apt install nvidia-tensorrt

Run:

import tensorrt as trt

---

🚀 Final Outcome

• ✅ LLM-Based AI Agent Running on Edge
• ✅ Accepts Queries via REST API
• ✅ Optimized for Low-Power Devices

---

We can use Ollama for deploying an LLM-based agent on Edge AI, but it has higher hardware requirements. Here’s how it fits in:

---

🛠 Can We Use Ollama on Edge AI?

✅ Yes, but it depends on our hardware:

• 🟢 NVIDIA Jetson Orin/Nano → Ollama works with GPU acceleration (TensorRT).
• 🟢 Apple M1/M2/M3 (Mac) → Ollama runs natively with Metal acceleration.
• 🟡 Raspberry Pi 5 → Difficult but possible (must use a small 4-bit model).
• 🔴 Intel NPU (Neural Compute Stick 2) → Not recommended (Ollama lacks NPU support).

🔹 Ollama works best with GPUs (CUDA on Jetson, Metal on Mac).
🔹 If our Edge device has only CPU, Ollama may be too slow.

---

🛠 How to Install Ollama on Edge AI?

1️⃣ Install Ollama

curl -fsSL https://ollama.com/install.sh | sh

2️⃣ Check if It Works

ollama run mistral

3️⃣ Use a Lightweight Model (Phi-3, TinyLlama)

ollama pull microsoft/phi-3-mini
ollama run phi-3-mini

---

🛠 How to Run an AI Agent with Ollama (FastAPI API)

1️⃣ Install FastAPI

pip install fastapi uvicorn

2️⃣ Create app.py

from fastapi import FastAPI
import subprocess

app = FastAPI()

@app.get("/ask")
def ask(question: str):
    cmd = f'ollama run phi-3-mini "{question}"'
    response = subprocess.check_output(cmd, shell=True).decode()
    return {"response": response}

# Run: uvicorn app:app --host 0.0.0.0 --port 8000

3️⃣ Run the API

uvicorn app:app --host 0.0.0.0 --port 8000

4️⃣ Test the API

curl "http://localhost:8000/ask?question=What%20is%20Edge%20AI?"

---

🛠 When to Use Ollama vs. Llama.cpp

Feature	Ollama	Llama.cpp
GPU Acceleration	✅ Yes (CUDA, Metal)	❌ No
Easy Model Management	✅ Yes	❌ Manual
Lower RAM Usage	❌ High (8GB+)	✅ Low (4GB possible)
Faster Inference	✅ Yes (GPU)	🟡 Medium (CPU)

🔹 If using Jetson with GPU, Ollama is good.

🔹 If using Raspberry Pi 5 (only CPU), use llama.cpp instead.

🛠 Yes, We Can Quantize Ollama Models!

Quantization reduces memory and speeds up inference by using lower-bit precision (4-bit, 8-bit). Ollama itself does not support custom quantization, but we can convert and load quantized models manually.

---

🔥 Step 1: Convert an LLM to Quantized GGUF Format

Ollama models are in .mod format, but we can use llama.cpp to quantize them.

1️⃣ Install llama.cpp for Quantization

git clone https://github.com/ggerganov/llama.cpp
cd llama.cpp
make

2️⃣ Download a Pretrained Model (e.g., Phi-3, Mistral)

wget -O model.f16.gguf https://huggingface.co/microsoft/Phi-3-mini-GGUF/resolve/main/model.f16.gguf

3️⃣ Quantize to 4-bit (Q4_0)

./quantize model.f16.gguf model.q4_0.gguf q4_0

For even smaller models:

./quantize model.f16.gguf model.q8_0.gguf q8_0  # 8-bit

---

🔥 Step 2: Run the Quantized Model in Ollama

Now, Ollama does not natively support GGUF yet. But we can do this:

1️⃣ Convert GGUF to Ollama Format

ollama create phi-3-mini-q4 -m model.q4_0.gguf

(⚠️ If this fails, Ollama does not support direct GGUF loading. Use llama.cpp instead.)

2️⃣ Run the Quantized Model

ollama run phi-3-mini-q4

---

🔥 Step 3: Alternative - Run Quantized Model with llama.cpp

If Ollama cannot load the GGUF model, run it with llama.cpp:

./main -m model.q4_0.gguf -p "Explain Edge AI in simple words." -n 100

---

💡 When to Use Quantized Models?

Use Case	Best Option
Jetson Orin + GPU	Ollama with CUDA
Raspberry Pi 5 (CPU only)	llama.cpp with 4-bit quantization
Low RAM (≤4GB)	llama.cpp with Q4_0 models
Fastest Response (8GB+ RAM, Edge AI)	Ollama with 8-bit models

---

🚀 Final Answer

✅ We can quantize models for Edge AI
✅ Use llama.cpp for loading GGUF models
❌ Ollama does not support custom quantized GGUF directly

🚀 Is CUDA the Best Choice for Edge AI?

✅ CUDA is great for Edge AI, but only if our device has an NVIDIA GPU (like Jetson Orin/Nano).

🔥 Where Can CUDA Work?

Device	CUDA Supported?	Best Option
NVIDIA Jetson Orin/Nano	✅ Yes	TensorRT + CUDA
Desktop with NVIDIA GPU	✅ Yes	CUDA-accelerated Ollama/Llama.cpp
Apple M1/M2/M3 (Mac)	❌ No	Use Metal (Apple’s equivalent)
Raspberry Pi 5 (ARM CPU only)	❌ No	Use llama.cpp (CPU)
Intel NPU (Neural Compute Stick 2)	❌ No	Use OpenVINO or EdgeTPU

---

🔥 Best CUDA-Optimized LLM Setup for Edge AI (Jetson)

1️⃣ Install CUDA & cuDNN

sudo apt install nvidia-cuda-toolkit

2️⃣ Install TensorRT for Faster LLM Execution

sudo apt install libnvinfer-dev

3️⃣ Run llama.cpp with CUDA for Fast Inference

LLAMA_CUBLAS=1 make
./main -m model.q4_0.gguf -p "Explain Edge AI." -n 100

4️⃣ Optimize CUDA with TensorRT (Jetson only)

sudo apt install tensorrt

Then use a TensorRT-optimized model.

---

🔥 Should We Use CUDA for Edge AI?

✅ Yes, if we have an NVIDIA Jetson or GPU
❌ No, if we use Raspberry Pi or Intel NPU

For Jetson, CUDA + TensorRT is the best choice for LLMs.
For Raspberry Pi, use llama.cpp (CPU-only, quantized models).

---

🚀 Final Verdict

If we have an NVIDIA GPU, CUDA is perfect. If not, use CPU-based LLMs.