Edge AI Deployment: Running LLMs on Your Device
Contents
Edge AI Value
Privacy: data stays on device
Speed: no network latency
Cost: zero API fees
Offline: works without internetWhat Devices Can Run
| Device | Runnable Models | Speed |
|---|---|---|
| iPhone 15 Pro | 3B Q4 | ~15 tok/s |
| MacBook M3 | 7B Q4 | ~30 tok/s |
| Mac Studio M2 Max | 13B Q4 | ~25 tok/s |
| NVIDIA Jetson | 7B Q4 | ~20 tok/s |
| High-end Android | 3B Q4 | ~10 tok/s |
Framework Choices
Ollama (Simplest)
ollama run llama3.2 # 3B model
ollama run codellama:7b # coding modelMLX (Apple Silicon)
# Apple Silicon optimized
from mlx_lm import generate
response = generate(
model="llama-3.2-3b",
prompt="explain what a closure is"
)llama.cpp (Universal)
# Universal, lightweight
./llama-cli -m model-q4.gguf -p "Hello"Real Performance
# iPhone 15 Pro test
model = "llama3.2-3b-q4"
prompt = "write a Python quicksort"
# speed: ~15 tokens/second
# quality: slightly below GPT-3.5
# battery: 5% drain in 10 minutesWhat Scenarios Suit
Good for Edge AI:
✅ privacy-sensitive data (medical, legal)
✅ offline scenarios (field, airplane)
✅ low-frequency use (save API costs)
✅ simple tasks (3B model enough)
Not for Edge AI:
❌ complex reasoning (needs larger models)
❌ high-frequency calls (device heats up)
❌ real-time chat (latency noticeable)Conclusion
Early 2026 Edge AI is already usable.
iPhone 15 Pro + 3B Q4 model, daily simple tasks sufficient. Mac Studio can run 13B model, most scenarios comparable to cloud API.
Privacy-first or offline scenarios—Edge AI is the best choice.