Training AI Models: Apple M4 vs. Raspberry Pi 3
In the age of tiny computers and powerful chips, I usually run my solar forecasting Python program on a spare Mac Mini equipped with Apple’s new M4 chip — a compact machine that handles model training with effortless speed. But recently, I became curious: what if I tried running the same code on something far less powerful — like a Raspberry Pi 3 from 2015?
Let’s just say: it was less about benchmarking and more a meditative exercise in patience.
The Setup
To explore the difference, I trained two well-known regression models using scikit-learn:
- Random Forest Regressor
- Gradient Boosting Regressor
The dataset included several years of daily solar energy production and weather conditions — temperature, irradiance, cloud cover, wind speed — all essential features for accurate forecasting. It’s the kind of model that benefits from regular retraining to stay aligned with real-world data trends.
Running this on the Mac Mini M4 produced blazing-fast results:
| Model | R² Score | MAE (kWh) | Training Time | |--------------------|----------|-----------|------------------| | Random Forest | 0.9209 | 4.22 | 1.67 seconds | | Gradient Boosting | 0.9601 | 2.79 | 0.75 seconds |
The results show not only excellent model accuracy, but also impressive efficiency — making daily retraining practical.
The Raspberry Pi Reality
Then came the Raspberry Pi 3 — a lightweight ARM board with 1 GB of RAM and a 1.2 GHz Cortex-A53 CPU. Getting it ready was an adventure of its own. After several hours of compiling packages, configuring swap space, and calming thermal throttling warnings, I finally had scikit-learn running.
The code and data were exactly the same. But the experience? Night and day.
| Model | R² Score | MAE (kWh) | Training Time | |--------------------|----------|-----------|--------------------| | Random Forest | 0.9209 | 4.22 | ~200 seconds | | Gradient Boosting | 0.9601 | 2.79 | ~90 seconds |
The results were surprisingly accurate — identical to those produced on the Mac. But the training time was long enough that you could step away, make coffee, and water your plants before it finished. It works — but it’s no longer real-time.
What Does This Mean?
Yes — you can train models on a Raspberry Pi 3. But you probably shouldn’t if you’re on a schedule.
While the Pi succeeded in running both regressors, the time and energy cost makes it impractical for frequent retraining. Memory constraints and CPU throttling only compound the issue as datasets grow.
A more sustainable workflow looks like this:
- Train on the Mac, then deploy to the Pi for inference
- Simplify models with fewer estimators and depth constraints
- Use lightweight alternatives like
DecisionTreeRegressoror linear regression when performance matters more than nuance
That’s the path I took. The Mac now handles all model training and forecasting logic. The Pi, meanwhile, serves dashboards, syncs updates, and integrates with my Home Assistant — quietly doing its job at the edge.
Final Thoughts
This wasn’t just a tech experiment — it was a reminder that machine learning doesn’t live in a vacuum. Code, data, and algorithms are only part of the equation. Hardware matters. The Mac Mini M4 slices through gigabytes of solar data in seconds. The Raspberry Pi 3, by contrast, forces you to slow down, strip things back, and appreciate efficiency. It shows that with careful design, even constrained devices can play a role in smart systems.
Each machine has its place.
One predicts the future. The other makes sure you see it on your wall-mounted display — and maybe reminds you it’s a good day to charge the car with sunshine.