Look, I’ll be honest with you.
When I first started working with AI models back in 2015, training one felt like watching paint dry—if that paint cost you thousands of dollars per hour and might not even turn out the color you wanted.
Fast forward to October 2025, and holy cow, things have changed.
The AI model training process isn’t just about throwing data at a computer anymore. It’s become this fascinating dance between data quality, computational power, cost optimization, and honestly, a bit of trial and error that nobody really talks about.
Here’s what surprised me most recently: a single prompt on models like ChatGPT can use as much energy as leaving a light bulb on for about an hour. Yeah, I had to reread that stat too.
So let me walk you through what actually happens when we train these AI models, because it’s way more interesting—and complicated—than most articles make it sound.
The Basic Building Blocks (But Nothing’s Basic Anymore)
Training an AI model starts with data. Lots and lots of data.
But here’s the thing that changed my perspective completely last year: it’s not about quantity anymore. The shift toward high-quality, curated datasets has been massive. Microsoft’s Phi models proved that smaller, carefully curated datasets can actually outperform models trained on way more data.
Think about it like cooking. You can’t make a great meal by just throwing every ingredient in your kitchen into a pot. You need the right ingredients in the right amounts.
The training process typically follows these stages:
Data Collection and Preparation – This is where engineers ask themselves critical questions: How much data do we actually need? Where should it come from? Can we store it cost-effectively? I’ve seen teams waste months collecting unnecessary data that just bloated their storage costs.
Setting Up Infrastructure – You’ll need serious computational power. Most teams now use GPUs because they handle simultaneous operations way better than traditional CPUs. Cloud-based solutions have become popular, though hardware shortages in 2025 are making everyone rethink their strategies.
Model Architecture Selection – This is where you pick your foundation. Are you going with a large language model? A smaller specialized model? The trend I’m seeing lately leans toward smaller, more efficient models that don’t require cloud processing for everything.
Pre-Training: The Foundation Phase
Pre-training is where your model learns general patterns from massive datasets.
But here’s what nobody tells you: we’re hitting some weird walls with pre-training in 2025.
Reports suggest that simply making pre-training runs larger isn’t delivering the amazing returns everyone expected. Models like Grok 3 and GPT-4.5, which had some of the largest pre-training runs we’ve seen, aren’t qualitatively that much better than models trained with significantly less compute.
What’s happening? Some say we’re running out of reasonable quality data. Others think the returns to further pre-training are just naturally diminishing.
I honestly think it’s both.
The industry is responding by getting smarter about synthetic data. Microsoft’s Orca and Orca 2 models showed that you can use synthetic data during post-training to get small language models performing at levels previously only seen in much larger models.
That’s actually pretty wild when you think about it.
The Role of GPUs and Hardware Reality Checks
Let me tell you about GPUs because this is where things get real expensive real fast.
GPUs process datasets and train complex models way faster than CPUs. They can handle simultaneous operations, which is absolutely necessary for large-scale machine learning tasks.
The problem? Hardware shortages have put massive pressure on the industry.
Cloud computing costs are rising while hardware availability is declining. This double whammy is actually driving the shift toward smaller models—not just because smaller is trendy, but because it’s becoming a necessity.
When I set up a training run now, I have to think about:
- Compute resources required for model training
- Whether to use on-premise GPUs or cloud-based solutions
- How to optimize the model preparation phase
- Deployment costs after training completes
Most of the computational burden rests with cloud providers now, and they’re scrambling to optimize their systems to meet growing GenAI demands.
Post-Training and Reinforcement Learning
After pre-training comes post-training, and this is where models get their personality.
Reinforcement learning helps models understand what responses are actually useful versus just technically correct. It’s like teaching someone not just the rules of a game, but how to play it well.
Models with advanced reasoning capabilities, like OpenAI’s o1, can now solve complex problems using logical steps similar to human thinking. They’re useful in fields like science, coding, math, law, and medicine.
What surprised me was learning that GPT-4.5 probably hasn’t been reinforcement-learned very effectively yet because it’s inconvenient on a model of that scale. Even the big players are dealing with practical limitations.
Cost Optimization: The Unsexy Truth of 2025
Here’s something that keeps me up at night: cost optimization.
Training AI models is incredibly resource-intensive. From data acquisition and storage to model deployment and maintenance, every stage needs refining.
The focus in 2025 has shifted dramatically toward efficiency. There’s even been a $500 million private sector investment announced specifically to optimize AI infrastructure.
Techniques like model distillation are gaining traction. This transfers knowledge from a larger, complex model to a smaller, efficient one while retaining most of the predictive power. Apple’s Intelligent System runs AI models directly on mobile devices using this approach instead of depending entirely on cloud servers.
Other optimization methods include quantization (reducing data volume for faster performance) and pruning (removing non-essential parts of the model). There’s noteworthy progress happening with transformer-based models too.
Emerging Trends That’ll Shape Training in 2025
Federated Learning is becoming huge. Institutions can share knowledge without sharing personal data, building global models together. Financial and healthcare organizations are particularly interested because they can move training procedures to the client side instead of transferring sensitive data to central servers.
The Shift from Training to Inference is another big one. As reasoning becomes more important, we’re seeing heightened demand for inference capabilities rather than just throwing more compute at training.
Explainable AI is finally getting serious attention. Users and stakeholders want to understand and trust machine learning outputs. The narrative is moving away from “AI will replace humans” toward “AI is an extremely powerful instrument—our superpower, but not our substitute.”
Can’t say I disagree with that sentiment.
What This Means for Anyone Training Models Today
If you’re planning to train an AI model right now, here’s my honest advice:
Don’t assume bigger is better. Frontier training compute is increasing, but maybe only 3.5x per year instead of the previous 4.5x trend. Focus on data quality over quantity.
Think hard about your infrastructure. Cloud versus on-premise isn’t a simple choice anymore with current hardware limitations.
Budget for the full lifecycle. Training is just one part. Deployment, monitoring, and optimization will eat up resources you didn’t expect.
Consider specialized models for specific tasks rather than trying to build one model that does everything. The synergy between training methods and how models power AI agents is creating new opportunities for targeted solutions.
Final Thoughts
The AI model training process in 2025 is simultaneously more sophisticated and more constrained than it was even two years ago.
We’re getting smarter about efficiency, more realistic about costs, and honestly more humble about what throwing more compute at a problem can actually solve.
What excites me most? The innovation happening at every stage. From synthetic data generation to edge device deployment, teams are finding creative solutions to real limitations.
And yeah, we’re still figuring a lot of this out as we go.
That’s what makes it interesting.
Frequently Asked Questions
How long does it take to train an AI model?
It varies wildly. Small models might train in hours, while large language models can take weeks or months depending on computational resources and data volume.
Why are smaller AI models becoming more popular?
Cost, efficiency, and hardware limitations are driving the trend toward smaller models that can run on edge devices without constant cloud connectivity.
What’s the biggest challenge in AI model training right now?
Balancing performance with cost and resource constraints while dealing with hardware shortages and rising cloud computing expenses.