One of the biggest decisions in building language models today is figuring out where to spend your limited compute budget. Should you train a bigger model or train on more data?

It’s something I’ve had to decide on more than once, especially while planning model training pipelines with fixed GPU hours and tight delivery timelines. This is a real-world challenge faced by AI product and engineering leaders every day.

Some recent experiments in this space have shown something interesting:
💠 If you have a small budget, it’s often better to go with a smaller model and train it on a lot of data. Bigger models don’t help much if they don’t have enough data to learn from.
💠 As your budget increases, the ideal approach shifts. You can start scaling up the model size, but the data size still plays a major role. The improvement you get from adding more parameters tends to flatten out quickly. What continues to help is feeding your model more tokens.

The key takeaway for you is:
◾ For a fixed budget, a medium-sized model with the right amount of training data can outperform a large model with limited data.
◾ As budgets grow, don’t just throw more parameters at the problem.

Focus on the balance between model size and data, and lean toward more training data if you’re unsure. Finding that balance is key. It often determines whether you’re building something usable or simply burning through compute.

🔍 While there’s no plug and play enterprise product for this yet, there are practical tools you can explore:
1️⃣ A helpful GitHub repo on scaling laws that shows how to model this trade-off (link in comments)
2️⃣ A Hitchhiker’s Guide to Scaling Law Estimation, which walks through small-scale simulations and extrapolation techniques (link in comments)

I highly recommend you use these type of Open-source tools, combined with internal logs and basic plotting, to give your AI teams a strong head start on getting the most out of your training budgets.

Remember, it is not just about building the POCs, it is about getting these AI products/ solutions into production


1) GitHub repo → “scaling_laws” : https://github.com/shehper/scaling_laws?

2) A Hitchhiker’s Guide to Scaling Law Estimation: https://arxiv.org/pdf/2410.11840v1