Strategies for Training Large Language Models | Generated by AI

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Question: How should I approach training a GPT-3-scale model — thinking about data collection strategy, how much data is really needed, and what’s the most cost-effective way to accumulate 10TB+ without paying for expensive external bandwidth?

Answer:

Great thinking, and you’re on the right track! Let me break this down clearly across three areas: how much data you actually need, where to get it without crazy bandwidth costs, and the practical strategy.

1. How Much Data Does a GPT-3-Scale Model Actually Need?

First, let’s calibrate expectations with real numbers:

GPT-3’s training corpus started at 45TB of compressed plaintext (roughly 90TB uncompressed), but after filtering, the actual training mix was around 570GB total — with CommonCrawl contributing about 400GB of that.

So the key insight is: raw data volume ≠ effective training data volume. Massive raw dumps get filtered down dramatically.

For reference: GPT-2 was trained on ~40GB of text, GPT-3 on approximately 500 trillion tokens, and Phi-3 on 3 trillion tokens. A solid order of magnitude target for a serious personal experiment is around 1 trillion tokens.

For your current 60GB experiment — that’s actually close to GPT-2 scale territory, which is a reasonable start. Jumping to 10TB of raw data is not unreasonable for GPT-3-class ambitions, but you need to account for heavy filtering reducing it significantly.

2. Data Quality > Raw Size

A smaller model trained on high-quality open source data can outperform a larger model trained on noisy, poorly curated datasets. The preprocessing pipeline — deduplication, URL filtering, tokenization, contamination detection — often matters more than architectural innovations.

Key ready-made datasets you should look at instead of raw-crawling everything:

These are pre-filtered and deduplicated, saving you enormous preprocessing work.

3. How to Get 10TB Without Expensive Bandwidth

You’re right to think carefully about this. Here are the real strategies:

Option A: Use Pre-existing Open Datasets (Best ROI)

Common Crawl comprises petabytes of raw web data released monthly. GPT-3, LLaMA, OpenLLaMA, and T5 all used it in pre-training. You can download specific monthly snapshots directly from commoncrawl.org — they offer free S3 access via AWS, which is the cheapest path.

Option B: Hugging Face datasets Library Streaming

You don’t need to download 10TB all at once. Use Hugging Face’s streaming API — process data on-the-fly without storing everything locally. This avoids the bandwidth problem entirely for training purposes.

from datasets import load_dataset
ds = load_dataset("HuggingFaceFW/fineweb-edu", streaming=True, split="train")

Option C: VPS / Server with Unlimited Internal Bandwidth (Your DMIT approach)

Your instinct about DMIT is solid for bulk downloading:

Used HDDs can be found at around €10/TB on eBay or Amazon — just be cautious about “0-hour usage” claims from resellers, and factor in that some drives may fail in transit.

Option D: Ask Friends / Research Groups for Direct Drive Copy

100% valid. Academic labs and ML communities often share drives directly for exactly this reason. The ML community around Hugging Face is very collaborative about this.

4. The Practical Roadmap

Step Action
Now (60GB) Finish GPT-2 scale experiment, run benchmarks (even simple ones like perplexity on held-out set)
Next (500GB–1TB) Download FineWeb-edu subset, clean it, tokenize with BPE
Later (10TB+) Use VPS to batch-download Common Crawl snapshots, copy to local drives
Training Use Chinchilla scaling laws to match data size to model parameter count

Scaling laws show that parameter count and dataset size should be scaled proportionally for optimal performance — a 70B model trained on insufficient data underperforms a 7B model trained on properly scaled, high-quality data.

The most important thing right now: run benchmarks on your 60GB GPT-2 experiment first before scaling. Even simple perplexity measurements or few-shot task evaluations will tell you if your pipeline is working before you invest in 10TB of data collection.

References:

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