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Building a Giant Language Model: When Does DIY Make Sense?

The world of natural language processing (NLP) has been revolutionized by large language models (LLMs). These behemoths, boasting billions of parameters, can understand and generate text so convincingly that they might have you questioning if you're talking to a human.


Companies like OpenAI (creators of GPT-3) and Google AI are making headlines with their LLM advancements, but what if you want to make one yourself? It might seem impossible, but building an LLM from scratch could be the right path for certain businesses.


The Case for In-House Language Models


Why would anyone want to climb that mountain when you could use pre-built models? Here are a few reasons:

  • Security and Privacy: Highly sensitive data might be best kept away from the servers of 3rd party LLM providers. An in-house model gives you complete control.

  • Customization: Pre-built models are trained on general-purpose data. If you have a very specific domain (e.g., legal documents, medical research), tailoring your own LLM could lead to much better results.


Of course, building your own LLM isn't a trivial endeavor. Let's talk costs.


The Price You Pay for Artificial Brilliance


To Build LLM from scratch is notoriously expensive. Meta's open-source LLM, Llama, provides some data points. Their 7B parameter model took around 180,000 GPU hours to train. Upping the parameters to 70B meant burning through 1.7 million GPU hours. Yikes!

Let's translate that to dollars. Using on-demand cloud computing with powerful GPUs, you're looking at $1-2 per GPU per hour. That means a model in the 10B parameter range could cost $150,000 to train. A 100B parameter beast? We're talking upwards of $1.5 million just in cloud computing costs.


There's also the hardware investment. If you're building at scale, purchasing your own GPUs (in the thousands!) makes sense in the long run. But plan on an upfront cost of at least $10 million. Oh, and let’s not forget the electricity bill!


Don't forget, you'll need a team of highly skilled (and highly paid) ML engineers, data scientists, and others to make this happen.


Convinced You're Crazy Enough to Try? Here's the Blueprint


Building an LLM from scratch can be distilled down to these key steps:


1. Data Curation: The Feast Your Model Will Devour

"Garbage in, garbage out" is the mantra here. LLMs drink up massive amounts of text—we're talking trillions of words. The internet is your oyster, with websites, books, code, and more. But quality matters! Here's what to consider:

  • Filtering: Weed out low-quality text that will do more harm than good.

  • De-duplication: Avoid redundant text polluting your dataset.

  • Privacy: Scrub any personally identifiable data.

  • Tokenization: Break that text down into smaller chunks ("tokens") your model understands.


2. Model Architecture: The Brains of the Operation

Transformer models are where it's at for LLMs. Think of them like giant brains with two key parts:

  • Encoder: Creates rich representations of your text, understanding word meanings in context.

  • Decoder: Generates text, one word after another, taking what the encoder learned into account.

Choosing the right mix of encoders and decoders depends on whether you want your LLM to mainly understand text, generate it, or both.


3. Training at Scale: Crunching the Numbers

This is where the magic (and the immense cost) happens. Here's the gist:

  • Self-Supervised Learning: Basically, it's teaching your model to predict missing words in a sequence.

  • Giant Compute: Use specialized techniques to spread the training load across those thousands of GPUs you bought.


Is It Worth It?

Building an LLM from scratch is an enormous investment of time, money, and talent. For most businesses, using existing models is the way to go. But if absolute control over your AI's inner workings or a truly tailored language experience is paramount, then maybe, just maybe, you're ready to build your own linguistic giant.


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