So, you want to get into the game of building a large language model. It sounds prestigious. It sounds like the kind of thing that gets you a massive seed round or at least a lot of respect at a tech mixer. But honestly? Most people have no idea what they’re actually signing up for when they decide to train a model from scratch. It isn’t just "plugging in some data and hitting run." It’s a messy, expensive, and often frustrating process of babysitting clusters of GPUs that want to fail at 3:00 AM.
You’ve probably seen the headlines about GPT-4 or Claude 3.5. These models are massive feats of engineering. But for most companies or independent researchers, the goal isn't necessarily to beat OpenAI at their own game. It's about creating something specialized. Something that doesn't hallucinate about your specific industry. Or maybe it's just about the privacy of owning your own weights. Whatever the reason, if you’re serious about building a large language model, you need to understand that the "Large" part of the name is the biggest hurdle.
Why data quality is the only thing that actually matters
Data is everything. Seriously. If you feed your model garbage, you’re going to get garbage out, no matter how many H100s you throw at the problem. Back in the day, people thought more was better. Just scrape the whole internet! Grab every Reddit thread, every Wikipedia page, and every sketchy blog post you can find. That was the "Common Crawl" era.
Things have changed. Now, researchers like those at Meta or Mistral emphasize "high-signal" data. Look at the Llama 3 paper. They used over 15 trillion tokens. But it wasn't just any tokens. They spent an absurd amount of time on deduplication and cleaning. If a model sees the same crappy SEO blog post 50 times during training, it starts to think that’s how humans actually talk. That’s bad.
You need a mix. You want code from GitHub to teach the model logic. You want textbooks for formal reasoning. You want conversational data so it doesn't sound like a Victorian ghost. But filtering that stuff? That’s the real work. Most of the "building" process is actually just writing Python scripts to clean up messy text files and removing "low-quality" junk.
The hardware reality check
Let’s talk about money. If you think you’re building a large language model on your gaming laptop, I have some bad news. You need VRAM. Lots of it.
To train a modest 7-billion parameter model—which is considered "small" these days—you’re looking at serious hardware. Most folks use A100s or H100s. A single H100 can cost upwards of $30,000, and you’re going to need dozens, if not hundreds, of them linked together via InfiniBand to get anything done in a reasonable timeframe.
The cloud vs. on-premise debate
Buying hardware is a nightmare because of the supply chain. Renting from AWS, Google Cloud, or specialized providers like Lambda Labs or CoreWeave is the standard. But even then, you’re burning through thousands of dollars an hour.
- Renting: Great for one-off projects. No maintenance.
- Buying: Only makes sense if you’re going to be training 24/7 for two years.
- Colab/Consumer GPUs: Fine for fine-tuning a pre-existing model, but mostly useless for building one from the ground up.
Hardware fails. It’s a fact of life. When you’re running a cluster of 512 GPUs, one of them will overheat or throw a memory error. If your software isn't set up for "check-pointing"—basically saving your progress every few hours—you could lose two days of work and $50,000 in compute time just like that. It’s stressful.
The architecture: It’s still Transformers (mostly)
Since 2017, the "Transformer" architecture has been the king. If you’ve read the "Attention Is All You Need" paper, you know the drill. But even within that framework, there are choices. Do you go with a standard Encoder-Decoder or just a Decoder-only setup?
Most modern LLMs like GPT-style models are decoder-only. They’re built to predict the next word in a sequence. It sounds simple, but the math behind the "Self-Attention" mechanism is what allows the model to understand that in the sentence "The animal didn't cross the street because it was too tired," the word "it" refers to the animal, not the street.
Tinkering with the guts
You have to decide on things like:
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- Context Window: How much can the model "remember" at once? 8k tokens? 128k? The bigger the window, the more memory you need.
- Tokenizer: How do you turn words into numbers? Byte Pair Encoding (BPE) is the standard. If your tokenizer is bad, your model will struggle with basic spelling or math.
- Activation Functions: SwiGLU is pretty popular right now. It’s faster and more stable than the older stuff.
The "Training" phase isn't the end
Once the base model is trained, you’re only halfway there. You have a "Base Model." It’s smart, but it’s a jerk. If you ask it "How do I bake a cake?", it might respond with another question like "What ingredients do I need for a cake?" because it’s just trying to complete a pattern it saw on a forum.
This is where RLHF (Reinforcement Learning from Human Feedback) comes in. You need humans to rank the model's answers. "This answer is good, this one is mean, this one is factually wrong." This "alignment" phase is what makes the model actually useful for people. It’s also incredibly expensive because you have to hire thousands of people to read and rate text.
Fine-Tuning: The shortcut
Most people shouldn't be building a large language model from scratch. They should be fine-tuning.
Take an open-source model like Llama 3 or Mistral. Use a technique called LoRA (Low-Rank Adaptation). It allows you to tweak the model's behavior using just a fraction of the compute power. You can train it on your company's internal documents in an afternoon on a single GPU. Honestly, for 99% of use cases, this is the smarter move. It’s cheaper, faster, and usually more effective than trying to teach a brand-new model how to speak English from scratch.
Evaluations: How do you know if it's actually good?
You can't just talk to the model and say "Yeah, it seems smart." That’s subjective. You need benchmarks.
People use things like MMLU (Massive Multitask Language Understanding) or HumanEval for coding. But even these are getting "contaminated." Because the models are trained on the whole internet, and the test questions for these benchmarks are also on the internet, the models basically memorize the answers. It’s like a student stealing the answer key before the SATs.
To really know if your model works, you have to build "blind" tests. Give it tasks it hasn't seen. Ask it to explain a concept in the style of a 1920s noir detective while only using three-letter words. If it can handle the weird stuff, it’s probably solid.
Ethics and the "Moat"
Building these things comes with a heavy weight. Bias is a massive problem. If your training data has a lot of 1950s era textbooks, your model is going to have some very backwards views on gender and race. Scrubbing that out is nearly impossible. You have to be proactive.
And then there's the "moat." In business, a moat is your competitive advantage. If everyone is building a large language model, what makes yours special? Is it your proprietary data? Your hyper-efficient inference? If you’re just building a "worse version of GPT-4," you’re going to get crushed. You have to find a niche. Maybe it's a model specifically for legal discovery in the UK. Maybe it's a model that runs entirely on a smartphone.
Practical Next Steps for Your Journey
If you’re still determined to go through with this, don’t start by writing CUDA kernels. Start small and scale up. The complexity grows exponentially, not linearly.
Audit your data sources immediately. Stop collecting and start cleaning. Use tools like datatrove or trafilatura to extract clean text. A 1GB dataset of perfect text is often more valuable than 1TB of junk.
Get familiar with the ecosystem. You need to know your way around Hugging Face. It’s the center of the universe for this stuff. Learn how accelerate and deepspeed work to distribute your training across multiple GPUs. Without these libraries, you’ll spend all your time debugging networking issues instead of training.
Run a small-scale "toy" model first. Try building a 100-million parameter model. It’ll be "dumb," but it will teach you the pipeline. You’ll learn how to handle the weights, how to monitor loss curves, and how to spot when your model is "collapsing"—where it just starts repeating the word "the" forever.
Consider the "Inference" cost. Training is a one-time cost, but running the model for users is forever. Look into quantization. Turning 16-bit weights into 4-bit weights can make your model run 4x faster with barely any loss in "intelligence." If you don't plan for inference early, your project will die in the lab because it’s too expensive to actually use.
Building an LLM is a marathon through a minefield. It’s one of the hardest engineering challenges in the world right now. But if you get it right, you aren't just building software; you're building a tool that can actually "reason" through problems. Just remember to save your checkpoints. Seriously. Save them often.