The LLM From Scratch Playbook

Short answer: What it takes to build an LLM from scratch

Building an LLM from scratch means five clear stages: define the use case, collect and clean data, design the transformer architecture and tokenizer, run pretraining and fine-tuning, then evaluate and deploy. This playbook gives a repeatable plan, a decision framework for build vs. fine-tune, and quick resource estimates so you can scope a real project.

Stage 0: Build vs. Fine-tune — quick decision

Ask these questions first:

• Do off-the-shelf models fail on your domain? If yes, consider build.
• Do you need full control over model behavior, licensing, or data privacy? Build may be right.
• Do you want faster time-to-value with lower cost? Fine-tune a base model.

For background reading on when to build, see this guide and the practical overview at Spheron.

Stage 1: Define the use case

Key decisions

• Scope: single task (QA, summarization) or general chat?
• Latency and size limits: do you need a tiny model for on-device use or a large cloud model?
• Safety and compliance: what filters and review workflow are required?

Tooling and docs

• Write a short spec: inputs, outputs, success metrics, and safety checks.
• Include a small labeled dataset example to test early.

Common pitfalls

• Vague goals: teams try to optimize everything and stall. Pick one primary metric.

Stage 2: Data curation & preparation

Key decisions

• Source mix: public data (Common Crawl, Wikipedia), licensed text, and your proprietary data.
• Quantity target: depends on model size. A rule of thumb is ~20 tokens per parameter for data-optimal training (see analysis).

Steps

1. Collect raw text from diverse sources.
2. Clean: remove duplicates, non-text, and bad encodings.
3. Normalize and chunk into training sequences.

Tooling

• Data pipelines: Apache Beam, Spark, or simple Python scripts for small projects.
• Dedup tools: hashing, shingling.
• Tokenizer prep: test tokenization coverage early.

Pitfalls

• Training on noisy duplicates inflates effective tokens and wastes compute.
• Ignoring domain-specific formats (tables, code) can break model behavior.

Stage 3: Tokenizer & architecture

Tokenizer choices

Most modern LLMs use subword tokenizers like BPE or SentencePiece (byte-level BPE is common). Tokenizer choice affects model size and speed. See practical implementations like rasbt/LLMs-from-scratch and tutorials that code tokenizers step-by-step.

Architecture basics

Use the Transformer block: multi-head attention, feed-forward layers, layer norm. Keep it simple for a first run: a small GPT-like stack is easier to debug than experimental designs. Learn the attention mechanism and transformer details.

Small code example (toy tokenizer)

def tokenize(text):\n    # toy split tokenizer for quick tests\n    return text.lower().split()[:1024]

Stage 4: Training — pretraining vs fine-tuning

Pretraining

Pretraining teaches the model language patterns on large unlabeled corpora. This requires the most compute and data. For small models you can pretrain on a few hundred million tokens; for larger models you need billions to trillions.

Fine-tuning

After pretraining, fine-tune on labeled or instruction data for your task. Instruction fine-tuning and RLHF are common for chat behavior. See applied guides on practical fine-tuning.

Compute & cost rough guide

• Tiny LLM (50M–500M params): single GPU to small cluster; days to weeks.
• Medium LLM (1B–10B): 8–64 GPUs; weeks; tens of thousands of dollars.
• Large LLM (10B+): 100+ GPUs; months; hundreds of thousands to millions.

These are broad ranges. Use the project estimator checklist below to refine GPU hours and cost.

Stage 5: Evaluation, safety, and deployment

Evaluation

• Automated metrics: perplexity, ROUGE, BLEU for tasks.
• Human evaluation: relevance, fluency, safety tests.

Safety & monitoring

• Filter outputs for harmful content.
• Audit model behavior with targeted tests.

Deployment

• Latency needs may require model distillation, quantization, or smaller runtime (8-bit, 4-bit).
• Production stack: model server, autoscaling, logging, and prompt versioning.

Project plan checklist (quick, repeatable)

1. Define scope and metric (1 page).
2. Gather sample data (1–5 GB) and build a test tokenizer.
3. Run a 1M-token pretraining experiment on a tiny model to test pipeline.
4. Estimate full data and compute using measurements from the test run.
5. Run full pretraining, then task fine-tuning and safety tests.
6. Deploy a canary instance and monitor for 2 weeks before full rollout.

Resource estimator: quick numbers to start planning

• Data: small experiment 1M tokens; useful model needs 10M+ tokens; production-grade often 100B+ tokens.
• GPU hours: small experiments 10–100 GPU-hours; medium projects 10k–100k GPU-hours.
• Budget: tiny projects <$5k, medium $10k–$100k, large $100k+ (varies by cloud and spot pricing).

Where to learn hands-on

Combine conceptual guides with code repos and workshops. Useful resources: Build a Large Language Model (book), the GitHub repo, and workshop videos like the 3-hour coding workshop. These give the step-by-step coding you need beyond this playbook.

FAQ

Can I build an LLM on a laptop?

You can learn by coding small models on a laptop. Real production pretraining needs more compute. Start small to validate pipelines.

How much data do I actually need?

It depends. For a useful domain-tuned model you might need tens of millions to billions of tokens. Use the 20 tokens per parameter rule to estimate scale.

When should I choose build over fine-tune?

Build when you need complete control, unique model behavior, or tight data governance. Fine-tune when a base model meets most needs and you want faster, cheaper results.

Next steps (quick)

• Run a 1M-token sanity run and measure cost and time.
• Use the checklist above to make a one-page project plan.
• Decide build vs. fine-tune with leadership based on cost, timeline, and IP needs.

Want a reusable project plan and resource estimator? Start with the templates linked in the practical guides above, then refine with your test-run metrics. Good planning saves weeks of wasted compute and thousands in cloud spend.

Sam