Build an LLM from Scratch: What It Actually Takes in 2026

Building an LLM from scratch means training a transformer model on a large text corpus from random weight initialization — not fine-tuning an existing model. It's what companies like OpenAI, Anthropic, Google, and DeepSeek do to create their foundation models. Most developers will never need to do this, but understanding the process is essential for anyone working seriously with LLMs.

The components you need to build

1. The transformer architecture

Modern LLMs are decoder-only transformers — they process input left to right and generate one token at a time. The core components:

• Token embeddings — convert token IDs to dense vectors (typically 768-8192 dimensions)
• Positional encodings — tell the model where each token is in the sequence (RoPE is standard in 2026)
• Transformer blocks — repeated N times (e.g. 32 blocks for a 7B model, 80 for a 70B)
• Multi-head self-attention — lets every token attend to every other token; the core computational primitive
• Feed-forward network — 2-3x wider than the attention dimension, applies nonlinear transformations
• Layer normalization — RMSNorm is standard (simpler and slightly faster than LayerNorm)
• Unembedding layer — projects back to vocabulary size for next-token probability distribution

2. The tokenizer

Text must be converted to integer token IDs before training. BPE (Byte Pair Encoding) is standard — it builds a vocabulary of common subword units. Training a tokenizer requires running BPE on a representative text sample, typically 10-100GB. Most teams reuse an existing tokenizer (Llama's, Qwen's, or a custom one) rather than building from scratch.

3. The training data pipeline

Pre-training requires trillions of tokens of high-quality text. The data pipeline involves:

• Downloading and deduplicating web crawls (Common Crawl, FineWeb)
• Quality filtering — removing low-quality, toxic, and duplicate content
• Domain mixing — balancing web, code, books, academic, and other sources
• Tokenizing and sharding into training batches

4. The training loop

for batch in dataloader:
    tokens = batch["input_ids"]          # (batch_size, seq_len)
    logits = model(tokens[:, :-1])       # predict next token
    loss = cross_entropy(logits, tokens[:, 1:])  # compare to actual next token
    loss.backward()
    optimizer.step()
    optimizer.zero_grad()

Standard optimizer: AdamW with cosine learning rate schedule and warmup. Newer models (including DeepSeek V4) use the Muon optimizer for better sample efficiency.

Parameter counts and what they mean

What it actually costs

What most people should do instead

Building from scratch is only justified when:

• You need a model trained on a highly domain-specific corpus (medical, legal, scientific)
• You have data that existing models haven't seen and can't be added via fine-tuning
• You need full control over the training data for compliance or IP reasons

For everything else: start from an open-weight model and fine-tune with Unsloth or LoRA. A fine-tuned Llama 3.3-70B will outperform a scratch-trained 7B on almost any task, at a fraction of the cost.

The best LLM for your use case is almost never one you trained from scratch. It's an open-weight model fine-tuned on your specific data and aligned with your users' preferences.