Building a large language model from scratch is a major systems project, not simply a matter of implementing a Transformer. You must define a narrow objective, assemble legally usable data, design an efficient tokenizer, provision reliable compute, train without silent failures, and evaluate whether the result is better than an existing open model.
For most Indian startups, researchers, and student teams, starting from scratch is justified only when existing models cannot provide the required language coverage, domain knowledge, privacy, or deployment economics. Otherwise, continued pretraining, parameter-efficient fine-tuning, retrieval-augmented generation, or distillation will usually deliver value faster. This building large language models from scratch guide focuses on the cases where pretraining is warranted—especially Indic-language, public-sector, and specialised enterprise applications.
1. Start with a model brief, not a parameter count
Write a one-page specification before selecting GPUs. Define:
- Target languages: Hindi alone is a different project from a balanced Hindi-Tamil-Telugu model or a multilingual model covering all scheduled Indian languages.
- Use case: chat, document completion, code, search, translation, speech transcripts, or structured extraction each requires different data and evaluations.
- Context length: 4K, 8K, 32K, or longer contexts affect memory, data formatting, and serving cost.
- Deployment target: a 100M–1B parameter model may be ideal for an edge device, while a larger model may suit an API platform.
- Success criteria: set measurable targets for token efficiency, factuality, latency, safety, and cost per million tokens.
Run a baseline first. Test strong open-weight models on your representative prompts, including noisy OCR, code-switching, transliterated text, and regional names. A baseline tells you whether the problem is model knowledge, retrieval, prompting, or language coverage.
2. Choose the smallest architecture that can answer the question
For text generation, the usual foundation is a decoder-only Transformer trained with next-token prediction. A modern block commonly includes:
- Causal self-attention, preventing the model from seeing future tokens.
- RoPE or another positional method for representing token order.
- RMSNorm for stable, efficient normalization.
- A gated feed-forward layer such as SwiGLU.
- Residual connections, carefully chosen initialization, and an appropriate attention implementation.
Do not copy a fashionable architecture without validating its trade-offs. Grouped-query or multi-query attention can reduce key-value cache memory during inference. The vocabulary, number of layers, hidden width, and context length should be selected together rather than optimised independently.
A small model trained on high-quality, domain-relevant data can outperform a larger model on a narrow task. For an Indic-language assistant, token efficiency and clean supervision may matter more than adding parameters.
3. Build an accountable data pipeline
Data quality is the central competitive advantage of a from-scratch model. Collect sources with clear provenance and maintain records for licence, language, collection date, filtering decisions, and permitted use. Avoid treating a web crawl as automatically usable training data.
A practical pipeline should include:
1. Acquisition: licensed documents, public-domain material, permitted web content, code, books, government publications, and curated domain corpora.
2. Extraction: preserve document structure while removing navigation, boilerplate, duplicated headers, and malformed markup.
3. Language identification: classify language at document and segment level. Detect mixed-language text rather than forcing every document into one label.
4. Quality filtering: remove spam, machine-generated repetition, broken OCR, extremely short fragments, and unsafe or irrelevant content according to the project’s policy.
5. Deduplication: use exact hashes and near-duplicate methods such as MinHash or locality-sensitive hashing. Deduplicate across train, validation, and test splits to prevent inflated results.
6. Contamination checks: compare evaluation examples against the training corpus and record any known overlap.
Indian-language data needs extra care. Script variants, transliteration, code-switching, spelling variation, OCR errors, and uneven web availability can all distort the dataset. The principles in this low-resource Indic NLP guide are useful when estimating coverage and designing language-specific quality checks.
4. Train a tokenizer for the actual corpus
A tokenizer determines how much text the model can process, how expensive inference becomes, and whether Indian languages receive fair capacity. BPE, unigram, and byte-level approaches can all work; the right choice depends on corpus composition and robustness requirements.
Measure the candidate tokenizer on:
- Tokens per character and tokens per word by language.
- Code-mixed and transliterated sentences.
- Names, numbers, URLs, emojis, and technical terms.
- Normalised versus raw Indic text.
- Compression and fertility compared with established tokenizers.
A vocabulary of 32,000–128,000 entries is a starting range, not a rule. A larger vocabulary reduces sequence length but increases embedding and output-layer memory. Reserve stable special-token conventions for documents, roles, tool calls, and end-of-sequence markers before training begins.
5. Estimate compute, memory, and failure recovery
Use a scaling estimate before renting hardware. Training cost depends on parameter count, training tokens, sequence length, precision, hardware utilisation, checkpoint frequency, and network performance—not just GPU hours. Budget separately for data processing, experiments, failed runs, evaluation, storage, and inference.
For a serious run, plan for:
- GPUs with sufficient HBM and fast interconnects.
- Distributed storage that can stream shards without becoming the bottleneck.
- Mixed-precision training, typically BF16 where supported.
- Checkpoints containing model, optimizer, scheduler, tokenizer, data position, and random states.
- Automatic restart after node failure and validation that resumed training is numerically consistent.
- Monitoring for loss, gradient norms, throughput, GPU memory, communication time, and data-loader stalls.
A small team should first complete a tiny end-to-end run. Train a 100M–500M parameter model or a reduced-token sample to verify tokenisation, masking, checkpoint restoration, evaluation, and serving. This is cheaper than discovering a data leak or broken distributed configuration after weeks of training.
6. Use distributed training deliberately
Data parallelism replicates the model and divides batches across workers. When the model or optimizer state does not fit on one GPU, use fully sharded data parallelism, tensor parallelism, pipeline parallelism, or a combination. The correct choice depends on model size, topology, batch size, and framework maturity.
FlashAttention-style kernels reduce attention memory traffic and usually improve throughput, but they do not make long-context training free. Sequence length still has a major effect on activation memory and compute. Profile before scaling out: poor data loading, network congestion, or synchronisation overhead can waste more money than an inefficient kernel.
Distributed training is an engineering discipline in its own right. Teams working on cluster orchestration can also review this guide to building distributed systems with AI agents, particularly for observability, retries, and workload coordination.
7. Pretraining, continued training, and post-training
Pretraining teaches broad statistical structure. Use a staged data mixture if the project requires it, but document every change so that improvements can be attributed correctly. AdamW, warm-up, gradient clipping, and cosine decay are common choices; run small ablations rather than assuming defaults are optimal.
After pretraining, convert the base model into a useful system through:
- Continued pretraining: additional domain or language data.
- Supervised fine-tuning: high-quality instruction and response examples.
- Preference optimisation: DPO or related methods when ranked responses are available.
- Tool and format training: structured outputs, function calls, retrieval citations, or workflow-specific actions.
- Safety tuning: refusal behaviour, privacy protection, content policies, and escalation paths.
Keep base, instruction-tuned, and safety-tuned checkpoints separate. This makes regressions easier to diagnose and gives downstream users more flexibility.
8. Evaluate Indic capability and real-world reliability
A single benchmark score is not evidence of readiness. Build a held-out evaluation suite covering language understanding, generation, reasoning, factuality, code if relevant, and safety. Include native speakers and domain experts in review.
Track:
- Per-language perplexity and token efficiency.
- Exact-match and task-specific scores.
- Human ratings for fluency, helpfulness, and cultural appropriateness.
- Hallucination and citation accuracy.
- Robustness to spelling errors, transliteration, code-switching, and long documents.
- Latency, throughput, memory use, and cost on the intended hardware.
Test for memorisation and training-data leakage. Publish methodology, data exclusions, known weaknesses, and uncertainty—not only the best scores. For multimodal or document-heavy use cases, compare the LLM against open-source vision-language options rather than assuming text-only pretraining is sufficient; this guide to open-source vision-language models for Indian languages provides a useful adjacent path.
9. Decide whether “from scratch” is the right investment
Build from scratch when you need a new tokenizer, missing language capability, strict data sovereignty, unusual deployment constraints, or foundational research control. Choose an existing model when your advantage lies in data access, workflow design, retrieval, evaluation, or distribution.
For many Indian builders, the strongest plan is staged: establish a baseline with an open model, improve retrieval and evaluation, run continued pretraining on a carefully licensed corpus, and only then commission a full pretraining run if the evidence supports it. A credible model card should report data sources, licences, training compute, intended uses, limitations, safety testing, and evaluation results.
FAQ
Can I build an LLM on one GPU?
Yes, for learning and small models. A single 24GB GPU can support meaningful experiments with compact architectures, gradient accumulation, activation checkpointing, and parameter-efficient methods. It is not a practical route to a competitive general-purpose foundation model.
How much data do I need?
There is no universal number. Data should match model scale, language balance, quality, and objective. A smaller clean corpus can be more valuable than a larger noisy crawl, especially for domain or Indic-language models.
Should I fine-tune an existing model instead?
Usually. Fine-tuning or continued pretraining is faster and cheaper unless the base model lacks essential language coverage, has unsuitable licensing, or cannot meet privacy and deployment requirements.
What should I publish?
Release reproducible evaluation methods, model and tokenizer details, data governance information, limitations, and safety findings. If the dataset cannot be shared, document how others can audit its composition and provenance.