How do you train large language models?

Summary

• Training an LLM involves three key phases-unsupervised pre-training, supervised fine-tuning, and RLHF alignment-with most teams fine-tuning existing models rather than training from scratch.
• Distributed parallel training strategies such as data, tensor, pipeline, and 3D parallelism are essential for scaling LLMs beyond what a single GPU can handle.
• Databricks supports enterprise LLM workflows through Agent Bricks, which unifies fine-tuning, RLHF, prompt optimization, benchmarking, and governance into a single control plane on the Databricks Platform.

How to train large language models

Training a large language model (LLM) is a multi-stage process that transforms raw text into a system capable of generating human-like language. It involves data collection, pre-training, and fine-tuning with alignment.
The cost of frontier pre-training has escalated dramatically. According to Stanford University HAI (Human-Centered Artificial Intelligence), Google's Gemini Ultra cost an estimated $191 million in compute to train and OpenAI's GPT-4 cost an estimated $78 million, up from roughly $900 to train the original Transformer model in 2017. Most teams do not pre-train from scratch. They fine-tune existing models and apply reinforcement learning from human feedback (RLHF) to align outputs with business goals.

What does the LLM training pipeline look like?

The pipeline has three broad phases:

1. Unsupervised pre-training: The model learns grammar, syntax, and world knowledge by predicting the next word across a vast text corpus.
2. Supervised fine-tuning (SFT): Instruction-response pairs teach the model to follow directions and produce useful answers.
3. RLHF and alignment: Reinforcement learning techniques fine-tune the model so outputs align with human values and safety requirements. Approaches like fine-grained human feedback can improve the precision of this alignment stage.

A complementary technique, prompt optimization, steers model behavior through carefully designed prompts without retraining weights. Organizations choose which stages to invest in based on their data, budget, and use case.

How does distributed training scale LLMs?

A single GPU cannot hold hundreds of billions of parameters. Distributed parallel training addresses memory and computation limits by spreading work across devices.

Strategy	How it works
Data parallelism	Replicates the model on multiple devices; each device processes a different data slice.
Tensor parallelism	Shards a single tensor computation across devices (intra-layer).
Pipeline parallelism	Assigns different model layers to different devices (inter-layer).
3D parallelism	Combines data, tensor, and pipeline parallelism in one run.

Choosing the right combination depends on model size, cluster topology, and interconnect speed.

Best practices for LLM training

• Prioritize data quality. Deduplication, filtering, and domain-relevant sourcing have more impact than raw dataset size.
• Start with an existing checkpoint. Fine-tuning a pre-trained model is orders of magnitude cheaper than training from scratch.
• Use parameter-efficient methods. Techniques like LoRA dramatically cut GPU memory needs during fine-tuning.
• Automate evaluation. Combine automated benchmarks (perplexity, task accuracy) with human review to catch regressions early.
• Plan for failure. Checkpointing, gradient clipping, and monitoring for hardware faults are essential at scale.

Where Databricks fits into LLM training workflows

For most enterprises, the highest-value work is fine-tuning, aligning, and continuously improving models on proprietary data. Agent Bricks, the unified control plane to build, run, and govern AI agents across any model, provider, or framework, supports the training stages that matter most:

• Self-improving through fine-tuning and RLHF: Agent Bricks leverages prompt optimization, fine-tuning, and RLHF with human feedback to automatically improve agent accuracy without costly rebuilds.
• Benchmarking on your data: The platform builds benchmarks from your own data and tasks, replacing ad-hoc spot-checks with systematic quality measurement.
• Open and governed: Build with any AI model, OpenAI, Gemini, Llama, Anthropic, while maintaining enterprise governance through access controls, lineage tracking, and policy enforcement.

Model Training is a supporting capability within this ecosystem, connecting training workflows to the Databricks Platform's lakehouse architecture.

FAQs

What hardware is required to train large language models?

NVIDIA GPUs are standard due to their parallel-processing architecture optimized for matrix operations. Training also requires high-speed RDMA networking and large storage capacity. For fine-tuning, parameter-efficient methods like LoRA make A100-class or smaller GPUs viable.

How does pre-training work?

The model learns general language patterns from massive unlabeled text. For decoder-only architectures, the task is causal language modeling, predicting the next token from preceding context.

What is the role of tokenization?

Tokenization converts raw text into subword units the model can process. Tokenizer choice affects vocabulary size, sequence length, and training efficiency.

How are training datasets prepared?

Data quality directly impacts model performance. Preparation includes deduplication, filtering harmful or low-quality content, and normalization to ensure consistency.

What is the difference between pre-training, fine-tuning, and RLHF?

Pre-training teaches next-token prediction on billions of tokens in a self-supervised manner. Fine-tuning trains the model on instruction-response pairs for specific tasks. RLHF aligns outputs with human preferences using reinforcement learning.

How much does it cost to train a large language model from scratch?

Frontier model training runs into tens or hundreds of millions of dollars in compute. Fine-tuning on proprietary data is far cheaper, often achievable for thousands to low six figures depending on model size and dataset.

How does distributed training and model parallelism work?

Work is split across many GPUs using data, tensor, or pipeline parallelism. Most large runs combine all three, known as 3D parallelism, to balance memory, compute, and network bandwidth.

What are common failure modes at scale?

Overfitting, underfitting, gradient instability, hardware failures, and data quality issues are the most frequent problems. Checkpointing and robust monitoring help teams recover quickly.

What optimization techniques are commonly used?

Learning rate scheduling, mixed-precision training, gradient accumulation, and parameter-efficient methods like LoRA are standard. These reduce memory use and speed up convergence.

How do you evaluate LLM quality?

Evaluation combines automated benchmarks, perplexity, accuracy on standard tasks, with human judgment. Agent Bricks supports this by building benchmarks from your own data and evaluating every output against them systematically.

From training to self-improving AI agents

Training an LLM is only the beginning. The ongoing challenge is improving accuracy on proprietary data while maintaining governance. Agent Bricks unifies fine-tuning, RLHF, prompt optimization, benchmarking, and governance into a single control plane built natively into the Databricks Platform, giving agents contextual reasoning and continuous improvement. Explore the finetuning workflow to see how these capabilities come together.