LLM Training FAQ: How AI Models Are Built From Scratch
Frequently asked questions about training large language models. Compute costs, data requirements, and the training process explained.
How long does it take to train a large language model?
Training time depends on model size and available compute. A 7B parameter model (like Llama 2 7B) takes 2-4 weeks on a cluster of 64-128 GPUs. A 70B parameter model takes 2-3 months on 512-2048 GPUs. Frontier models like GPT-4 or Claude reportedly take 3-6 months on 10,000-25,000 GPUs. The limiting factor is usually GPU availability and budget rather than algorithmic constraints. Training is typically done in phases: pre-training (the longest phase, learning from raw text), supervised fine-tuning (days to weeks), and alignment training via RLHF or DPO (days to weeks). Checkpoints are saved regularly so training can resume if hardware fails.
How much does it cost to train an AI model?
Costs scale dramatically with model size. Fine-tuning a 7B model on custom data: $100-1,000 using cloud GPUs. Training a 7B model from scratch: $100,000-500,000. Training a 70B model from scratch: $2-10 million. Training a frontier model (GPT-4, Claude 3 class): $50-100+ million. These costs include GPU rental, electricity, engineering time, and data processing. NVIDIA H100 GPUs cost approximately $2-3 per hour on cloud platforms. A training run using 10,000 H100s for 3 months at $2.50/hour costs roughly $54 million in compute alone. Costs are declining as hardware improves and training techniques become more efficient.
What data is used to train language models?
Language models are trained on diverse text data from the internet and curated sources. Common sources include: web pages from Common Crawl (the largest source, billions of pages), Wikipedia (high-quality knowledge in hundreds of languages), books and academic papers, code repositories from GitHub, social media and forums (Reddit, Stack Overflow), and news articles. The data undergoes extensive processing: deduplication removes redundant content, quality filtering removes spam and low-quality text, toxicity filtering removes harmful content, and PII scrubbing removes personal information. Training data composition significantly affects model behavior — more code data improves coding ability, more scientific text improves reasoning.
What hardware is needed to train LLMs?
Training large models requires specialized hardware. GPUs: NVIDIA dominates with H100 and B200 GPUs, each with 80GB+ high-bandwidth memory. Training clusters use thousands of GPUs connected via high-speed networking. TPUs: Google's Tensor Processing Units are an alternative, used to train Gemini and PaLM. Networking: InfiniBand or NVLink interconnects enable GPUs to communicate at 400-900 Gbps. Fast networking is critical because training is distributed across many GPUs. Storage: petabytes of fast storage (NVMe SSDs) for training data and checkpoints. Cooling: GPU clusters generate enormous heat, requiring industrial cooling systems. Cloud providers (AWS, GCP, Azure, CoreWeave) offer GPU clusters for rent, avoiding the $100M+ capital cost of building custom infrastructure.
What is fine-tuning and when should I use it?
Fine-tuning adapts a pre-trained model to a specific task or domain by training on additional data. Use fine-tuning when: you need the model to follow a specific output format consistently, you have domain-specific knowledge the base model lacks (medical, legal, financial), you want to change the model's tone or personality, or you need better performance on a narrow task. Methods include: full fine-tuning (updating all weights — expensive but thorough), LoRA (updating small adapter matrices — 10-100x cheaper), QLoRA (LoRA with quantized base model — runs on consumer GPUs), and prompt tuning (learning soft prompt embeddings). Start with LoRA on 100-1,000 high-quality examples before considering full fine-tuning.
What is RLHF and why does it matter?
RLHF (Reinforcement Learning from Human Feedback) is the training technique that transforms a raw language model into a helpful assistant. The process has three stages: first, supervised fine-tuning on human-written examples of ideal responses. Second, training a reward model on human preference comparisons (which response is better?). Third, optimizing the language model to maximize the reward model's scores using reinforcement learning (PPO algorithm). RLHF is what makes ChatGPT, Claude, and Gemini helpful and safe rather than just autocomplete engines. Without RLHF, models tend to produce generic, sometimes harmful, and poorly-structured outputs. DPO (Direct Preference Optimization) is a simpler alternative that skips the reward model.
Can I train my own language model?
Yes, but the approach depends on your resources. Consumer hardware (1 GPU, 24GB VRAM): fine-tune models up to 13B parameters using QLoRA. Cost: $0 beyond hardware. Tools: Hugging Face Transformers, Axolotl, LLaMA Factory. Cloud budget ($1,000-10,000): fine-tune 70B models or train small models (1-3B) from scratch using LoRA. Use Lambda Labs, RunPod, or Vast.ai for affordable GPU rental. Startup budget ($100K-1M): train a competitive 7B model from scratch. Use cloud GPU clusters with custom training pipelines (Megatron-LM, DeepSpeed). Most practitioners should fine-tune existing open models (Llama, Mistral, Qwen) rather than training from scratch — it is 100-1000x cheaper and produces comparable results for most applications.
How do you evaluate if training worked?
Evaluation is critical and should happen continuously during training. Loss curves: training loss should decrease smoothly. Sudden jumps indicate instability. Validation loss should decrease alongside training loss — if it increases while training loss drops, the model is overfitting. Benchmark scores: run standard benchmarks (MMLU, HumanEval, GSM8K) periodically. Scores should improve with training steps. Human evaluation: have humans interact with the model and rate responses. Automated metrics miss nuances that humans catch. A/B testing: compare the new model against a baseline on real user queries. Chatbot Arena ELO ratings are the gold standard for overall quality. Red-teaming: test for safety issues, biases, and failure modes. A model that scores well on benchmarks but fails safety tests should not be deployed.