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Training a foundation model from scratch takes more than a large GPU budget. It means assembling and governing a broad training dataset, designing a model and training strategy, running the work across suitable hardware, and evaluating the result. Frontier-scale development requires substantial capital and specialized teams; many organizations can build useful applications more practically by adapting an existing pretrained model.
What counts as a foundation model?
Stanford’s Center for Research on Foundation Models (CRFM) defines a foundation model as one trained on broad data—generally with self-supervision at scale—that can be adapted to a wide range of downstream tasks. The defining idea is reuse: the base model is intended to support multiple applications rather than just one narrowly specified task.
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That definition covers a range of models, not a single size or training budget. Apple’s 2025 technical report, for example, describes an approximately 3-billion-parameter on-device model alongside a server model. That is an example of designing models for different deployment environments, not evidence that an on-device model has the same capability or training requirements as a frontier general-purpose model.
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A training run is only one part of the job. Teams must decide what they are building, prepare data that suits that goal, choose a model and training approach, execute the run, and check whether the resulting model is useful and safe for its intended applications.
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- Set the scope. Define the capabilities and uses the model needs to support. A broad, reusable base model is a different undertaking from a model intended for a focused domain or deployment environment.
- Build and govern the data pipeline. Source data, assess its suitability, filter and curate it, and review privacy and poisoning risks before and during training.
- Design the model and training plan. Choose an architecture and allocate data and compute to meet the training objective. The model, dataset, and compute budget affect one another.
- Run training on a coordinated system. Distribute work across accelerators and keep the hardware, interconnect, software, and parallelism strategy operating together.
- Evaluate and prepare for use. Test performance, limitations, and safety for the intended applications; adaptation or deployment work follows the base-model training.
Stanford CRFM describes this as co-design: algorithms, models, software, and hardware—including parallelism strategies—must be considered together. A cluster with powerful accelerators does not replace the people and systems needed to make a training run effective.
What data is needed?
The amount varies widely by model and objective. The U.S. Government Accountability Office (GAO) said in its October 2024 report that generative-AI training data can range from millions to trillions of data points. That range is not a recipe or a minimum: volume alone does not show whether a dataset is useful, representative, deduplicated, safe, or appropriate for a particular model.
Data work includes more than collecting a large corpus. GAO describes filtering and curation to reduce harmful content, privacy evaluations, and poisoning risks when data is scraped from public sources. Commercial developers interviewed by GAO often disclosed only high-level dataset descriptions, such as publicly available internet information. “Publicly available” by itself does not establish the legal status of a particular dataset or settle whether it may be used for training.
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Because the corpus affects what the model learns, data engineering and governance are core parts of the project—not a one-time download before the technical work begins.
How many GPUs does it take?
There is no defensible universal GPU count. The answer depends on the target model and dataset, training duration, parallelism strategy, hardware utilization, and the cluster’s interconnect, as well as whether the goal is educational, domain-specific, or frontier-scale. A GPU count without those details does not tell you how long training will take or what it will cost.
Compute, data, and model size interact. OpenAI’s 2020 scaling-law study reported empirical power-law relationships between language-model loss and model size, dataset size, and training compute, framing training as an allocation problem under a fixed compute budget. DeepMind’s Chinchilla study examined more than 400 language models, from 70 million to over 16 billion parameters, trained on 5 billion to 500 billion tokens. In that study’s compute-optimal setting, the authors proposed increasing model size and token count in equal proportions. Their 70-billion-parameter Chinchilla model used four times more data than Gopher at the same compute budget and outperformed several larger models on the reported benchmarks. These findings describe the studies’ setups; they are not a universal rule for every architecture, modality, or current training strategy.
OpenAI’s 2018 analysis reported that the compute used by the largest training runs had doubled every 3.4 months from 2012 in the historical period it examined. That is a dated trend from that analysis, not a current forecast or a GPU purchasing guide.
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How much does it cost?
Public training-cost figures are estimates, not usually disclosed invoices. Stanford HAI’s 2024 AI Index, drawing on Epoch AI estimates, modeled costs using factors including training duration, hardware type and quantity, utilization, and cloud-compute rental prices. The historical estimates below are model-specific; they are not a present-day quote or a universal budget.
| Model and training year | Estimated training cost | Estimate attribution |
|---|---|---|
| Original Transformer, 2017 | About $900 | Stanford HAI’s 2024 AI Index, using Epoch AI estimates |
| RoBERTa Large, 2019 | About $160,000 | Stanford HAI’s 2024 AI Index, using Epoch AI estimates |
| GPT-4, 2023 | About $78 million | Stanford HAI’s 2024 AI Index, using Epoch AI estimates |
| Gemini Ultra, 2023 | About $191 million | Stanford HAI’s 2024 AI Index, using Epoch AI estimates |
These figures do not establish a complete project budget. They should not be read as independently audited expenditures or assumed to include all research, data acquisition, failed runs, post-training, staffing, inference, or deployment. The sources do not provide a comparable 2026 accelerator rental quote or a specified cluster configuration from which to calculate one. A useful estimate needs a defined model and workload, hardware and provider, expected utilization, training duration, and the scope of costs being counted.
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The OECD identifies compute, data, and specialized AI talent as central resource requirements for foundation-model development. In practice, the work spans several specialties:
- Data engineering and governance: building pipelines, curating datasets, and addressing privacy and data-quality risks.
- Model research and optimization: selecting or developing architectures and training methods appropriate to the objective.
- Distributed-systems engineering: coordinating training across accelerators and managing parallelism and communication.
- Hardware and cluster operations: configuring, monitoring, and maintaining the systems used for training.
- Evaluation and security: measuring capabilities and limitations and assessing risks for intended uses.
- Domain and product expertise: deciding what the model must do and whether its behavior is useful in the application.
The OECD notes that cost and complexity have limited foundation-model development to well-capitalized companies and organizations. That is especially relevant to building broad models from scratch; it does not mean every useful AI application requires a frontier-scale training operation.
Should you train from scratch or adapt an existing model?
The practical choice depends on whether you need a new reusable base model or an application built on capabilities that already exist. The OECD notes that open foundation models can enable fine-tuning and deployment without the developer needing the original pretraining compute and dataset.
| Path | What you build | Resource burden | When it fits |
|---|---|---|---|
| Train a frontier model from scratch | A broadly capable new base model | Highest: large datasets, accelerator clusters, specialized teams, and substantial capital | When there is a compelling reason and the organization has the resources to create a new base model |
| Train a smaller or specialized model | A narrower model or one focused on a particular domain | Lower than frontier work, but still requires suitable data, compute, expertise, and evaluation | When the scope is focused and a new model is justified for that task |
| Adapt an existing open pretrained model | A fine-tuned or otherwise adapted model | Avoids the original full pretraining burden; still requires domain data, evaluation, and operational work | When the goal is a domain application rather than creating a new general-purpose base model |
Training a foundation model and fine-tuning one are different jobs. If the desired result is a domain application, adapting an existing pretrained model may avoid repeating the full pretraining run while still requiring careful data work, evaluation, and deployment planning.
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