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Nolano AI: An Automated Platform for Developing Large-Scale Foundation Models

For AI/ML Researchers at the frontier of foundation modeling research.
The library seeks to abstract away the complexity of HPC, distributed training, tooling, systems and non-AI related engineering overhead involved in getting the foundation model research going.
We allow AI researchers to focus on what matters most: breakthrough innovations in foundation models, by getting their experiment going in minutes, not months.

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What We Abstract Away

Our platform eliminates the boilerplate parts of foundation model research so you can focus on pushing the frontier:
Months of engineering setup for distributed training across clusters - we handle the infrastructure complexity so you can focus on model innovation.
Deep expertise in HPC, CUDA, parallelization strategies, and hardware optimization - no need to become a systems expert to train foundation models.
Redundant implementation of data loaders, training loops, and evaluation pipelines - our battle-tested pipelines handle petabyte-scale datasets efficiently.
Complex configuration management across different experiments and modalities - simplified configuration with intelligent defaults and validation.

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Supported Modalities

Language & Code

Build state-of-the-art language and code models with support for all major architectures.See tutorial

Time Series

Create powerful forecasting models for both univariate and multivariate time series data.See tutorial

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Key Features

  • Custom tokenizers for text & time series
  • BPE, WordPiece, SentencePiece implementations
  • Supports major dense (Encoder/Decoder) and Mixture-of-Experts architectures
  • Qwen, DeepSeek, Chronos, and TimeMoE
  • Multi-node, multi-GPU training out of the box
  • Data, model, and pipeline parallelism
  • Gradient accumulation and mixed precision training
  • Automatic sharding and load balancing
  • High-performance data loading and preprocessing
  • Memory-efficient streaming for large datasets
  • Automatic data shuffling and batching
  • Adaptive learning rate scheduling
  • Gradient clipping and stability monitoring
  • Memory optimization techniques
  • Dynamic loss scaling for mixed precision
  • Custom evaluation metrics support
  • Real-time training metrics and visualization
  • Supports Weights & Biases integration
  • Automatic checkpointing and versioning
  • Integration with Huggingface
  • Dynamic scaling based on workload
  • Optimized for cloud and on-premise deployments

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What You Get

Zero-to-training in under an hour

Start your first experiment immediately with our streamlined workflow

One-command operations

Data preparation, training, evaluation and inference with simple CLI commands

Built-in best practices

Scalable distributed training, hyperparameter transfer and automated optimization

Production-ready

Models ready for deployment without additional engineering overhead
Ready to get started? Check out our Quickstart Guide to begin building your first foundation model.
Nolano AI: Democratizing Foundation Model Research