Provides a decentralized compute protocol that enables users to train and fine-tune AI models using a scalable SDK and access to exclusive GPU resources. Reduces machine learning training costs by up to 80% while ensuring data privacy, transparent model tracing, and instant autoscaling to eliminate idle compute time.
Funding
$2.8M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Training and fine-tuning AI models often requires significant computational resources, leading to high costs and limited access to powerful GPUs, especially for smaller teams and individual developers. Securing these resources can be challenging, and traditional cloud solutions may not offer the most cost-effective or flexible options.
Solution
Lumino provides a decentralized compute protocol that enables users to train and fine-tune AI models with a scalable SDK and access to a global network of GPUs. The platform aims to reduce machine learning training costs by providing access to GPUs that may not be available elsewhere, while also ensuring data privacy and transparent model tracing. Lumino's infrastructure autoscales instantly to eliminate idle compute time, allowing users to pay only for the resources they consume during training jobs. By offering pre-configured templates and support for custom models, Lumino simplifies the deployment process and accelerates the development cycle for machine learning projects.
Target Audience
The primary target audience includes machine learning engineers, AI developers, and researchers who require cost-effective and scalable GPU resources for training and fine-tuning AI models.
Features
- Scalable SDK for developing and training ML models
- Access to a global cloud of GPUs, including A100s
- Pay-per-training job pricing model, eliminating costs for unused compute
- Instant autoscaling to dynamically adjust compute resources based on demand
- Pre-configured templates for common machine learning tasks
- Support for custom models and deployment in seconds
- Cryptographically verified proofs for transparent and auditable model tracing
- Data privacy controls, allowing users to manage their data on their terms