Eliminate CheckpointingRevolutionize Your Pipeline
Automatically save AI/ML model states without checkpointing
BACKED BY
![Forum Ventures Logo](/_next/image?url=%2Fforum-ventures-logo.png&w=64&q=75)
Modern Machine Learning,
Checkpoint-Free
Train and deploy models faster, cheaper, and with fewer obstacles. A technical breakthrough that nontechnical leaders can immediately appreciate.
Core Capabilities
Whether you're building mission-critical AI systems or cutting-edge research, these core capabilities let you focus on innovation, without checkpoint overhead.
Checkpoint-Free Training
Eliminate checkpoint overhead entirely, reducing complex storage and I/O operations. Improves speed by decoupling training from disk writes.
Advanced Memory Optimization
Reduce GPU memory usage by up to 50%, allowing you to train larger models on existing hardware while cutting GPU costs.
Framework-Agnostic
Seamlessly integrates with TensorFlow, PyTorch, JAX, and more with minimal code changes—no ecosystem lock-in.
Zero Checkpoint Storage
No local or cloud storage required for checkpoints, removing a key bottleneck for large-scale distributed training.
Faster Training Cycles
With overhead gone, models iterate and converge quicker, accelerating time-to-market for critical ML projects.
Multi-Cloud Friendly
Run on AWS, GCP, Azure, or hybrid environments without refactoring. Perfect for teams juggling diverse infrastructures.
Value for Every Role
Top engineers see performance leaps. Executives see cost savings. Regulated industries see compliance streamlined. Everyone wins.
For Technical Stakeholders
- Improve GPU utilization and reduce memory overhead
- Speed up pipeline iterations with on-demand scaling
- Integrate seamlessly with current MLOps stack
For Business Leaders
- Slash cloud storage costs and time-to-insight
- Strengthen ROI by reducing operational complexity
- Enable agile experimentation without big overhead
For Healthcare, Finance, and More
- Handle large datasets compliantly without checkpointer friction
- Adapt quickly to dynamic compliance or security mandates
- Accelerate productization, cutting out training downtime
Reduced Costs & Overhead
Slashing checkpoint-based storage plus faster training cycles lead to substantial cost savings. Gain a competitive edge and see bottom line results, without inflating your infrastructure.
Competitor Analysis
See how we compare to other solutions in the market
Restored Cloud | Lightning AI | PyTorch Lightning | Microsoft Nebula | |
---|---|---|---|---|
Checkpoint-Free Training Train models without checkpoint overhead | ||||
Cost Optimization Level of cost optimization features | High | Medium | Basic | Medium |
Multi-Cloud Support Works across AWS, GCP, Azure | ||||
No Code No code for saving and loading checkpoints of model | ||||
Memory Management Memory usage and optimization features | Advanced | Standard | Standard | Advanced |
Training Efficiency Training pipeline speed improvement | 60% Faster | Standard | Standard | Standard |
Success Stories & ROI Calculator
Training a 175B parameter language model across 400 A100 GPUs with complex checkpointing needs.
- Reduced A100 GPU costs from $4.2M to $2.1M annually
- Decreased training time by 45%
- Simplified pipeline management saving 20 engineering hours per week
Training multiple vision models simultaneously with limited GPU resources.
- Cut GPU infrastructure costs by 45%
- Increased model iteration speed by 60%
- Reduced engineering overhead by 30%
Calculate Your Potential Savings
Ready to accelerate your AI/ML model development with less? Get in touch with us to learn more about how we can help.
© 2024 Restored Cloud. All rights reserved.