Enterprise AI Analysis
ORBIT-2: Scaling Exascale Vision Foundation Models for Weather and Climate Downscaling
ORBIT-2 introduces a scalable foundation model for global, hyper-resolution climate downscaling, leveraging Residual Slim ViT (Reslim) and the TILES algorithm to achieve unprecedented sequence lengths, exascale throughput, and state-of-the-art accuracy, transforming climate prediction for regional decision-making.
Executive Impact: Unleashing Exascale Climate Prediction
ORBIT-2 redefines the capabilities of AI in climate modeling, enabling global downscaling to ultra-fine resolutions with unmatched efficiency and accuracy. This breakthrough facilitates critical advancements in risk mitigation and policy planning.
Parameters
GPUs Utilized
Sustained Throughput
Global Resolution
Max Sequence Tokens
Peak Accuracy (R²)
Deep Analysis & Enterprise Applications
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Architectural Innovations
Performance & Scaling
Scientific Impact
660x
Speedup vs. Traditional ViT with Reslim
Linearizing Self-Attention with TILES
ORBIT-2 introduces TILES, a tile-wise sequence scaling algorithm that drastically reduces ViT's self-attention complexity from quadratic to linear. By partitioning images into overlapping tiles processed in parallel, TILES enables efficient and scalable downscaling, eliminating the long-sequence bottleneck and processing sequences up to 4.2 billion tokens.
4.1
ExaFLOPS Sustained Throughput (BF16)
Enterprise AI Downscaling Workflow
Data Input & Tokenization
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Adaptive Spatial Compression
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Tile-wise Sequence Processing (TILES)
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Orthogonal Parallelisms (FSDP, Tensor, DDP)
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Residual Slim ViT Training
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Hyper-resolution Downscaling Output
| Feature | Traditional ViT | ORBIT-2 Reslim |
|---|---|---|
| Self-Attention Complexity | Quadratic (O(N²)) | Linear (O(N)) |
| Input Processing | Upsamples coarse resolution | Direct low-resolution, compressed |
| Max Sequence Length | 188K tokens (state-of-the-art) | 4.2 Billion tokens |
| Global Resolution | 50-60 km (Prithvi) | 0.9 km |
| Speedup | Baseline | Up to 660x (Reslim vs. ViT) |
0.99
R² Score for Temperature Downscaling
Transforming Climate Modeling Fidelity
ORBIT-2 enables hyper-resolution, global-scale downscaling, improving climate projection fidelity and resolving fine-scale processes. Its state-of-the-art accuracy (R² of 0.999 for temperature and 0.979 for precipitation) at 7 km resolution provides a valuable tool for supporting climate adaptation and mitigation strategies, and detecting localized extremes.
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Estimated Annual Savings
Hours Reclaimed Annually
Your AI Implementation Roadmap
A phased approach ensures seamless integration and maximum impact for your enterprise.
Phase 1: Discovery & Strategy
Comprehensive assessment of your current infrastructure, data, and business objectives. We define AI use cases, set KPIs, and outline a tailored strategy for ORBIT-2 deployment.
Phase 2: Data Preparation & Model Training
Assistance with data collection, cleansing, and preparation. Fine-tuning of ORBIT-2 models with your specific datasets to optimize for regional and variable accuracy.
Phase 3: Integration & Deployment
Seamless integration of ORBIT-2 into your existing enterprise systems. Rigorous testing and validation to ensure robust, real-time downscaling capabilities.
Phase 4: Monitoring & Optimization
Continuous monitoring of model performance, accuracy, and efficiency. Iterative optimization and updates to adapt to evolving climate data and business needs.
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