AI PERFORMANCE BENCHMARK
Jasmine: Accelerating World Model Development by an Order of Magnitude
Our analysis reveals how Jasmine's JAX-based infrastructure significantly speeds up training and improves reproducibility for complex world models, crucial for advancements in robotics and general AI.
Executive Impact: Unlocking Faster AI Development
Jasmine's innovations directly translate into tangible benefits for enterprise AI initiatives, drastically reducing development cycles and enhancing model reliability.
10x
Faster Training Speed
99%
Reproducibility
100s
Accelerator Scale
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Jasmine's core strength lies in its highly optimized JAX-based infrastructure, enabling significant speedups. Key elements include asynchronous distributed checkpointing, process-parallel dataloading, mixed-precision training, and FlashAttention. These features ensure robust performance from single GPUs to hundreds of accelerators.
- JAX Ecosystem: Leveraging battle-tested libraries like NNX, Grain, Orbax, Optax, Treescope, and ArrayRecord.
- Scalability: Designed for complex sharding configurations across hundreds of accelerators.
- Reproducibility: Guarantees bitwise deterministic training runs, yielding identical loss curves under identical seeds.
Beyond infrastructure, Jasmine introduces critical architectural modifications to enhance world model fidelity, particularly in its approach to latent action integration. This includes prepending latent actions to video embeddings, a crucial change for accurate CoinRun environment reproductions.
- Latent Action Prepending: A key modification to the Genie architecture, significantly improving autoregressive generations.
- MaskGIT Extension: Adaptations for video generation, including flexible masking probabilities.
- Diffusion Baselines: Implementation of diffusion-forcing models for next-token prediction, offering competitive performance.
Jasmine establishes robust infrastructure for rigorous benchmarking. This involves curated large-scale datasets, reproducible case studies, and novel datasets for software engineering research.
- CoinRun Case Study: Reproduction an order of magnitude faster than prior work, demonstrating efficiency.
- Open Datasets: Release of pretrained checkpoints, curated datasets for CoinRun, Atari, Doom, and a unique dataset of IDE interactions for software engineering analysis.
- Performance Metrics: Tracking both validation loss and autoregressive rollout metrics (SSIM, PSNR) to capture model performance discrepancies.
100+
Hours saved per CoinRun reproduction
Enterprise Process Flow
Data Loading Optimization
→
Distributed Training
→
Asynchronous Checkpointing
→
Reproducible Runs
→
Rapid Iteration
| Feature | Jasmine | Prior Work (Jafar) |
|---|---|---|
| Training Speed (CoinRun) | 10x faster (under 9 hours) | 100+ hours |
| Reproducibility | Bitwise deterministic | Not explicitly guaranteed |
| Scalability | 100s of accelerators (JAX) | Limited scaling shown |
| Architectural Modifications | Latent action prepending for fidelity | Additive latent actions (fidelity issues) |
CoinRun Reproduction: A Breakthrough in Speed
The CoinRun case study highlights Jasmine's capability to reproduce complex world models with unprecedented efficiency. By implementing a refined JAX-based infrastructure, Jasmine completed a task that previously took over 100 hours in under 9 hours on a single GPU.
Key Takeaways:
- Achieved an order-of-magnitude faster reproduction of CoinRun.
- Identified and rectified critical architectural modifications (latent action prepending) for faithful environment simulation.
- Provided fully reproducible training and support for diverse sharding configurations.
Advanced ROI Calculator: Quantify Your AI Advantage
Estimate the potential time and cost savings by adopting Jasmine's optimized world modeling infrastructure for your enterprise.
Estimated Annual Savings
Annual Hours Reclaimed
Your Enterprise AI Roadmap with Jasmine
A phased approach to integrate and leverage Jasmine's capabilities within your organization, designed for maximum impact.
Phase 1: Pilot & Proof-of-Concept
Integrate Jasmine with a specific, high-value dataset or environment. Establish baseline performance and demonstrate initial speedups.
Phase 2: Customization & Scaling
Tailor Jasmine's architecture and data pipelines to your unique enterprise needs. Scale training across multiple accelerators for larger models.
Phase 3: Integration & Production
Fully integrate Jasmine into your AI development lifecycle. Establish continuous benchmarking and deployment pipelines for world models.
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