Energy
Parabole AI Achieves 1,000x Speedup for Industrial Process Optimization With Gurobi
Parabole AI
Objective
Parabole AI helps large enterprises solve complex industrial optimization challenges using causal AI. Causal AI is a branch of AI where algorithms are rooted in the definition of cause-and-effect relationships between inputs and outputs. While classical AI and machine learning focus on prediction, causal AI works to answer the same questions generatively, with clear structural definitions. Parabole sought to pair domain-specific causal modeling with state-of-the-art compute infrastructure using NVIDIA’s accelerated computing platform to transform real-time AI decision-making at industrial scale for its customers with speed, context, and confidence.
Customer
Parabole AI
Partner
Gurobi
Use Case
Simulation / Modelling / Design
Products
Key Takeaways
Faster Causal AI Analysis
- Overall causal analysis achieved a 1,000x speedup (from approximately 10 hours to under 1 minute) in moving from CPU to NVIDIA GH200 Grace Hopper™ Superchip.
Optimized Models for Linear Programming
- The process of building and running optimization models using Mixed Integer Linear Programming (MILP) has seen a 1.5x to 2.5x speed or efficiency improvement.
Enhanced Data Processing and Graph Generation
- Textual data processing saw up to a 6.8x speedup, and causal graph generation and analysis showcased a 2.5x faster execution time.
The Need to Optimize for Scale
In complex industrial environments, it can be extremely difficult to make optimization both scalable and actionable in situations where data is noisy, expertise is fragmented, and objectives are fluid.
Traditional mathematical optimization engines such as Gurobi are powerful, but they require clearly defined variables, constraints, and objectives, which is often not feasible when qualitative data, cross-functional interactions, and external forces like macroeconomics are in play. Parabole TRAIN continuously ingests cross-functional data, validates hypotheses, and refines mathematical models for execution by an optimization engine—enabling dynamic, high-speed optimization at scale. Such optimization at scale needs full-stack accelerated computing.
Parabole’s TRAIN Platform Offers Causal-First Approach to Optimization
Making real-time decisions in large, complex industrial settings is tough—especially when data comes from many different systems. That’s why Parabole built the TRAIN platform, a smart solution that fits right into existing workflows without requiring major changes.
Instead of trying every possible option (which takes massive computing power), TRAIN identifies the key factors that truly drive outcomes. It narrows down the choices before running any calculations—making decisions faster and more efficient.
To do this, TRAIN blends science, company knowledge, and real-world data, along with expert input. This helps it test smarter scenarios, reduce trial and error, and cut overall computing time by over 10x.
Parabole AI
Decision Space Optimization With AI
Using contextual combined causal graphs, Parabole achieved 10x improvements in speed and relevance to reduce the load on optimization models.
To meet the demands of near-real-time performance, Parabole parallelized core causal modeling operations into thousands of large computation batches. For this, it harnessed NVIDIA’s accelerated computing platform, which provided:
- Thousands of NVIDIA® CUDA® cores for massively parallel workloads
- High-speed memory access and NVLink™ chip-to-chip (C2C) CPU-GPU communication for rapid data movement
- A scalable infrastructure that handles the increasing model complexity without latency spikes.
This synergy of causal intelligence, machine learning, and high-performance computing enables TRAIN to deliver continuous, context-aware optimization—not just solving business problems, but solving them very quickly, accurately, and at scale.
“By blending cross-functional expertise with NVIDIA GPU-accelerated computation, we’re not just solving equations—we’re solving real business problems, continuously and contextually at a near-real-time speed.”
Sandip Bhaumik
CTO, Parabole AI
Achieving 1,000x Speedup in Causal Model Generation
By shifting from CPU-based to GPU-accelerated computation, Parabole achieved a 1,000x speedup in causal model generation and analysis.
This leap in performance was made possible by optimizing platform modules to fully use NVIDIA’s accelerated computing platform, with benchmark tests spanning multiple NVIDIA compute architectures—from the NVIDIA Ampere architecture to the NVIDIA GH200 Grace Hopper Superchip.
Key performance gains in moving from the Ampere architecture to GH200 included:
- Textual Data Processing: 4.5x–6.8x speedup
- Mathematical Model Generation and Execution (MILP—Mixed Integer Linear Program Optimization): 1.5x–2.5x improvement
- Causal Graph Generation and Analysis: 2.5x faster execution
Overall causal analysis time reduced from ~10 hours to under 1 minute in moving from CPU (64 vCPU x86) to GH200.
These improvements are not just technical feats—they enable real-time decision-making at industrial scale. In one deployment at a large equipment manufacturer, TRAIN’s causal optimization AI agent was used to generate minute-level production plans across a complex plant with multiple machines and product types. The system:
- Optimized across multiple KPIs, including machine health, maintenance cycles, quality, and waste
- Avoided operational sequences with high risk of downtime, using embedded causal knowledge of machines and operators
- Adapted quickly to sudden disruptions (e.g., machine failures or demand shifts) via easy-to-use scenario reconfiguration
- Reduced weekly production planning problem involving 40+ million decision points to a few minutes’ task, previously infeasible with brute-force methods
Intervention Summary Task Performance
- 7,200 seconds on a 64-core x86 vCPU
- Completed in 7.4 seconds on GH200
- 973x speedup on NVIDIA GPU
Hypothesis Generation Performance
- 8,500 seconds on a 64-core x86 vCPU
- Completed in 8.9 seconds on GH200
- 955x speedup on NVIDIA GPU
The Future of AI-Driven Industrial Optimization
The integration of Parabole’s TRAIN platform, NVIDIA accelerated computing, and the Gurobi Optimization solver is enhancing real-time industrial processes. Together, this collaboration delivers:
- Optimized decision space via causal variable selection and Bayesian refinement
- Inclusion of qualitative factors for more realistic modeling
- Root-cause-based recommendations that drive real-world action
- Real-time adaptability to changing operational conditions
- Rapid plan generation—in under 5 minutes
- Explainable outputs through transparent causal hypotheses
- A user-friendly interface enabling easy scenario reconfiguration
This causal, AI–driven framework enables organizations to move beyond traditional, rigid optimization models—delivering faster, more interpretable, and more adaptive decision-making at industrial scale. This novel optimization enabled 5% natural gas savings at a large refinery through real-time energy rule generation and delivered a 66% overall equipment effectiveness (OEE) boost—a metric in manufacturing for machine availability, production speed, and product quality in large manufacturing production lines.
Looking ahead, Parabole is working to expand its methodology into additional domains such as energy and logistics, while enhancing AI model robustness and causal inference accuracy through deeper integration of external and internal data.
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