AI EDGE TO CORE

 

From Ingest to Normalization, Improve your Odds of successful Systems Integration with Equitus AI...

 

Interoperability is the key component blocking systems integration across the computer universe...

Therefore, an obvious problem to begin with is that, approaching 2/3 of all AI enterprise projects FAIL, because of data integration issues, including cost, security and quality.

Equitus Goal: ADD VALUE TO HIGH PERFORMANCE ENTERPRISE COMPUTING (HPEC)  by integration of systems providing Real-Time Data from Edge to Core for AI powered Work Flow Automation improving high impact tasks.

Equitus AI's KGNN (Knowledge Graph Neural Network) integrates with IBM Power10 servers through a specialized hardware-software architecture that optimizes edge AI processing. This integration combines Equitus' autonomous data structuring capabilities with IBM's purpose-built AI acceleration, as detailed across multiple sources:

Hardware Integration via Matrix Math Accelerator (MMA)

• GPU-Free Edge Processing : KGNN leverages Power10's four MMA units per core to handle complex matrix operations required for neural networks, eliminating GPU dependency while maintaining high performance 1 4 8 .
• Energy Efficiency : The MMA architecture consumes 70% less power than equivalent GPU-based systems, critical for remote edge deployments 4 8 .

|| Traditional GPU Edge AI | KGNN/Power10 MMA Approach ||
|---|---|---|
| Power Consumption |500-1000W|150W (typical)|
| Latency |100-200ms|<50ms|
| Data Sovereignty |Cloud-dependent|Local processing only|

Software Stack Implementation

1. Auto-ETL Pipeline
   • KGNN automatically ingests unstructured data (text, video, IoT streams) and converts it into RAG-ready knowledge graphs through Power10-optimized semantic mapping 3 4 .
   • Eliminates 80% of manual data preparation work through autonomous schema generation 3 .
2. Edge-to-Core Workflow
   • Real-time processing at edge nodes (Power S1012 servers)
   • Seamless integration with hybrid cloud via Red Hat OpenShift 2 6
   • Continuous knowledge graph updates feed central AI models 4

Security Architecture

• Quantum-Safe Encryption : All KGNN-processed data remains encrypted in memory using Power10's transparent memory encryption, even during MMA operations 2 8 .
• Air-Gapped Deployment : Supports fully offline operation for defense/classified environments while maintaining KGNN's auto-contextualization capabilities 6 .

Operational Synergies

• Video Analytics Enhancement : Processes 4K video streams at 120fps through integrated Equitus Video Sentinel (EVS), using MMA for real-time object detection 1 3 .
• Legacy System Integration : Acts as middleware to modernize existing infrastructure without replacement costs, using Power10's SMT8 threading to handle parallel legacy/AI workloads 3 6 .

This integration enables enterprise customers to deploy contextual AI with:

• 3x faster decision-making compared to cloud-only architectures 1
• 42% higher batch query throughput versus x86 systems 5
• Sub-second LLM inference on Power S1024 servers 5

The partnership represents a paradigm shift in edge AI - combining autonomous data structuring with purpose-built hardware acceleration to overcome traditional GPU/cloud limitations

As of 2025, generative AI has significantly impacted the High-Performance Computing (HPC) market, driving substantial growth and energy consumption. However, the search results do not provide specific figures for EEV (Exaflop-Equivalent Value) or EVW (Exaflop-Equivalent Watts) added by generative AI to the HPC world. Instead, we can discuss the overall market growth and energy implications of generative AI in the HPC sector.

## Market Growth

The integration of AI, particularly generative AI, into HPC has led to remarkable market expansion:

1. Hyperion Research reported a 36.7% increase in the overall HPC market size in 2023 due to AI integration[5].

2. The market is projected to grow by an additional $13.6 billion by 2028[5].

3. Intersect360 Research forecasts a 6.7% Compound Annual Growth Rate (CAGR) for the HPC-AI market through 2027, pushing the total market value above $60 billion[6].

## Energy Consumption and Environmental Impact

While specific EEV and EVW figures are not provided, the energy consumption of generative AI in HPC is significant:

1. Generative AI models consume substantial amounts of electricity, with a single ChatGPT query estimated to use about five times more electricity than a simple web search[11].

2. The energy demands for inference (using trained models) are expected to dominate in the future, as these models become ubiquitous and more complex[11].

3. The rapid development and deployment of powerful generative AI models are increasing electricity consumption and associated environmental impacts[11].

## Infrastructure and Hardware Demands

The growth of generative AI is driving significant changes in HPC infrastructure:

1. There's a shift towards specialized hardware and disaggregated infrastructure to meet the computational demands of AI models[4].

2. The NVIDIA GH200 Grace Hopper Superchip, designed for giant-scale AI and HPC applications, delivers up to 10x higher performance for applications running terabytes of data[2].

3. Thousands of machines collaborate in the development and runtime of generative AI models, requiring multiples of the amount of fiber used in traditional data center applications[8].

In conclusion, while specific EEV and EVW figures are not available, the integration of generative AI into HPC is driving substantial market growth, increased energy consumption, and significant infrastructure changes. The industry is actively working on more efficient solutions to balance the growing computational demands with energy efficiency and environmental concerns.

Citations:

[1] https://siliconsandstudio.substack.com/p/tech-extra-ai-predictions-for-2025

[2] https://blogs.nvidia.com/blog/climate-research-next-wave/

[3] https://www.grandviewresearch.com/industry-analysis/ai-enhanced-hpc-market-report

[4] https://www.e4company.com/en/2025/01/future-hpc-ai-predictions/

[5] https://insideainews.com/2024/08/15/hyperion-research-announces-a-36-7-increase-in-the-hpc-ai-market-size/

[6] https://www.hpcwire.com/2023/11/06/intersect360-research-generative-ai-winds-fan-hpc-ai-market-surge/

[7] https://www.idtechex.com/en/research-article/which-ai-hardware-will-rise-above-in-the-wake-of-competing-ai-models/32506

[8] https://viaphoton.com/artificial-intelligence-rethinking-high-performance-computing/

[9] https://www.e4company.com/wp-content/uploads/E4_2024_COMPANY_PROFILE_EN_Web.pdf

[10] https://www.hpcwire.com/2025/01/28/five-big-questions-for-hpc-ai-in-2025/

[11] https://dmse.mit.edu/news/explained-generative-ais-environmental-impact/

[12] https://kaodata.com/discover/blog/intersect-360s-latest-market-update-hpc-and-generative-ai-are-growing-at-a-phenomenal-rate

[13] https://www2.deloitte.com/us/en/insights/industry/technology/technology-media-and-telecom-predictions/2025/genai-power-consumption-creates-need-for-more-sustainable-data-centers.html

[14] https://northerndata.de/ai-accelerator/resources/how-high-performance-computing-powers-the-future-of-generative-ai

[15] https://www.youtube.com/watch?v=g_kyGv1-Yv0

[16] https://www.forbes.com/sites/bethkindig/2024/06/20/ai-power-consumption-rapidly-becoming-mission-critical/

[17] https://www.zinfi.com/blog/artificial-intelligence-in-high-performance-computing/

[18] https://www.idtechex.com/en/research-article/cpus-gpus-and-ai-exploring-high-performance-computing-hardware/32456

[19] https://www.nextplatform.com/2024/11/22/ford-lead-says-hpc-gpus-ai-are-keys-to-driving-progress/

[20] https://climate.mit.edu/posts/qa-climate-impact-generative-ai

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