The "Power-up On Prem" capability of Equitus PowerGraph (KGNN) on IBM Power 11 systems is rooted in a proprietary software layer that is deeply optimized for the Power hardware's dedicated AI silicon. This combination allows highly regulated sectors (financial, healthcare, gov/def) to run advanced Generative AI workloads with cloud-level performance and on-premise security, satisfying data sovereignty regulations.
The enhancement is achieved by leveraging the Matrix Math Accelerator (MMA) and the Spyre AI Accelerator as dedicated, efficient AI processing units for the software layer of the Knowledge Graph Neural Network (KGNN).
How Equitus PowerGraph (KGNN) Optimizes AI on IBM Power 11: Webinar link
Equitus PowerGraph addresses the four key areas (Speed, Integration, Cost, Security) by solving the traditional AI deployment paradox: you shouldn't have to move your mission-critical data to the cloud to apply modern AI.
1. Speed & Performance (Leveraging MMA and Spyre)
Equitus PowerGraph's core function is to generate and query a live, semantic Knowledge Graph. This is accelerated by the specialized AI chips in the Power 11 architecture.
| Hardware Feature | Function within PowerGraph | Impact on Speed |
| Matrix Math Accelerator (MMA) | In-Core Inferencing: The MMA is a dedicated unit within each Power 11 CPU core. PowerGraph uses it to perform the fundamental matrix multiplication required for AI model inference (predictive AI, vector search) at the point of data, directly within the CPU. | Ultra-Low Latency: Enables real-time decisions (e.g., fraud scoring or clinical alert generation) by eliminating data movement outside the processor core. |
| IBM Spyre AI Accelerator | Generative AI Offload: Spyre is an optional, high-throughput PCIe card accelerator optimized for large language models (LLMs) and Generative AI inference. PowerGraph utilizes Spyre to execute complex RAG (Retrieval-Augmented Generation) queries against the massive knowledge graph. | High Throughput & Scale: Provides scalable, energy-efficient performance for large-scale Generative AI applications (e.g., Natural Language Query (NLQ)) that is comparable to high-power cloud GPUs. |
| Platform Support (AIX, IBM i, Linux) | Equitus is engineered to run natively on the operating systems that host the client's mission-critical applications. | Zero Overhead: Ensures the AI acceleration is immediately available to the core business data systems without requiring costly and complex virtualization or emulation layers. |
2. Security & Compliance (On-Prem Cloud Capability)
For regulated industries, the primary pain point of cloud AI is the loss of data control. PowerGraph and Power 11 solve this by bringing the AI stack inside the client's protected perimeter.
Data Sovereignty: The entire AI stack—from the source data to the resulting Generative AI insight—remains on-premises, eliminating the risk of data exfiltration associated with public cloud processing.
Built-in Encryption: IBM Power 11 provides hardware-accelerated, transparent memory encryption and Quantum-Safe Cryptography (QSC). PowerGraph leverages this platform security to protect the sensitive data (PHI, PII, classified information) even while it is being processed by the AI models.
Traceability and Provenance: The KGNN provides an intrinsic data layer that captures the lineage and context for every piece of data used by the AI model. This provides the auditable trail required by financial (e.g., Basel, MiFID) and healthcare (e.g., HIPAA) regulations, eliminating the "black box" problem of cloud AI.
3. Integration & Zero-ETL (Bridging Data Silos)
PowerGraph's central innovation is the Automated Knowledge Graph Generation (KGNN), which solves the decades-old integration problem that stalls most enterprise AI projects.
| Problem (Cloud/Traditional ETL) | PowerGraph/KGNN Solution | Impact on Integration |
| Data Silos & ETL Hell | Zero-ETL: PowerGraph connects directly to disparate data sources (AIX mainframes, IBM i systems, Linux databases, unstructured documents) and automatically builds the semantic knowledge graph. | Fastest Path to AI: Eliminates months of manual data cleaning and transformation, unifying all enterprise data into a single, contextualized source for AI. |
| Lack of Context | Semantic Graph: The KGNN models the relationships between data points, providing the context that prevents Large Language Models (LLMs) from "hallucinating" (making up facts). | Accuracy: Ensures the NLQ/Gen AI results are highly accurate and grounded in the enterprise's mission-critical data. |
4. Cost (TCO Reduction)
The solution reduces Total Cost of Ownership (TCO) by leveraging and enhancing existing, fully depreciated IBM Power assets, avoiding expensive cloud egress fees, and maximizing hardware efficiency.
Leveraging Existing Investment: By running on the existing IBM Power 11 platform, organizations avoid the massive Capital Expenditure (CapEx) of buying and integrating a separate x86/GPU cluster solely for AI.
Energy Efficiency: The Spyre Accelerator is highly efficient, delivering AI performance for a fraction of the energy consumption of general-purpose GPUs, significantly lowering Operational Expenditure (OpEx) for power and cooling.
Cloud Cost Avoidance: Keeping the AI processing and data ingestion on-prem eliminates unpredictable cloud consumption and expensive data egress charges, making the operational costs predictable and controllable—a critical factor for government and financial institutions.
KPI "Power-Up On Prem" strategy is highly effective because it directly addresses the customer's existing investment software and data center concerns. Below is the step-by-step deployment plan, structured to solve the user pain points of Integration, Security, and Costs at each stage.
Equitus PowerGraph (KGNN) Deployment Plan for IBM Power 10/11 : WEBINAR LINK
Goal: Achieve fully operational, on-premises Gen AI (NLP/NLQ) in days, not months, by maximizing the value of existing IBM Power hardware and the new Spyre AI Accelerator (for Power11).
Phase 1: Foundation & Hardware Readiness (Addressing Cost)
This phase ensures the client maximizes their existing hardware investment and prepares for the energy-efficient performance gains.
| Step | Action | Customer Deliverable & Pain Point Addressed |
| 1.0. System Assessment (CDW/Equitus) | Conduct a remote health check and capacity planning review of the client's existing IBM Power 10/11 environment (OS: AIX, IBM i, or Linux). | Cost Reduction (Hardware/Energy): Confirms existing server capacity is sufficient and identifies optimal resource partitioning, ensuring the client does not over-provision or acquire unnecessary x86/GPU hardware. |
| 1.1. Spyre Accelerator Installation (for Power11) | Physically install and configure the IBM Spyre AI Accelerator cards (via the PCIe bus) into the Power11 systems. | Cost Reduction (Energy/TCO): Activates the dedicated, energy-efficient AI inference engine on-premise, directly lowering the operational cost per query compared to high-power consumption GPUs. |
| 1.2. Base Software Appliance Installation | Deploy the pre-configured Equitus KGNN software appliance (often containerized via Red Hat OpenShift or SUSE Rancher, as suggested by Equitus) onto the designated Power logical partition (LPAR). | Cost Reduction (Workflow/Personnel): Installs the pre-optimized stack in a single step, minimizing manual software installation and reducing initial IT labor costs. |
Phase 2: Data Integration & KGNN Activation (Addressing Integration & Workflow)
This phase focuses on the "zero-ETL" promise, eliminating the biggest workflow bottleneck for AI projects.
| Step | Action | Customer Deliverable & Pain Point Addressed |
| 2.0. Source System Connection | Establish secure, read-only connections from PowerGraph to the client's fragmented data sources (e.g., DB2, Oracle, HPE databases, S3 buckets, file shares, and legacy systems). | Integration & Workflow: Eliminates the need to copy data. PowerGraph connects directly to data silos, honoring the existing data location. |
| 2.1. Automated Knowledge Graph Generation | Initiate the core KGNN process. PowerGraph automatically ingests, cleans, links, and contextualizes the disparate data sets, building the schema-less, AI-ready Knowledge Graph in real-time. | Integration & Workflow (Zero-ETL): Eliminates ETL. The graph self-constructs in days, not months, creating an immediate semantic layer that is essential for accurate Gen AI. |
| 2.2. Vectorization and RAG-Readiness | Automatically vectorize the newly contextualized data within the KGNN. This prepares the graph for Retrieval-Augmented Generation (RAG) capabilities. | Workflow (Accuracy): Ensures the final Gen AI/NLQ models will be grounded in truth (the client’s specific data), dramatically increasing accuracy and trustworthiness. |
Phase 3: AI Enablement & Security Hardening (Addressing Security)
This phase productionalizes the solution, activating the AI capabilities while ensuring data sovereignty and compliance.
| Step | Action | Customer Deliverable & Pain Point Addressed |
| 3.0. NLQ/NLP Model Deployment | Deploy the client's selected LLM/NLP models and integrate them with the PowerGraph's vectorized data using an API layer. (Inference will be accelerated by the Spyre chip.) | Performance & Security: Activates real-time, low-latency NLQ/Gen AI inference on-premises via the Spyre chip, keeping the entire AI pipeline secure and local. |
| 3.1. Granular Access Control & Provenance | Implement role-based access control (RBAC) directly within PowerGraph, integrating with the client’s existing Active Directory (AD). Configure the system to log all data access and query results. | Security (Audit & Compliance): Ensures only authorized users access specific graph data. Provides full data provenance for every AI result, critical for regulatory audits and increasing user trust. |
| 3.2. Abacus Digital Secure Integration | Establish a secure, bidirectional API connection between the on-prem PowerGraph and the AbacusDigital.net platform for specialized services (e.g., anti-fraud, financial health scoring, or compliance monitoring). | Security & Integration: Extends the secure, on-prem AI with advanced third-party analytics/security layers without exposing the raw data to the public internet, offering a complete, protected data-to-decision stack. |
Summary of Deployment Success Metrics
| Pain Point Solved | Metric for Success | Value to the vlcm.com User |
| Integration/Workflow | Time from Installation to NLQ Demo | Days, not Months. (Demonstrates the "7 Days to Gen AI" promise and zero-ETL validity.) |
| Cost (ETL/Personnel) | Reduction in FTE hours dedicated to data prep. | IT staff are freed from manual data pipeline maintenance to focus on strategic AI use cases. |
| Cost (Energy/TCO) | Power consumption per AI inference. | Proves the Spyre chip's efficiency on Power11, validating the decision to use existing IBM hardware over costly, power-hungry GPU clusters. |
| Security/Accuracy | Auditable Data Provenance for NLQ results. | Builds trust and compliance by showing the lineage of every AI-generated answer. |
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