Building a Local Document Intelligence System on GB10
How to Chat with Your Files Securely, at Low Latency, and Zero Cloud Dependency—Complete RAG Implementation Guide
Modern organizations sit on vast amounts of unstructured knowledge—PDFs, Word documents, spreadsheets, presentations, emails, and internal reports. While this data contains critical institutional intelligence, it is often locked in silos, difficult to retrieve, and disconnected from real-time business workflows such as sales, legal review, compliance checks, or operational decision-making.
This blog walks through a practical, production-ready Document Intelligence system built entirely on-premise using the Dell Pro Max GB10 platform, demonstrating how organizations can "chat with their files" using a simple Retrieval-Augmented Generation (RAG) architecture—without sending sensitive data to the cloud.
The Core Problem: Knowledge Exists, Context Is Lost
Most enterprises face three structural issues that prevent them from unlocking the full value of their document repositories:
1. Fragmented Knowledge
Policies, contracts, standard operating procedures (SOPs), emails, and reports live across multiple formats and locations. Finding the right document requires knowing where to look—and often, who to ask.
2. High Retrieval Cost
Finding the right answer requires manual searching through folders, email threads, or relying on tribal knowledge. This process is time-consuming, error-prone, and doesn't scale as organizations grow.
3. Cloud Constraints
Uploading sensitive data to cloud-based AI services introduces multiple challenges:
- Ongoing API and inference costs that scale with usage
- Network latency affecting user experience
- Data residency and client-privilege risks for legal and healthcare sectors
- Regulatory barriers (HIPAA, GDPR, DPDP Act 2023, legal privilege, internal governance)
For industries such as legal services, healthcare, BFSI, manufacturing, and government, these constraints often make cloud-based AI impractical or impossible to deploy.
Why GB10: Local AI Without Compromise
At the center of this solution is Dell Pro Max GB10, a workstation powered by NVIDIA Blackwell architecture. This isn't a consumer-grade device—it's an enterprise-ready AI supercomputer designed for local deployment.
Dell Pro Max GB10 Specifications
Processor
ARM64-based with 20 cores optimized for AI workloads
Memory
128 GB unified memory for large model inference
GPU Architecture
NVIDIA Blackwell for enterprise-grade performance
Form Factor
Compact, rack-ready design for office/lab deployment
This makes GB10 suitable for deploying enterprise-grade AI capabilities locally, inside an office, lab, or secure LAN—without relying on cloud infrastructure or external API calls.
See It in Action: Live GB10 Document Intelligence Demo
Watch this hands-on demonstration showing how to build and deploy a document intelligence system on GB10 in real-time. You'll see document upload, indexing, vector embedding generation, and live query responses—all running locally without cloud dependencies.
Architecture Overview: How the Local RAG System Works
The architecture is intentionally simple and modular, designed for rapid deployment and easy customization. It consists of three primary layers:
Layer 1: User Interface
- Web UI for document upload and conversational chat interface
- CLI tools for power users and automation workflows
- API endpoints for system integration with existing enterprise tools
Layer 2: RAG Orchestration (On Device)
All intelligence runs locally on GB10:
- Query understanding – natural language processing of user questions
- Context retrieval – semantic search across document embeddings
- Answer generation – LLM-powered response synthesis with citations
Layer 3: Core Components
- Document Processor – parses PDFs, DOCX, TXT, XLSX, and more
- Vector Database – stores embeddings locally (ChromaDB, Qdrant, or Weaviate)
- LLM Runtime – configurable per use case (LLaMA 3.1, Mistral, Qwen)
🔒 Zero Cloud Dependency
No cloud APIs. No external inference calls. No data leakage. Everything runs on your hardware, under your control.
Supported LLM Models
GB10's 128GB unified memory enables running large language models locally without performance degradation:
LLaMA 3.1
8B or 70B parameters
Best for: General document Q&A
Mistral Variants
7B to 22B parameters
Best for: Fast inference, multilingual
Qwen 2.5
7B to 72B parameters
Best for: Technical documents, code
Building the RAG: Fast, Practical, and Accessible
One of the key takeaways from the demo is the speed of implementation. This isn't a months-long research project—it's a practical system you can deploy in hours.
Time to working prototype
Skills required
Code availability
Implementation Workflow
Inside Visual Studio or your preferred IDE, the workflow looks like this:
- Initialize a local LLM runtime (LLaMA.cpp, Ollama, or vLLM)
- Load and chunk documents into manageable segments (500-1000 tokens)
- Generate embeddings using sentence transformers or OpenAI-compatible models
- Store vectors in a local database (ChromaDB, Qdrant, or Weaviate)
- Query → retrieve → generate answers with document-level citations
✅ Full Transparency
Every answer is traceable to a source document. Users can click through to see the exact paragraph or page that informed the AI's response—building trust and enabling verification.
Live Demo Highlights: Talking to Your Files
The demo showcases three real-world scenarios that demonstrate the system's capabilities:
Example 1: "What is GB10?"
- The system queried multiple uploaded documents (product specs, datasheets, manuals)
- Returned a structured answer synthesizing information from 3 different sources
- Included document-level citations with page numbers for verification
Example 2: "Top use cases for GB10"
- Retrieved a single authoritative PDF (GB10 Use Cases whitepaper)
- Ranked use cases by ROI and implementation complexity
- Demonstrated low latency (2.3 seconds) and precise context matching
Example 3: Upload → Index → Query (Real Time)
- A new sales document was uploaded during the demo
- Automatically chunked into 13 embeddings in under 10 seconds
- Instantly queryable from the chat interface—no manual indexing required
- Answers clearly sourced to the newly uploaded file with timestamp
This illustrates how institutional memory becomes interactive knowledge. Instead of searching through folders or asking colleagues, users simply ask questions in natural language and get accurate, cited answers in seconds.
Performance Benchmarks on GB10
Based on observed and documented metrics from production deployments:
| Metric | Performance | Use Case |
|---|---|---|
| Document Indexing Speed | ~5,200 pages/minute | Bulk document ingestion |
| Query Latency | 2–5 seconds | Real-time Q&A |
| Concurrent Users | 10–20 users (LAN) | Department-level deployment |
| Document Capacity | ~1 million documents | Enterprise knowledge base |
| Latency vs Cloud | 100–500 ms faster | No network round-trips |
Ideal Deployment Scenarios
- Small to mid-size teams (5-50 users) needing instant document access
- Departmental AI deployments (legal, HR, finance, sales) with sensitive data
- Secure enterprise environments requiring air-gapped or on-premise solutions
- Research labs and universities processing large document corpora
The Economics: Why On-Prem Wins
A simple cost comparison highlights the ROI of deploying document intelligence on GB10 versus cloud-based LLM APIs:
| Metric | Cloud LLM APIs | GB10 On-Premise |
|---|---|---|
| 10,000 queries/month | $2,000–$5,000 | $0 |
| Annual cost | $24,000–$60,000 | Fixed hardware cost |
| 4-year TCO | ~$240,000 | One-time capex |
| Cost savings | — | 96–98% |
| Payback period | — | ~2 months |
Strategic Advantages Beyond Cost
Full Data Privacy
No data leaves your premises
Predictable Performance
No API rate limits or throttling
No Vendor Lock-In
Full control over models and data
Real-World Use Cases Enabled by Document Intelligence
With this setup, teams across industries can deploy specialized document intelligence workflows:
Legal Document Intelligence
Search across contracts, case law, and precedents. Extract clauses, identify risks, and generate summaries—all while maintaining attorney-client privilege.
Use case: Contract review, due diligence, legal research
Smart HR File Processing
Query employee handbooks, policies, and benefits documentation. Automate onboarding Q&A and compliance checks without exposing PII to cloud services.
Use case: HR chatbot, policy Q&A, compliance automation
Finance Automation
Extract data from invoices, financial statements, and audit reports. Automate reconciliation and anomaly detection with full audit trails.
Use case: Invoice processing, financial analysis, audit support
Sales Knowledge Assistants
Instant access to product specs, pricing sheets, case studies, and competitive intelligence. Empower sales teams with accurate answers during client calls.
Use case: Sales enablement, RFP response, competitive analysis
Preventive Maintenance Insights
Search equipment manuals, maintenance logs, and failure reports. Predict issues and recommend preventive actions based on historical data.
Use case: Manufacturing, facilities management, equipment monitoring
Fraud Detection Workflows
Analyze transaction records, customer communications, and compliance documents. Identify patterns and anomalies indicative of fraudulent activity.
Use case: Banking, insurance, e-commerce fraud prevention
In essence: Your own AI lab in a box.
Who This Is For
🏢 Enterprises
Organizations with sensitive data and strict compliance requirements (HIPAA, GDPR, DPDP Act 2023). Industries include legal, healthcare, BFSI, and manufacturing.
🎓 Universities
Building AI labs and research platforms for students and faculty. Enable document intelligence research without cloud costs or data privacy concerns.
🚀 Startups
Needing predictable AI economics without runaway cloud bills. Build competitive moats with proprietary document intelligence capabilities.
🏛️ Government
Public sector organizations requiring sovereign AI infrastructure. Deploy document intelligence for citizen services, policy analysis, and administrative automation.
Each can explore industry-specific workflows and deploy measurable use cases within a 90-day roadmap provided by Copilots.in's AI Lab Program.
Closing Thoughts: Intelligence Moves to the Data
The GB10 Document Intelligence system demonstrates a critical shift in enterprise AI: Intelligence is moving closer to the data, not the other way around.
By combining local LLMs, RAG architecture, and GB10's hardware capabilities, organizations can unlock their knowledge securely, cost-effectively, and at production scale—without relying on the cloud.
For teams evaluating on-prem AI seriously, this architecture is no longer experimental.
It is deployable, economical, and ready for real-world workloads.
Ready to Build Your Document Intelligence System?
Book a discovery call with our team to explore how GB10 can transform your organization's document workflows. We'll walk through your specific use case, demonstrate the system live, and provide a customized 90-day deployment roadmap.
📅 Book a Discovery Call30-minute consultation • No commitment required • Technical deep-dive available
Related Resources
→ Dell Pro Max GB10 Product Overview
Complete specifications, architecture, and deployment options
→ AI Sales Agent on GB10: 64% Open Rate Case Study
See how local LLMs power hyper-personalized outreach at zero cost
→ 10 Real-World GB10 Use Cases
Explore industry-specific AI applications across enterprises and universities
→ Copilots AI Lab Program
90-day deployment roadmap with hardware, training, and pilot development
📚 Citations & References
- Dell Technologies. (2024). Dell Pro Max with GB10 Technical Specifications. Dell Official Documentation.
- NVIDIA Corporation. (2024). NVIDIA Blackwell Architecture Whitepaper. NVIDIA Developer Resources.
- Meta AI. (2024). LLaMA 3.1 Model Card and Performance Benchmarks. Meta AI Research.
- ChromaDB. (2024). Vector Database for AI Applications. ChromaDB Official Documentation.
- Copilots.in. (2024). GB10 Document Intelligence System Demo. Internal Testing and Customer Deployments.
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