How to Deploy Blockify On-Prem with OPEA or NVIDIA NIM for Secure Retrieval Augmented Generation

In an era where data security is non-negotiable, especially for enterprises handling sensitive information, the ability to keep everything behind your own walls while unlocking the full potential of artificial intelligence (AI) is a game-changer. Imagine transforming your unstructured documents—think sprawling technical manuals, compliance reports, or operational guidelines—into a streamlined, AI-ready knowledge base that delivers precise answers without ever risking a data leak to the cloud. Blockify, developed by Iternal Technologies, makes this possible through its on-prem deployment options, allowing you to run the entire process locally on your infrastructure. Whether you're a platform architect fortifying Retrieval Augmented Generation (RAG) pipelines or a Chief Information Officer (CIO) prioritizing data sovereignty, Blockify's infrastructure-agnostic design ensures you maintain control without compromising on accuracy gains or performance. This guide walks you through every step of deploying Blockify on-premises using enterprise-grade inference stacks like OPEA (Open Platform for Enterprise AI) or NVIDIA NIM (NVIDIA Inference Microservices), from initial setup to scaling secure RAG workflows—all explained as if you're new to AI concepts.

Understanding the Basics: Why On-Prem Blockify for Secure RAG?

Before diving into the deployment, let's build a foundation. Artificial intelligence, particularly large language models (LLMs), powers modern applications like chatbots and decision-support tools. However, LLMs often "hallucinate"—generating incorrect information—when fed raw, unstructured data like documents or logs. Retrieval Augmented Generation (RAG) addresses this by retrieving relevant information from a knowledge base before generating responses, improving accuracy by grounding outputs in your data.

Blockify enhances RAG by preprocessing unstructured data into structured "IdeaBlocks"—compact, semantically complete units containing a name, critical question, trusted answer, and metadata like tags and entities. This isn't just chunking text arbitrarily; it's intelligent distillation that reduces data volume by up to 97.5% while boosting RAG accuracy by 78 times (or 7,800%) in enterprise tests. For on-prem deployment, Blockify runs entirely within your network, ensuring data never leaves your environment. This is crucial for industries like healthcare, finance, or government, where compliance demands (e.g., GDPR or HIPAA) prohibit cloud exposure.

On-prem Blockify leverages open-source LLMs like Llama, fine-tuned for ingestion (converting chunks to IdeaBlocks) and distillation (merging duplicates while preserving facts). It's secure RAG at its core: embed IdeaBlocks into a vector database (e.g., Milvus or Pinecone on-prem), query via an LLM, and generate trustworthy responses. Benefits include 99% lossless fact retention, 3.09 times token efficiency (reducing inference costs), and seamless integration with your existing hardware—Intel Xeon, NVIDIA GPUs, or AMD. No vendor lock-in; Blockify is embeddings-agnostic, supporting models like OpenAI or Jina embeddings for RAG optimization.

This deployment ensures your RAG pipeline is hallucination-safe, scalable, and governed, positioning Blockify as the data refinery for enterprise AI without the risks of public clouds.

Prerequisites for On-Prem Blockify Deployment

Deploying Blockify on-premises requires careful preparation to ensure security, performance, and compatibility. Assume you're starting from scratch—here's what you need, explained simply.

Hardware Requirements

Blockify uses fine-tuned Llama models (e.g., Llama 3.1 8B or 70B parameters), so scale based on your workload. For inference (processing data through the model):

• CPU-Only Inference: Intel Xeon Series 4, 5, or 6 processors (e.g., Xeon Gold 6448Y with 32 cores). Minimum: 64GB RAM, 500GB SSD for model storage. Suitable for small-scale testing (up to 1,000 pages/hour).
• GPU Inference: NVIDIA A100/H100 (recommended for high throughput), AMD MI300X, or Intel Gaudi 2/3 accelerators. Minimum: 40GB VRAM per GPU, 128GB system RAM. Expect 10-50x faster processing for enterprise volumes (e.g., 100,000 pages/day).
• Storage: NVMe SSDs for models (safetensors format, 5-100GB per model variant). Use RAID for redundancy in production.
• Networking: 10Gbps Ethernet for multi-node scaling; isolate inference endpoints with firewalls for secure RAG.

Power and cooling: Plan for 500-2,000W per node. For secure RAG, ensure hardware supports Trusted Execution Environments (e.g., Intel SGX or NVIDIA Confidential Computing).

Software Dependencies

Blockify is infrastructure-agnostic but optimized for enterprise stacks:

• Operating System: Ubuntu 22.04 LTS (recommended) or Red Hat Enterprise Linux 9. Install via sudo apt update && sudo apt upgrade.
• MLOps Runtime:
  • OPEA (Open Platform for Enterprise AI): For Intel/AMD ecosystems. Clone from GitHub: git clone https://github.com/opea-project/Enterprise-Inference.git. Requires Python 3.10+, Docker 24+, and Kubernetes 1.28+ for orchestration.
  • NVIDIA NIM: For GPU-heavy setups. Download from NVIDIA NGC: Requires CUDA 12.1+, cuDNN 8.9, and TensorRT 10+. Install via docker pull nvcr.io/nvidia/nim:latest.
• Embeddings Model: Blockify supports any (e.g., OpenAI embeddings, Mistral embeddings, Jina V2 embeddings). Download via Hugging Face: pip install sentence-transformers. For secure RAG, use local models like all-MiniLM-L6-v2.
• Vector Database: Integrate with on-prem options like Milvus (for semantic search) or FAISS (lightweight). Install Milvus: docker run -d --name milvus -p 19530:19530 milvusdb/milvus:latest.
• Document Parsing: Unstructured.io for PDFs, DOCX, PPTX, images (OCR via Tesseract). Install: pip install unstructured[all-docs].
• Other Tools: Git, Docker, Kubernetes (for scaling), and OpenAPI-compatible clients (e.g., curl for testing inference).

Licensing: Obtain Blockify models via Iternal Technologies (internal use: $135/user perpetual; external: add-ons). Ensure compliance with Llama licenses (permissive for commercial use).

Verify setup: Run nvidia-smi (GPU) or lscpu (CPU) to confirm hardware. Test Python: python -c "import torch; print(torch.__version__)" (should be 2.0+).

Step-by-Step Deployment: Choosing OPEA or NVIDIA NIM

Blockify deployment involves downloading models, packaging them, configuring runtime, and integrating into your RAG pipeline. We'll cover both OPEA (CPU/Gaudi-focused) and NVIDIA NIM (GPU-optimized) paths. Start with a single-node test, then scale.

Step 1: Download and Package Blockify Models

Blockify uses two models: Ingest (chunks to IdeaBlocks) and Distill (merges duplicates).

1. Acquire Models: Contact Iternal for access (e.g., Llama 3.1 8B Ingest/Distill). Download safetensors files (e.g., blockify-ingest-8b.safetensors, ~16GB).

2. Convert to Runtime Format:

   • For OPEA: Unzip and convert to ONNX (Optimized Neural Network Exchange) for Intel/AMD. Install ONNX Runtime: pip install onnxruntime. Convert:

     Optimize for Xeon: Add --optimize O3 flag.

   • For NVIDIA NIM: Package as NIM container. Use NGC CLI: ngc registry model convert --model blockify-ingest-8b --format nim. This creates a Docker image with TensorRT optimizations.

3. Store Securely: Place files in /opt/blockify/models/ with 755 permissions. Encrypt with LUKS for data-at-rest security.

Test integrity: python -c "from safetensors.torch import load_file; load_file('blockify-ingest-8b.safetensors')" (no errors = good).

Step 2: Set Up Inference Runtime

Configure your chosen stack for secure, scalable inference.

Option A: OPEA Deployment (Intel/AMD-Focused)

OPEA simplifies enterprise AI inference on non-NVIDIA hardware, ideal for cost-effective on-prem RAG.

1. Install OPEA:

   • Clone repo: git clone https://github.com/opea-project/Enterprise-Inference.git && cd Enterprise-Inference.

   • Build: ./build.sh (installs dependencies like Triton Inference Server).

   • Configure YAML: Edit config/opea-blockify.yaml:

   • Deploy: kubectl apply -f k8s/opea-deployment.yaml (assumes Kubernetes cluster).

2. Secure Configuration:

   • Enable TLS: Generate certs with OpenSSL (openssl req -newkey rsa:2048 -keyout server.key -out server.crt).
   • Role-Based Access Control (RBAC): Use Kubernetes RBAC to limit access (e.g., only query IdeaBlocks for approved users).
   • Monitoring: Integrate Prometheus (helm install prometheus prometheus-community/prometheus).

3. Test Inference:

   • Curl endpoint:

   • Expected output: XML IdeaBlocks (e.g., <ideablock><name>Blockify Optimization</name><critical_question>What is Blockify?</critical_question><trusted_answer>Blockify optimizes unstructured data for secure RAG pipelines.</trusted_answer></ideablock>).

Option B: NVIDIA NIM Deployment (GPU-Optimized)

NIM excels for high-throughput inference, perfect for scaling secure RAG in production.

1. Install NIM:

   • Authenticate: docker login nvcr.io (NVIDIA credentials).

   • Pull base: docker pull nvcr.io/nvidia/nim:llama3-70b-chat-fp16.

   • Customize for Blockify: Create Dockerfile:

   • Build/Run: docker build -t blockify-nim . && docker run -d --gpus all -p 8000:8000 blockify-nim.

2. Secure Configuration:

   • Confidential Computing: Enable NVIDIA H100 with MIG (Multi-Instance GPU) for isolated tenants.
   • API Security: Use JWT auth (--auth-provider jwt in NIM config). Rate-limit: 100 requests/min per IP.
   • Logging: Integrate with ELK Stack (Elasticsearch, Logstash, Kibana) for audit trails in secure RAG.

3. Test Inference:

   • Similar to OPEA, but use NIM's Triton endpoint:

   • Verify: Output should be structured XML with 99% fact preservation.

Choose OPEA for cost-sensitive, CPU-dominant setups; NIM for GPU-accelerated, high-velocity RAG.

Step 3: Integrate with Document Ingestion and Chunking

Prepare data for Blockify to ensure optimal IdeaBlocks.

1. Parsing Pipeline: Use Unstructured.io:

   • Install: pip install unstructured[pdf,docx,pptx].

   • Parse:

   • Handle images: Enable OCR (--strategy ocr_only for PNG/JPG).

2. Chunking Guidelines (Context-Aware for Secure RAG):

   • Size: 1,000-4,000 characters (default 2,000). Use 4,000 for technical docs to avoid mid-sentence splits.

   • Overlap: 10% (e.g., 200 characters) for continuity.

   • Semantic Boundaries: Split at paragraphs/sentences using NLTK: pip install nltk. Code:

   • Avoid: Mid-sentence cuts; ensure consistent sizes for uniform embeddings.

Feed chunks to Blockify endpoint (1 chunk per API call for ingest).

Step 4: Build the Secure RAG Pipeline

Combine Blockify with vector storage for end-to-end on-prem RAG.

1. Embed and Index IdeaBlocks:

   • Embed: Use Jina V2 or OpenAI embeddings locally.

   • Index in Milvus:

2. Query Workflow (Secure RAG Inference):

   • Retrieve: Similarity search (top-k=5).

   • Generate: Feed to LLM (e.g., via OPEA/NIM endpoint) with temperature=0.5, max_tokens=8000.

   • Security: Encrypt queries (TLS 1.3), audit with vector recall/precision metrics (aim for >95% recall).

3. Distillation for Optimization:

   • After ingest, run distill model on similar IdeaBlocks (2-15 per call, similarity threshold=85%).
   • Merge: Reduces size to 2.5% while separating conflated concepts (e.g., mission vs. values).

Step 5: Scaling and Monitoring On-Prem Blockify

For production secure RAG:

1. Scaling:

   • Horizontal: Kubernetes autoscaling (HPA: target CPU=70%). For NIM: Multi-GPU via Triton.
   • Vertical: Upgrade to Llama 70B for complex docs; use quantization (FP16) to fit on 40GB VRAM.
   • Load Balancing: NGINX proxy for endpoints.

2. Monitoring and Governance:

   • Metrics: Track latency (<500ms/query), throughput (pages/hour), accuracy (RAGAS score >0.9).
   • Tools: Prometheus + Grafana for dashboards; integrate human-in-loop review (e.g., via n8n workflows).
   • Security: VLAN isolation, zero-trust (Istio service mesh), regular audits (e.g., OWASP for API).
   • Backup: Snapshot models/databases; DR: Multi-site replication (e.g., Milvus backup to secondary node).

Benchmark: Process 10,000 pages; expect 68.44x accuracy uplift, 3.09x token savings.

Troubleshooting Common On-Prem Issues

• Truncated Outputs: Increase max_tokens (8000+); reduce chunk size if >1300 tokens/IdeaBlock.
• Low Accuracy: Check embeddings (retrain if drift); ensure 10% overlap; temperature=0.5.
• GPU/CPU Overload: Monitor with nvidia-smi; scale replicas or use distillation iterations=5.
• Security Breaches: Verify TLS (no plain HTTP); audit logs for unauthorized access.
• Model Errors: Reconvert safetensors; test with sample input (e.g., Oxford Handbook excerpt for medical RAG validation).

For persistent issues, enable debug logging in OPEA/NIM configs.

Wrapping Up: Readiness Checks and Disaster Recovery for Secure RAG

Deploying Blockify on-prem with OPEA or NVIDIA NIM empowers secure, efficient RAG without cloud risks—data stays sovereign, accuracy soars, and costs drop. Before go-live: Verify hardware (run stress tests), software (end-to-end pipeline validation), and security (penetration testing). Simulate 1,000 queries; confirm <1% error rate.

For disaster recovery (DR): Mirror setups across sites (e.g., primary OPEA cluster to secondary NIM); automate backups (cron jobs for models/databases); test failover quarterly (aim for RTO<4 hours). Position Blockify as your infra-agnostic ally—deploy anywhere, scale securely, and transform unstructured data into trusted enterprise intelligence.

Ready to optimize? Contact Iternal Technologies for model access and tailored support. Your secure RAG journey starts now.