TrustGraph as an AI Factory on Bare Metal

When deployed on high-performance bare metal infrastructure, TrustGraph transforms into a complete AI Factory - a self-contained, high-throughput system for building, managing, and querying Knowledge Graphs at massive scale.

What is an AI Factory?

An AI Factory is a production-grade infrastructure that:

• Ingests vast amounts of raw data (documents, databases, APIs, streams)
• Processes data through intelligent pipelines (extraction, linking, reasoning)
• Transforms unstructured data into structured knowledge
• Serves knowledge to applications through high-performance APIs
• Learns continuously from new data and feedback
• Operates at scale with predictable performance and cost

TrustGraph on bare metal delivers all these capabilities with unmatched performance and economics.

Why Bare Metal?

Performance Advantages

1. Zero Virtualization Overhead

# Cloud VM: Virtualization layer adds latency
User Request → Hypervisor → VM → Container → Application
Latency: ~5-10ms overhead

# Bare Metal: Direct hardware access
User Request → Application
Latency: Sub-millisecond

Result: 10-100x lower latency for graph queries

2. Dedicated Hardware Resources

// Cloud: Shared resources with noisy neighbors
// - CPU throttling during peak hours
// - Network bandwidth competition
// - Disk I/O contention
// - Unpredictable performance

// Bare Metal: Dedicated resources
const performance = {
  cpu: "100% dedicated - no throttling",
  memory: "100% dedicated - no swapping",
  network: "100% dedicated - 100Gbps possible",
  storage: "NVMe SSDs in RAID - consistent IOPS",
};

// Result: Predictable, consistent performance

3. Hardware Optimization

Bare Metal Configuration for AI Factory:
  CPU:
    - AMD EPYC 9004 or Intel Xeon Platinum
    - 128+ cores per server
    - Optimized for graph traversal

  Memory:
    - 1-2TB DDR5 per server
    - Keep hot graph data in memory
    - Eliminate database disk reads

  Storage:
    - 8-16 NVMe SSDs in RAID 10
    - 10M+ IOPS sustained
    - 50GB/s+ sequential throughput

  Network:
    - 100Gbps Ethernet
    - RDMA support
    - Direct connections between servers

  Result: 100x faster than cloud

Cost Advantages

Monthly Cost Comparison (100TB Knowledge Graph, 100M queries/month):

Cloud (AWS/Azure/GCP):
- Compute (Kubernetes cluster): $15,000/month
- Graph DB (managed): $8,000/month
- Vector Store (managed): $5,000/month
- Storage (1TB SSD): $1,000/month
- Network egress (10TB): $1,000/month
- Load balancers: $500/month
- Total: $30,500/month
- Annual: $366,000

Bare Metal (self-owned):
- Hardware (4 servers): $200,000 (one-time)
  - Amortized over 4 years: $4,167/month
- Colocation (rack, power, cooling): $3,000/month
- Network (10Gbps): $500/month
- Total: $7,667/month
- Annual: $92,000

Savings: $274,000/year (75% cost reduction)
ROI: 8.8 months to break even

At scale (1PB+ data, 1B+ queries/month):
- Cloud: $150,000-300,000/month
- Bare Metal: $15,000-30,000/month
- Savings: 90% cost reduction

Data Sovereignty

// Bare metal in your own datacenter
const aiFactory = new TrustGraph({
  deployment: {
    type: "bare-metal",
    location: "your-datacenter",
    network: "isolated",
    airGap: true,  // No internet connection
  },
  compliance: {
    dataResidency: "on-premise",
    encryption: {
      atRest: "AES-256",
      inTransit: "TLS 1.3",
      keyManagement: "HSM",
    },
    auditLog: {
      enabled: true,
      retention: "7 years",
      destination: "local-siem",
    },
  },
});

// Benefits:
// ✅ Complete data control
// ✅ Meet any compliance requirement
// ✅ No data leaves your facility
// ✅ Air-gap capable
// ✅ Government/military grade security

AI Factory Architecture

Hardware Configuration

Typical 4-Server AI Factory (Small to Medium):

Graph Cluster (2 servers):
  Purpose: Knowledge Graph storage and traversal
  CPU: AMD EPYC 9654 (96 cores) × 2
  RAM: 2TB DDR5 per server (4TB total)
  Storage: 8× 7.68TB NVMe SSDs in RAID 10 (30TB usable per server)
  Network: 100Gbps Ethernet with RDMA
  Database: Neo4j cluster mode or Cassandra
  Capacity: 10-100TB Knowledge Graphs
  Performance: 1M+ queries/second

Vector Store Cluster (1 server):
  Purpose: Embedding storage and similarity search
  CPU: AMD EPYC 9554 (64 cores)
  RAM: 1TB DDR5
  Storage: 8× 3.84TB NVMe SSDs in RAID 10 (15TB usable)
  Network: 100Gbps Ethernet
  Database: Qdrant or Milvus
  Capacity: 100B+ vectors
  Performance: 100K+ queries/second

Compute Cluster (1 server):
  Purpose: Agent orchestration, API gateway, ingestion
  CPU: AMD EPYC 9374F (32 cores)
  RAM: 512GB DDR5
  Storage: 2× 3.84TB NVMe SSDs in RAID 1
  Network: 100Gbps Ethernet
  Services: TrustGraph agents, API, ingestion pipeline
  Performance: 10K+ concurrent agents

Total Cost: ~$200,000
Power: ~5kW
Rack Space: 8U

Large-Scale AI Factory (Enterprise):

Configuration: 20-server cluster

Graph Cluster: 10 servers
  - 960 CPU cores total
  - 20TB RAM total
  - 300TB NVMe storage
  - Distributed Neo4j or Cassandra
  - Capacity: 1PB+ Knowledge Graphs
  - Performance: 10M+ queries/second

Vector Cluster: 6 servers
  - 384 CPU cores total
  - 6TB RAM total
  - 90TB NVMe storage
  - Distributed Qdrant/Milvus
  - Capacity: 1T+ vectors
  - Performance: 1M+ queries/second

Compute Cluster: 4 servers
  - 256 CPU cores total
  - 2TB RAM total
  - 30TB NVMe storage
  - Kubernetes for orchestration
  - Performance: 100K+ concurrent agents

Total Cost: ~$1M
Power: ~25kW
Rack Space: 40U
Capacity: Petabyte-scale
Performance: 10M+ queries/second

Software Stack

Complete TrustGraph AI Factory Stack:

Layer 1: Infrastructure
  - Operating System: Ubuntu Server 22.04 LTS
  - Container Runtime: containerd
  - Orchestration: Kubernetes (bare metal)
  - Networking: Calico with RDMA
  - Storage: Rook/Ceph for distributed storage

Layer 2: Data Storage
  Graph Database:
    - Neo4j Enterprise (clustered)
    - Or: Apache Cassandra with graph plugin
    - Or: Memgraph High Availability

  Vector Store:
    - Qdrant (clustered)
    - Or: Milvus (distributed)
    - Or: Pinecone self-hosted

  Object Storage:
    - MinIO (S3-compatible)
    - For document storage

Layer 3: TrustGraph Platform
  Core Services:
    - Knowledge Graph Engine
    - Vector Search Engine
    - Entity Extraction Pipeline
    - Relationship Linking Engine
    - Reasoning Engine
    - Agent Orchestration

  APIs:
    - REST API (high-throughput)
    - GraphQL API
    - gRPC API (low latency)
    - WebSocket API (real-time)

Layer 4: Observability
  - Prometheus (metrics)
  - Grafana (dashboards)
  - Jaeger (distributed tracing)
  - ELK Stack (logging)
  - Alert Manager

Layer 5: Security
  - Authentication (OAuth2, SAML)
  - Authorization (RBAC)
  - Encryption (at rest and in transit)
  - Network policies
  - Intrusion detection

Deployment

Automated deployment:

# Clone TrustGraph bare metal deployment
git clone https://github.com/trustgraph-ai/trustgraph-bare-metal
cd trustgraph-bare-metal

# Configure your hardware
cat > config/hardware.yaml <<EOF
servers:
  graph_nodes:
    - hostname: graph-01.factory.internal
      ip: 10.0.1.10
      cpu_cores: 96
      ram_gb: 2048
      storage:
        - /dev/nvme0n1
        - /dev/nvme1n1
        # ... all NVMe devices
    - hostname: graph-02.factory.internal
      ip: 10.0.1.11
      # ...

  vector_nodes:
    - hostname: vector-01.factory.internal
      ip: 10.0.1.20
      # ...

  compute_nodes:
    - hostname: compute-01.factory.internal
      ip: 10.0.1.30
      # ...

network:
  cluster_cidr: 10.0.0.0/16
  service_cidr: 10.1.0.0/16
  backend: rdma  # Or: standard

storage:
  graph_replication: 3
  vector_replication: 2
EOF

# Deploy AI Factory
./deploy.sh

# Deployment steps:
# 1. Provision operating systems
# 2. Configure networking (RDMA if available)
# 3. Set up storage (RAID, distributed storage)
# 4. Deploy Kubernetes
# 5. Deploy databases (Neo4j, Qdrant)
# 6. Deploy TrustGraph platform
# 7. Deploy observability stack
# 8. Configure load balancing
# 9. Set up monitoring and alerts
# 10. Run validation tests

# Result: Production-ready AI Factory in ~2 hours

Performance Characteristics

Query Performance

Graph Query Latency:

// Simple 1-hop query
// Cloud: 50-100ms
// Bare Metal: 1-5ms
await trustgraph.query({
  cypher: "MATCH (n:Person {name: 'John'}) RETURN n"
});

// Medium 3-hop query
// Cloud: 200-500ms
// Bare Metal: 10-30ms
await trustgraph.query({
  cypher: `
    MATCH (p:Person {name: 'John'})-[:KNOWS*1..3]-(friend)
    RETURN friend
  `
});

// Complex multi-hop with aggregation
// Cloud: 1-5 seconds
// Bare Metal: 50-200ms
await trustgraph.query({
  cypher: `
    MATCH (c:Company)-[:INVESTS_IN*1..4]->(s:Startup)
    WHERE c.region = 'Silicon Valley'
    WITH s, count(c) as investors
    MATCH (s)-[:OPERATES_IN]->(sector:Sector)
    RETURN sector.name, sum(s.valuation) as total_value
    ORDER BY total_value DESC
  `
});

Vector Search Latency:

// Top-10 similarity search
// Cloud: 20-50ms
// Bare Metal: 1-3ms
await trustgraph.vectorSearch({
  query: "AI in healthcare",
  topK: 10,
});

// Top-100 with filtering
// Cloud: 100-200ms
// Bare Metal: 5-10ms
await trustgraph.vectorSearch({
  query: "AI in healthcare",
  topK: 100,
  filters: { date: { gte: "2024-01-01" } },
});

Hybrid Queries (Graph + Vector):

// Combined retrieval
// Cloud: 500ms-2s
// Bare Metal: 20-50ms
const results = await trustgraph.retrieve({
  query: "Impact of AI on healthcare industry",
  strategy: "graph-rag",
  vectorTopK: 20,
  graphDepth: 3,
  fusion: "rrf",
});

Throughput

Sustained Query Throughput:

Small Factory (4 servers):
  Simple Queries: 1M+ queries/second
  Medium Queries: 100K queries/second
  Complex Queries: 10K queries/second
  Mixed Workload: 500K queries/second

Large Factory (20 servers):
  Simple Queries: 10M+ queries/second
  Medium Queries: 1M queries/second
  Complex Queries: 100K queries/second
  Mixed Workload: 5M queries/second

Ingestion Throughput:

Document Ingestion:
  Text Documents: 10,000 docs/second
  PDFs (with OCR): 1,000 docs/second
  Structured Data: 100,000 records/second
  Real-time Streams: 1M events/second

Entity Extraction:
  Entities per second: 1M+
  Relationships per second: 500K+

Graph Updates:
  Nodes created: 100K/second
  Edges created: 500K/second
  Properties updated: 1M/second

Scalability

Horizontal Scaling:

// Start with 4 servers
const factory = new TrustGraph({
  cluster: {
    graphNodes: 2,
    vectorNodes: 1,
    computeNodes: 1,
  },
});

// Add capacity dynamically
await factory.cluster.addNode({
  type: "graph",
  hostname: "graph-03.factory.internal",
  // Automatically rebalances data
});

// Scale to 100+ servers
// Linear performance scaling
// No downtime required

Vertical Scaling:

# Upgrade individual servers without cluster downtime

Step 1: Drain workload from node
kubectl drain graph-01 --ignore-daemonsets

Step 2: Upgrade hardware
# Add more RAM, replace with faster CPUs, add NVMe drives

Step 3: Rejoin cluster
kubectl uncordon graph-01

# Cluster automatically rebalances
# No application downtime

Cost Economics

Total Cost of Ownership (TCO)

3-Year TCO Comparison:

AI Factory Bare Metal (Medium: 100TB, 100M queries/month):

Year 1:
  Hardware: $200,000
  Colocation: $36,000
  Network: $6,000
  Staff (1 FTE): $150,000
  Total: $392,000

Year 2-3 (annual):
  Colocation: $36,000
  Network: $6,000
  Staff (1 FTE): $150,000
  Total: $192,000/year

3-Year Total: $776,000
Average Monthly: $21,556

Cloud Equivalent (AWS/GCP/Azure):
  Year 1-3 (annual): $366,000
  3-Year Total: $1,098,000
  Average Monthly: $30,500

Savings: $322,000 over 3 years (29% reduction)
Break-even: 13 months

Large-Scale TCO (Petabyte, 1B queries/month):

AI Factory Bare Metal (Large: 1PB, 1B queries/month):

Year 1:
  Hardware: $1,000,000
  Colocation: $180,000
  Network: $30,000
  Staff (2 FTE): $300,000
  Total: $1,510,000

Year 2-3 (annual):
  Colocation: $180,000
  Network: $30,000
  Staff (2 FTE): $300,000
  Total: $510,000/year

3-Year Total: $2,530,000
Average Monthly: $70,278

Cloud Equivalent:
  Year 1-3 (annual): $2,400,000
  3-Year Total: $7,200,000
  Average Monthly: $200,000

Savings: $4,670,000 over 3 years (65% reduction)
Break-even: 7.5 months

Cost per Query

Bare Metal AI Factory:
  Hardware amortized: $0.000001/query
  Operations: $0.000002/query
  Power: $0.0000005/query
  Total: $0.0000035/query

Cloud:
  Compute: $0.00001/query
  Database: $0.00002/query
  Network: $0.000005/query
  Total: $0.000035/query

Bare Metal is 10x cheaper per query

Production Use Cases

1. Enterprise Knowledge Management

// Fortune 500 company deploys AI Factory
const enterprise = {
  data: "50TB company knowledge",
  documents: "10M documents",
  employees: "100K users",
  queries: "10M queries/day",
};

// Bare Metal AI Factory:
const deployment = {
  servers: 8,
  cost: "$400K hardware + $10K/month operations",
  performance: "Sub-10ms query latency",
  capacity: "Handles 10x growth",
};

// Business impact:
const impact = {
  employeeProductivity: "+25%",
  decisionSpeed: "3x faster",
  knowledgeRetention: "90% of tribal knowledge captured",
  roi: "6 months",
};

2. Financial Services

// Investment bank builds market intelligence system
const financeFactory = {
  data: "200TB market data",
  entities: "100M companies, people, instruments",
  relationships: "1B+ relationships",
  updates: "1M updates/day real-time",
  queries: "100M queries/day",
};

// Bare Metal requirements:
const requirements = {
  latency: "<5ms for trading decisions",
  throughput: "1M queries/second peak",
  compliance: "On-premise, air-gapped",
  costSensitivity: "Very high",
};

// Result:
const result = {
  deployment: "20-server AI Factory",
  cost: "$1M + $50K/month",
  vs_cloud: "$200K/month = $2.4M/year saved",
  performance: "10x better than cloud",
  compliance: "Full control, auditable",
};

3. Healthcare Research

// Medical research institution
const healthcare = {
  data: "500TB medical literature + patient data",
  documents: "50M research papers",
  clinicalTrials: "500K trials",
  queries: "1M queries/day from researchers",
  compliance: "HIPAA, air-gapped",
};

// Bare Metal AI Factory:
const medicalFactory = {
  servers: 15,
  configuration: "On-premise, isolated network",
  cost: "$750K + $30K/month",
  performance: "Real-time literature synthesis",
  benefits: [
    "Accelerate drug discovery",
    "Connect disparate research",
    "Find hidden patterns",
    "Complete data sovereignty"
  ],
};

4. Government Intelligence

// Intelligence agency
const intelligence = {
  data: "Petabyte-scale intelligence data",
  sources: "Documents, signals, imagery, databases",
  classification: "Top Secret",
  deployment: "Air-gapped facility",
  performance: "Real-time threat analysis",
};

// AI Factory configuration:
const govFactory = {
  servers: 50,
  deployment: "Classified datacenter",
  security: [
    "No internet connection",
    "Hardware-encrypted storage",
    "TEMPEST shielding",
    "Physical access control"
  ],
  capabilities: [
    "Multi-hop link analysis",
    "Pattern detection",
    "Threat prediction",
    "Multi-INT fusion"
  ],
  cost: "$2.5M (cloud not an option)",
};

Advanced Optimizations

Memory-Resident Graphs

// Load entire graph in RAM
await trustgraph.configure({
  graphDatabase: {
    mode: "memory-resident",
    allocation: "1.5TB",  // Entire graph in RAM
    persistence: "async-checkpoint",
    checkpointInterval: "5 minutes",
  },
});

// Benefits:
// - Zero disk reads for queries
// - 100x faster than disk-backed
// - Microsecond query latency
// - Suitable for <2TB graphs

RDMA Networking

# Remote Direct Memory Access
network:
  type: rdma
  protocol: RoCE  # RDMA over Converged Ethernet
  speed: 100Gbps

# Benefits:
# - Bypass CPU for network operations
# - Sub-microsecond latency
# - 100Gbps sustained throughput
# - Zero-copy data transfer

# Performance improvement:
# - 10x lower latency
# - 5x higher throughput
# - 50% lower CPU usage

GPU Acceleration

// Add GPUs for specific workloads
await trustgraph.configure({
  acceleration: {
    embeddings: {
      device: "gpu",
      model: "NVIDIA H100",
      count: 4,
    },
    graphAlgorithms: {
      device: "gpu",  // GPU-accelerated graph algorithms
      libraries: ["cuGraph", "GraphBLAS"],
    },
  },
});

// 100x faster embedding generation
// 50x faster graph algorithms (PageRank, etc.)

Operational Excellence

Monitoring

// Comprehensive observability
const monitoring = {
  metrics: {
    query_latency_p50: "2ms",
    query_latency_p99: "15ms",
    throughput: "500K queries/second",
    cpu_usage: "45%",
    memory_usage: "75%",
    disk_iops: "5M",
    network_throughput: "50Gbps",
  },
  alerts: [
    "Latency exceeds 50ms",
    "CPU exceeds 80%",
    "Disk space below 20%",
    "Node failure",
  ],
  dashboards: [
    "Real-time performance",
    "Capacity planning",
    "Cost tracking",
    "Error rates",
  ],
};

Disaster Recovery

Backup Strategy:
  # Continuous replication
  - Real-time replication to secondary datacenter
  - 3x replication within primary cluster

  # Snapshots
  - Hourly snapshots (retained 24 hours)
  - Daily snapshots (retained 7 days)
  - Weekly snapshots (retained 4 weeks)
  - Monthly snapshots (retained 12 months)

  # Point-in-time recovery
  - Restore to any point within 30 days
  - RTO (Recovery Time Objective): 15 minutes
  - RPO (Recovery Point Objective): 0 seconds

High Availability:
  - Active-active configuration
  - Automatic failover
  - Zero downtime upgrades
  - 99.99% uptime SLA

Conclusion

TrustGraph on high-performance bare metal transforms into a complete AI Factory that delivers:

Performance:

• 10-100x faster than cloud
• Sub-millisecond query latency
• Millions of queries per second
• Real-time ingestion and updates

Cost:

• 50-90% lower than cloud
• Predictable costs
• Linear scaling
• Fast ROI (6-12 months)

Control:

• Complete data sovereignty
• Air-gap capable
• Any compliance requirement
• Custom hardware optimization

Scale:

• Petabyte-scale Knowledge Graphs
• Billions of entities and relationships
• Millions of concurrent users
• Continuous growth

For production-critical applications requiring maximum performance, lowest cost, and complete control, deploying TrustGraph as an AI Factory on bare metal is the optimal architecture.

Next Steps

• TrustGraph Deployment Guide
• Hardware Sizing Calculator
• Performance Benchmarks
• Enterprise Support