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Enterprise Infrastructure

Enterprise LLM Infrastructure

Build production-grade infrastructure for enterprise AI at scale. From GPU cluster management to high-availability deployments, we design and implement robust systems that deliver reliable, performant AI for mission-critical applications.

Design InfrastructureView Projects

99.9%

Uptime SLA

<100ms

P95 Latency

10K+

Requests/Second

Components

Core infrastructure components

GPU Cluster Management

Design and manage GPU clusters optimized for LLM inference with efficient resource allocation and scheduling.

  • NVIDIA A100/H100 optimization
  • Multi-GPU inference
  • Dynamic GPU allocation
  • Cost-optimized scheduling

Load Balancing & Scaling

Distribute inference requests across model replicas with intelligent routing and auto-scaling.

  • Request-aware routing
  • Horizontal auto-scaling
  • Queue management
  • Burst handling

Observability Stack

Comprehensive monitoring, logging, and alerting for production LLM operations.

  • Token throughput metrics
  • Latency percentiles
  • Error rate tracking
  • Custom dashboards

Security & Compliance

Enterprise-grade security with encryption, access control, and compliance frameworks.

  • Data encryption at rest/transit
  • RBAC and audit logs
  • SOC 2 compliance
  • Network isolation

Architecture

Proven architecture patterns

High-Availability Deployment

Multi-region, multi-zone deployment with automatic failover for mission-critical applications.

Primary → Secondary → Disaster Recovery

Inference Gateway Pattern

Centralized gateway for routing, rate limiting, authentication, and request transformation.

Clients → Gateway → Model Pool

Model Serving Mesh

Service mesh architecture for managing multiple model versions and A/B testing.

Traffic Split → Model v1/v2/v3

Edge-Cloud Hybrid

Smaller models at edge for low-latency, larger models in cloud for complex queries.

Edge (7B) ↔ Cloud (70B)

Technology Stack

Enterprise-grade technologies

Kubernetes

Orchestration

NVIDIA Triton

Inference Server

vLLM

Inference Engine

Ray Serve

Distributed Serving

Prometheus

Monitoring

Grafana

Visualization

Jaeger

Tracing

Istio

Service Mesh

HashiCorp Vault

Secrets

Terraform

Infrastructure as Code

ArgoCD

GitOps

Redis

Caching

Planning

Capacity planning considerations

Concurrent Users

Request queuing, connection pooling, rate limiting

Tokens per Second

GPU memory, batch size, model parallelism

Average Latency

Model size, quantization, caching strategy

Peak Load

Auto-scaling headroom, burst capacity, queue depth

Checklist

Production deployment checklist

GPU driver and CUDA version compatibility
Model weight storage and distribution
Inference server configuration
Load balancer setup with health checks
Auto-scaling policies and triggers
Monitoring and alerting rules
Logging and audit trail
Backup and disaster recovery
Security hardening and penetration testing
Performance benchmarking and optimization
Documentation and runbooks
On-call procedures and escalation

Ready to build enterprise AI infrastructure?

Let's design and implement production-grade infrastructure for your AI workloads.

Start Infrastructure Project