AI Platform and Infrastructure Readiness Checklist

Description

AI Platform and Infrastructure Readiness Checklist

50 actionable checkboxes for AI compute architecture, networking, GPU sizing, LLM gateway design, and load testing.

This role-based checklist contains 50 ready-to-use checkboxes extracted from the LLM Production Readiness — Complete Checklist (v8 consolidated). It covers the infrastructure architecture, hardware sizing, and performance validation required before routing production traffic to LLM systems.

What’s Inside:

• 50 checkboxes across 4 domains: Infrastructure (28), LLM Gateway (5), Hardware Sizing (8), Load Testing (9)
• Compute & serving: isolated container deployment, horizontal autoscaling on request queue depth (not CPU), three health check types (liveness/readiness/output quality canary), staged rollout (canary → 5% → 25% → 100%) with automated rollback, inference engine selection by scale (Ollama dev / vLLM production / NVIDIA NIM enterprise), circuit breakers and timeouts on every LLM call path, and KEDA autoscaling triggered by per-replica queue depth from Prometheus
• Data & storage: encryption at rest for model weights/prompt logs/outputs/training datasets, TLS 1.2+ minimum (TLS 1.3 preferred) for all data in transit, per-user knowledge isolation in memory/RAG systems, automated backup and point-in-time recovery, GDPR/CCPA/HIPAA-aligned retention schedules, user-scoped semantic cache entries (cross-user cache matches are a privacy violation), and cache isolation testing before go-live
• Network segmentation & VPC controls: LLM inference endpoints inside private VPC, egress allowlisting on LLM containers, vector databases and knowledge bases on private subnets, API traffic routed through gateway inside VPC, network-level rate limiting and DDoS protection at API gateway tier, and physical/logical GPU node isolation from corporate network
• Kubernetes production configuration: startupProbe with failureThreshold ≥ 40 × 10s (400s) for large model load, API keys injected via Kubernetes Secrets (never CLI arguments), explicit GPU resource limits in pod spec, topology spread constraints on GPU nodes, and PersistentVolumeClaim for model weights
• Container hardening: distroless or minimal base images, AppArmor or seccomp profiles with system call whitelisting, and inference engine configuration flag verification against exact version release notes
• LLM gateway (multi-provider control plane): unified gateway deployment to prevent vendor lock-in, primary + fallback provider configuration with tested failover, semantic caching at gateway layer, unified cross-provider cost tracking, and API rate limits by user/team/org including semantic-based throttling for jailbreak patterns
• VRAM sizing: minimum VRAM calculation formula (model params × bytes/param + KV cache + 2-4 GB runtime), GPU tier selection by use case (H100/B200 multi-GPU, A100 enterprise, RTX 4090/5090 dev/staging), and petabyte-scale storage planning (base weights + training data + fine-tuned versions + logs)
• Inference engine configuration: PagedAttention enablement in vLLM for KV cache memory management, disaggregated prefill/decode evaluation for high-concurrency (vLLM V1), and actual throughput measurement (never vendor-reported peak)
• GPU local memory security: GPU memory clearing between inference requests for different users (LeftOvers attack protection) and hardware-level memory isolation via NVIDIA MIG for multi-tenant deployments
• Pre-production load testing gate: structured load test against realistic traffic patterns (mandatory go/no-go), four key inference metrics (RPS, TTFT, ITL, end-to-end latency at P50/P95/P99), KV-cache utilisation and request queue depth validation under peak load, and hardware sizing confirmation from load test results
• GPU & infrastructure monitoring: GPU utilisation and VRAM pressure dashboards, KV-cache exhaustion alerting, continuous VRAM headroom tracking, inter-GPU NVLink bandwidth monitoring for multi-GPU deployments, and automated restart policy for CUDA OOM crashes
• Interactive HTML with progress tracking — check off items as you complete them

Use Cases:

• AI infrastructure architecture and capacity planning with VRAM calculations
• LLM gateway design for multi-provider deployments with automatic failover
• Kubernetes production configuration and container security hardening for AI workloads
• Network segmentation and VPC architecture for LLM infrastructure
• Pre-production load testing validation and GPU monitoring
• Semantic cache design with user-scoped privacy isolation

Perfect For:

Infrastructure engineers, cloud architects, platform teams, DevOps engineers, and technical leads responsible for the compute, networking, and hardware layer of AI deployments.