WHY STANDARD DEPLOYMENT STRATEGIES FALL SHORT
For GPU-backed AI models, spinning up replicas takes 5-30 minutes and costs $10-60/hr per replica. Doubling capacity for blue-green deployment of a 16-GPU model means 32 GPUs, adding $28-85K in monthly costs.
GPU-aware deployment minimizes overlap. For a 70B Llama 3 on 4 H100 GPUs, model loading takes 45 seconds with vLLM. Memory overlap is 280 GB out of 320 GB available.
GPU-AWARE BLUE-GREEN DEPLOYMENT
Using NVIDIA MIG or MPS, a single H100 can host both old and new models simultaneously, eliminating duplicate GPU nodes. When sharing is not possible, use staged rollout: deploy new model to 2 of 8 replicas, route 5-10 percent traffic, monitor, then complete. Overlap cost: $14 vs $280 for full blue-green.
CANARY DEPLOYMENTS WITH GPU BUDGET CONTROL
A canary replica on 4 H100 GPUs costs $14/hr regardless of traffic. Sequential canary evaluations use the same GPU resources: 5 deploys per week at 10-min windows cost $11.67/week total.
Advanced canaries include embedding drift monitoring: compute cosine similarity between old and new model outputs. Drift below 0.95 triggers investigation, below 0.85 triggers rollback.
ORCHESTRATION FRAMEWORKS
Kubernetes with NVIDIA GPU Operator provides GPU partitioning and device plugin config. Argo Rollouts or Flagger must support weighted traffic splitting based on GPU-aware metrics like memory utilization and SM occupancy.
Key parameters: maxSurge 25%, maxUnavailable 0%, canary analysis interval 30s, failure threshold 3. Latency gate at 1.5x p99 baseline. Deployment infra cost: 5-10 percent of inference GPU budget.