> GENAI_GPU
Cost-Optimized LLM Inference Cluster: vLLM & Spot GPUs
High-efficiency production LLM serving cluster utilizing vLLM PagedAttention, Ray cluster autoscaling, and spot GPU instances with automated failover.
Mathematical Breakeven Inflection Curve
Inflection champion above 20 million tokens/day. Self-hosting 70B models on 4x A100/H100 Spot instances is 60% cheaper than OpenAI/Anthropic API rates.
3 Maturity Tiers & Infrastructure Specifications
Component stack and cost steps from prototype to hyper-scale enterprise
1. Prototype / Early Stage1M - 10M tokens/day
$450 - $1,800 / mo
$0.18 / 1M tokens
Stack Components:
- 1x EC2 g5.12xlarge (4x A10G 96GB)
- vLLM (Llama-3.1-8B-Instruct)
- FastAPI Gateway
Cost Allocation:Single Instance Tagging
Autoscaling:Manual Scheduled Scaling
2. Scaled Production20M - 150M tokens/day
$2,800 - $11,500 / mo
$0.075 / 1M tokens
Stack Components:
- Ray Cluster on Kubernetes
- 2x - 6x g5.48xlarge or p4d.24xlarge Spot instances
- vLLM with chunked prefill + prefix caching
Cost Allocation:FOCUS Model Ingestion & Serving Tags
Autoscaling:Ray Autoscaler based on vLLM queue depth
3. High-Throughput Enterprise200M - 2B tokens/day
$11,500 - $65,000 / mo
$0.038 / 1M tokens
Stack Components:
- Multi-Cloud GPU Fleet (AWS Spot + RunPod / Lambda Labs)
- 8x H100 SXM5 nodes with TensorRT-LLM
- Speculative Decoding with 8B Draft Model
Cost Allocation:Per-Tenant Token Attribution & Chargeback
Autoscaling:Global Latency-Aware GPU Scheduler
Key Cost Drivers
- •GPU instance hourly lease fees ($2.50 to $32.00/hour)
- •Cross-AZ inter-GPU communication bandwidth
- •High-speed shared model cache storage
Waste Vulnerabilities
- •WST-AI-01: Leaving idle GPU nodes running overnight without requests
- •WST-AI-02: Unbatched single-request inference pipeline
Mitigation Playbooks
- •Enable vLLM continuous batching and PagedAttention
- •Configure Ray autoscaler min_workers=0 with 15-minute idle timeout
- •Blend 80% Spot GPUs with 20% On-Demand reserve