Compute Resource Management

Strategies for cost-efficient operation of GPU/NPU servers and cloud infrastructure

On-Premises vs. Cloud

    flowchart TD
    A["AI workload"] --> B{"Workload characteristics"}
    B -->|"Large-scale training<br/>Irregular load"| C["Cloud<br/>AWS / GCP / Azure"]
    B -->|"Continuous inference<br/>Data security critical"| D["On-premises<br/>GPU servers"]
    B -->|"Mixed strategy"| E["Hybrid<br/>Cloud bursting"]

    style A fill:#2563EB,stroke:#1D4ED8,color:#fff
    style C fill:#16A34A,stroke:#15803D,color:#fff
    style D fill:#EA580C,stroke:#C2410C,color:#fff
    style E fill:#7C3AED,stroke:#6D28D9,color:#fff

Cloud AI Service Comparison

Item	AWS Bedrock	Google Vertex AI	Azure AI
Key models	Claude, Llama, Titan	Gemini, PaLM	GPT-4, Phi
Fine-tuning	Supported	Supported	Supported
On-demand pricing	Per-token billing	Per-token billing	Per-token billing
Provisioned throughput	Supported	Supported	Supported

Cost Optimization Strategies

1. Model Tiering

Complex tasks   → Large models (Claude Opus, GPT-4o)
General tasks   → Mid-size models (Claude Sonnet, GPT-4o-mini)
Simple tasks    → Small models (Claude Haiku, GPT-3.5)

2. Caching Strategy

For prompts or context that are reused repeatedly, prompt caching can cut costs by up to 90%.

3. Batch Processing

For workloads that don’t require a real-time response, using the Batch API cuts costs by 50%.

GPU Spec Guide

Use case	Recommended GPU	Notes
Large-scale training	H100, A100	80GB+ VRAM
Mid-size fine-tuning	A10G, L40S	24–48GB VRAM
Inference server	T4, L4	16GB VRAM, cost-efficient
Local development	RTX 4090	24GB VRAM

Model Selection & Tuning