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Cloud Infrastructure Insights

Expert tutorials, benchmarks, and guides on GPU servers, AI deployment, VPS hosting, and cloud computing.

Marcus Chen - Senior Cloud Infrastructure Engineer
Written by our expert

Marcus Chen

Senior Cloud Infrastructure Engineer & AI Systems Architect

10+ years of experience in GPU computing, AI deployment, and enterprise hosting. Former NVIDIA and AWS engineer. Stanford M.S. in Computer Science. I help businesses deploy AI models and optimize cloud infrastructure.

117+ Articles
10+ Years Exp.
50+ AI Deployments
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Comparing AWS vs Azure vs GCP scaling limits and quotas - cloud providers scalability comparison diagram Servers
Marcus Chen
15 min read

Comparing AWS vs Azure vs GCP scaling limits and quotas is critical if you run elastic, high-traffic, or AI-heavy workloads. This in-depth comparison breaks down service quotas, autoscaling behavior, soft vs hard limits, and real-world pros and cons so you can choose the right cloud for long-term scalability.

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How to design cloud architectures for elastic scaling - diagram of autoscaling services, databases, and load balancers in a modern cloud setup Servers
Marcus Chen
12 min read

This guide explains how to design cloud architectures for elastic scaling step by step, from stateless services and autoscaling to stateful data, quotas, and real-world load tests. You will learn concrete patterns to keep costs under control while scaling AI and GPU workloads across AWS, Azure, and GCP.

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Cloud autoscaling strategies for AI and GPU workloads - diagram of elastic GPU clusters scaling with traffic and costs breakdown Servers
Marcus Chen
11 min read

Cloud autoscaling strategies for AI and GPU workloads are critical because GPUs are 10–20 times more expensive than CPUs and highly bursty. This guide explains practical autoscaling patterns, real-world pricing ranges on AWS, Azure, and GCP, and how to design elastic, cost-optimized GPU architectures for AI training and inference.

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ARM Server Viability for LLM Workloads - Benchmark chart showing Graviton4 outperforming x86 in tokens per second (112 characters) Servers
Marcus Chen
6 min read

ARM Server Viability for LLM Workloads is gaining traction as data centers prioritize power efficiency. This guide tackles common challenges like software compatibility and delivers actionable solutions with real benchmarks. Learn how to deploy LLMs on ARM for lower TCO without sacrificing performance.

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Kubernetes Deployment for Multi-GPU LLM Clusters - Diagram of vLLM pods across H100 nodes with tensor parallelism Servers
Marcus Chen
6 min read

Kubernetes Deployment for Multi-GPU LLM Clusters enables efficient scaling of large language models across GPU nodes. This guide covers cluster setup, pod configurations, inference engines like vLLM, and optimization strategies. Deploy Llama 3.1 or DeepSeek with high throughput today.

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LLM Quantization Methods to Reduce Server Costs - Pricing table comparing FP16 vs INT4 on RTX 4090 VPS and A100 cloud (112 chars) Servers
Marcus Chen
6 min read

LLM Quantization Methods to Reduce Server Costs offer powerful ways to slash GPU expenses while maintaining model performance. From INT8 to advanced INT4 techniques, these methods enable running massive models like Llama 3 on cheaper hardware. This guide breaks down strategies, costs, and real-world savings for AI deployments.

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GPU vs CPU Performance for LLM Inference - side-by-side benchmark graph of RTX 4090 vs Ryzen CPU on 14B model showing 40+ tok/s GPU advantage Servers
Marcus Chen
6 min read

GPU vs CPU Performance for LLM Inference reveals GPUs dominate large models with massive parallelism, while CPUs shine for small-scale or low-volume tasks. This guide compares tokens per second, latency, and costs with benchmarks. Choose wisely for optimal AI inference on VPS or cloud setups.

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