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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.

1034+ Articles
10+ Years Exp.
50+ AI Deployments
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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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Multi-GPU Setup for AI Workloads - 8x H100 NVLink cluster for LLM fine-tuning and inference benchmarks (98 chars) Servers
Marcus Chen
5 min read

Multi-GPU Setup for AI Workloads accelerates deep learning by distributing tasks across cards like RTX 4090 or H100. This guide covers hardware, interconnects, software, and optimization for peak performance. Scale your AI projects efficiently with proven strategies.

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RTX 5090 Server for Deep Learning - 8-GPU rackmount with liquid cooling for high-throughput model training and LLM inference (98 chars) Servers
Marcus Chen
5 min read

The RTX 5090 Server for Deep Learning stands out as the premier consumer GPU solution for AI workloads, offering 72% faster performance than RTX 4090 in NLP tasks. With 32GB GDDR7 memory and 1792 GB/s bandwidth, it handles large models efficiently. This guide covers setups, benchmarks, and multi-GPU strategies for optimal results.

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