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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.

1258+ Articles
10+ Years Exp.
50+ AI Deployments
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AWS Cost Optimization for Ollama Inference - Pricing comparison table of g5 vs p4d instances for Llama 3 deployment (112 chars) Servers
Marcus Chen
5 min read

AWS Cost Optimization for Ollama Inference transforms expensive GPU deployments into budget-friendly operations. Learn proven tactics like spot instances and model quantization to slash bills while maintaining high throughput. This guide delivers actionable steps for EC2, EKS, and SageMaker setups.

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Optimize Ollama GPU Memory in AWS SageMaker - Benchmark chart of VRAM usage before/after quantization on ml.g5.12xlarge (112 chars) Servers
Marcus Chen
6 min read

Running Ollama in AWS SageMaker demands precise GPU memory optimization to avoid out-of-memory crashes and maximize token throughput. This guide covers instance choices, Docker setups, quantization techniques, and real-world benchmarks. Achieve 2-5x faster inference while minimizing expenses.

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Scale Ollama Server with AWS EKS Kubernetes - EKS control plane with GPU node groups, Ollama pods scaling via HPA, load balancer distributing inference traffic (98 chars) Servers
Marcus Chen
7 min read

Scale Ollama Server with AWS EKS Kubernetes by creating a managed cluster, adding GPU nodes, and deploying via Helm charts. This approach ensures horizontal scaling, load balancing, and fault tolerance for demanding AI workloads. Follow our detailed guide for optimal performance.

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vLLM Local Deployment Tutorial - GPU server running inference engine with continuous batching and PagedAttention memory optimization for high-throughput language model serving Servers
Marcus Chen
12 min read

This comprehensive vLLM Local Deployment Tutorial walks you through setting up a production-ready language model inference server on your local hardware. From installation to Docker containerization, you'll master the complete vLLM deployment workflow with practical examples and real-world benchmarks.

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Quantization Guide for Local LLMs - RTX 4090 benchmarks showing Q4 vs FP16 speed gains Servers
Marcus Chen
5 min read

Running large language models locally hits VRAM walls fast. This Quantization Guide for Local LLMs solves that with proven techniques to shrink models while keeping quality high. Get step-by-step setups for RTX 4090 hosting.

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Run Llama 31 Locally Step-by-step - Run LLaMA 3.1 Locally Step-by-Step - RTX 4090 running quantized 70B model with Ollama ... Servers
Marcus Chen
5 min read

Running LLaMA 3.1 locally gives you full control over powerful AI without cloud costs or data leaks. This step-by-step guide covers Ollama setup, GPU optimization and advanced quantization for peak performance. Unlock offline inference today.

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