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AI Hosting & Infrastructure

AI Hosting & Infrastructure

AI Hosting & Infrastructure

Build production AI infrastructure on dedicated GPU servers. These guides cover networking, storage architecture, scaling strategies, and deployment patterns for running AI workloads on bare metal. From private AI hosting to multi-GPU clusters, learn how to architect GPU infrastructure that scales.

AI Hosting & Infrastructure Apr 2026

Multi-Tenant GPU Server Isolation Patterns

How to serve multiple tenants from one GPU server without one customer's workload starving another.

AI Hosting & Infrastructure Apr 2026

One Big GPU vs Many Small GPUs – The Architectural Debate

The case for one 96GB card versus three or four 16GB cards at similar price - which wins for which…

AI Hosting & Infrastructure Apr 2026

PCIe Lanes and Multi-GPU Performance on Dedicated Servers

x16 per card, x8, x4 - the PCIe topology of your server decides how much performance you extract from multi-GPU…

AI Hosting & Infrastructure Apr 2026

Virtual GPU Partitioning for Inference – Options and Tradeoffs

vGPU, MIG, MPS, and plain CUDA device selection - a plain-English guide to how GPUs get sliced up for multi-workload…

AI Hosting & Infrastructure Apr 2026

Scaling Inference Horizontally vs Vertically

Bigger card or more cards - the oldest infrastructure question, applied specifically to LLM inference in 2026.

AI Hosting & Infrastructure Apr 2026

SGLang vs vLLM in 2026 – Production Comparison

Both engines claim best-in-class throughput. Running them side-by-side on identical hardware reveals where each actually wins.

AI Hosting & Infrastructure Apr 2026

Splitting Embedding and LLM Across Two GPUs

In a RAG stack the embedder and the LLM compete for VRAM and compute. Putting them on different cards solves…

AI Hosting & Infrastructure Apr 2026

Tensor Parallelism vs Pipeline Parallelism on Dedicated GPU Servers

The two ways to split a large model across multiple GPUs. When to use which, with concrete numbers from vLLM…

AI Hosting & Infrastructure Apr 2026

Two RTX 6000 Pro Architecture Patterns

192GB of VRAM across two cards. The serving patterns that justify this much capacity and the ones that do not.

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