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.
1 GPU vs 2 GPU: When Does Multi-GPU Make Sense?
Understand when scaling from one GPU to two GPUs makes sense for AI inference, including throughput gains, latency trade-offs, and cost considerations.
GPU Capacity Planning for AI SaaS Products
Step-by-step GPU capacity planning for AI SaaS — sizing GPUs for chatbots, APIs, image generation, and voice agents based on…
Model Sharding: Run 70B+ Models Across Multiple GPUs
A practical guide to sharding 70B+ parameter models across multiple GPUs, covering VRAM requirements, sharding strategies, configuration examples, and performance…
Tensor Parallelism vs Pipeline Parallelism for Multi-GPU
Understanding tensor parallelism and pipeline parallelism for multi-GPU LLM inference, including architecture diagrams, configuration examples, and scaling benchmarks.
Docker vs Bare Metal for AI Inference: Performance Comparison
Docker containers versus bare metal for AI inference performance. Measuring GPU overhead, deployment flexibility, and operational trade-offs on dedicated GPU…
Kubernetes vs Docker Compose for AI: When to Scale
Kubernetes versus Docker Compose for AI workload orchestration. Understanding when the complexity of K8s is justified for GPU inference versus…
Single GPU vs Multi-GPU vs Multi-Server: Scaling Guide
Compare single GPU, multi-GPU, and multi-server configurations for AI inference and training. Understand when each scaling tier delivers the best…
API-First vs Model-First AI Architecture
Comparing API-first and model-first approaches to AI system design. When to build around API contracts versus optimising for model performance,…
Monolith vs Microservices for AI Inference
Monolithic versus microservices architecture for AI inference pipelines. Comparing deployment complexity, latency, scaling, and when to split your AI stack…
On-Premise vs Cloud vs Dedicated: AI Hosting Guide
Comparing on-premise hardware, cloud GPU instances, and dedicated GPU servers for AI workloads. Total cost of ownership, performance consistency, and…
Explore GPU Hosting Solutions
From the blog to your next deployment — pick the right platform for your workload.
Dedicated GPU Hosting
Bare-metal servers with a dedicated GPU, NVMe, full root access, and 1Gbps networking from our UK datacenter.
Browse GPU ServersPrivate AI Hosting
Isolated GPU infrastructure for sensitive AI workloads — no shared hardware, full data control.
Explore Private AIMulti-GPU Clusters
Scale horizontally with multi-GPU configurations for training and large-model inference.
Explore ClustersAPI Hosting
Host your own AI API endpoints on dedicated GPU servers — low latency, high availability.
Explore API HostingOpen Source LLM Hosting
Deploy LLaMA, Mistral, DeepSeek, and more on dedicated hardware with no per-token API fees.
Explore LLM HostingTokens/sec Benchmarks
Real-world tokens per second data across every GPU we offer, tested on popular LLMs.
View BenchmarksReady to deploy your AI workload?
Dedicated GPU servers from our UK datacenter. NVMe storage, 1Gbps networking, full root access.
65 Victory Avenue
PE7 1XU, England
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