Cluster · Hardware

Hardware.

Mac Studio, GPU workstations, 42U racks, power, cooling, and the hardware decisions that decide whether an on-premise LLM is fast enough to be used. Sizing, throughput, and operating costs.

Hardware.

Articles

Choosing AI Inference Hardware: H100 vs H200 vs RTX 6000 Ada vs Mac Studio M3 Ultra

Sizing a Sovereign AI Appliance: Concurrent Users, Latency, and Throughput

Mac Studio M3 Ultra 192GB: The Surprise Sovereign-AI Edge Appliance

Strix Halo 128GB: AMD's Sovereign-AI Workstation Play

Datacenter-Grade AI Racks: Power, Cooling, UPS, and Air-Gap Network Design

NVLink Topology for Multi-GPU LLM Deployments

H100 vs H200 Memory Bandwidth: The Practical Impact on LLM Inference

RTX 6000 Ada 48GB for Sovereign Tower Deployments

AMD MI300X vs NVIDIA H100 for LLM Serving

Apple Silicon for LLM Inference: Benchmarks and Real-World Numbers

Edge GPU Appliances for Branch and Field Offices

Air-Gap Network Architecture for Sovereign AI Clusters

Dell vs HPE vs Supermicro AI Servers in the GCC

Power and Cooling Calculations for a 4-GPU Rack in Muscat's Climate

Choosing 2U, 4U, or Tower for Your Sovereign AI Deployment

PCIe Gen5 vs Gen4 for AI Inference: Does It Matter?

Inference Quantization vs Hardware Class: Where to Spend the Budget

LLM Concurrency Benchmarks per GPU Class

Storage Tier Design for LLM Weights and KV Cache

100GbE vs 25GbE for an AI Cluster Backbone

UPS Sizing for an AI Rack

Hardware Procurement Patterns for the Omani Government AI Buyer

HSM Integration for AI Model Key Management

Liquid vs Air Cooling for H100 and H200 Racks

BYOC: Bring-Your-Own-Hardware Certification for Sovereign AI