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.
5 pillar articles · 20 supporting articles
Articles
HardwarePillar
Choosing AI Inference Hardware: H100 vs H200 vs RTX 6000 Ada vs Mac Studio M3 Ultra
HardwarePillar
Sizing a Sovereign AI Appliance: Concurrent Users, Latency, and Throughput
HardwarePillar
Mac Studio M3 Ultra 192GB: The Surprise Sovereign-AI Edge Appliance
HardwarePillar
Strix Halo 128GB: AMD's Sovereign-AI Workstation Play
HardwarePillar
Datacenter-Grade AI Racks: Power, Cooling, UPS, and Air-Gap Network Design
HardwareSupporting
NVLink Topology for Multi-GPU LLM Deployments
HardwareSupporting
H100 vs H200 Memory Bandwidth: The Practical Impact on LLM Inference
HardwareSupporting
RTX 6000 Ada 48GB for Sovereign Tower Deployments
HardwareSupporting
AMD MI300X vs NVIDIA H100 for LLM Serving
HardwareSupporting
Apple Silicon for LLM Inference: Benchmarks and Real-World Numbers
HardwareSupporting
Edge GPU Appliances for Branch and Field Offices
HardwareSupporting
Air-Gap Network Architecture for Sovereign AI Clusters
HardwareSupporting
Dell vs HPE vs Supermicro AI Servers in the GCC
HardwareSupporting
Power and Cooling Calculations for a 4-GPU Rack in Muscat's Climate
HardwareSupporting