Maximizing GPU Utilization with HPE Alletra Storage MP X10000
Signal65, working together with HPE and Kamiwaza tested HPE Alletra Storage MP X10000 as a target for maintaining a distributed KV-Cache.
Digital Twins as the Control Plane for Physical AI
This paper presents a different way of thinking about what a digital twin is. A continuously learning operational mirror of a physical asset.
Improving AI Inference with AMD EPYC Host CPUs
This report explores the important role of the host CPU and evaluates the impact they have on AI performance.
Azure Maia 200 and the New Era of Hyperscaler Inference Silicon
Azure Maia 200 represents a meaningful step in Microsoft’s effort to build differentiated AI infrastructure for the inference era.
The Kompanio Ultra 910 Architectural Advantage
The ultimate goal of this Signal65 Lab Insights report is to compare the Kompanio Ultra 910 to competing silicon in both Chromebooks and Windows PCs.
Benchmarking Leadership in Open and Proprietary Models
This report evaluates agentic AI capability with the Kamiwaza Agentic Merit Index (KAMI) Benchmark and provides analysis of the agentic AI landscape as of Q1 2026.
Data Preparation for Enterprise AI: Compute-Ready Data with Dell AI Infrastructure
As enterprises accelerate AI adoption, the quality, structure, and accessibility of their data have become the primary determinant of success.
The Unified HPE ProLiant Compute Infrastructure Stack
In this report, we cover the HPE ProLiant DL145 Gen11 server powered by the AMD EPYC™ 8124P processor, evaluated to determine its resilience to thermal stress in terms of sound levels and workload latencies, along with edge deployment options and capabilities.
From Dense to Mixture of Experts: The New Economics of AI Inference
The AI infrastructure landscape is shifting again as model designs move from dense transformers to Mixture of Experts and as reasoning-heavy workloads become the norm.
Evaluating Agentic AI: Dell AI Solutions with Open Models
As agentic AI becomes the center of enterprise strategy, organizations increasingly recognize the need to run AI on premises to maintain data sovereignty, preserve operational control, and achieve predictable, cost-effective performance.