Scale-Up vs Scale-Out: Two Ways to Grow NPU Infrastructure
Comparing high-bandwidth connections within a system with network-based expansion across nodes and racks.
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Special Coverage: Independent coverage of Independent coverage of neural processing unit cluster architecture and distributed AI compute, including accelerator interconnects, memory systems, scale-up and scale-out design, scheduling, model serving, power and cooling, cluster management, reliability and workload economics.
Published articles
NPU Cluster Architecture: Nodes, Fabric, Memory and Storage
The major building blocks of a cluster and how their interfaces shape performance and operational complexity.
Read article →What Is an NPU Cluster? A Guide to Distributed Neural Compute
An introduction to connecting multiple neural processing units into a coordinated platform for AI workloads.
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Interconnects for NPU Clusters: Bandwidth, Latency and Topology
How fabrics, switching, topology and collective communication affect distributed model execution.
Memory Systems for NPU Clusters: HBM, DRAM and Shared Memory
The role of capacity, bandwidth, locality and memory pooling in training and inference cluster design.
Scheduling AI Workloads Across a Shared NPU Cluster
Resource allocation, queueing, isolation, priorities and utilization for multi-tenant accelerator platforms.
Serving Large Language Models on NPU Clusters
Deployment patterns for model sharding, replicas, batching, KV cache and distributed inference.
NPU Cluster Networking: Ethernet, InfiniBand and Emerging Fabrics
A practical comparison of network choices, congestion management and operational trade-offs.
Power and Cooling for High-Density NPU Clusters
Facility power, rack design, liquid cooling, thermal limits and efficiency metrics for dense AI infrastructure.
Reliability in NPU Clusters: Fault Domains and Recovery
Designing around component failures, job restarts, health monitoring, redundancy and graceful degradation.
Cluster Software: Compilers, Runtimes and Orchestration
How drivers, runtimes, schedulers and orchestration tools determine whether hardware can be used effectively.
Benchmarking NPU Clusters: Measuring Real Production Performance
Methods for evaluating throughput, tail latency, utilization, scaling efficiency and workload reproducibility.
NPU Clusters at the Edge: Distributed AI Beyond the Data Center
Use cases and constraints for smaller regional, telecom, industrial and enterprise edge clusters.
Security and Multi-Tenancy for Shared NPU Infrastructure
Isolation, identity, firmware integrity, workload boundaries and governance in shared accelerator environments.
Planning an NPU Cluster: Capacity, Cost and Deployment Checklist
A practical framework for sizing compute, memory, networking, power, software and operational support.
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