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.
Read article →npucluster.com · Research & Insights
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.
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.
Comparing high-bandwidth connections within a system with network-based expansion across nodes and racks.
Read article →The major building blocks of a cluster and how their interfaces shape performance and operational complexity.
Read article →An introduction to connecting multiple neural processing units into a coordinated platform for AI workloads.
Read article →How fabrics, switching, topology and collective communication affect distributed model execution.
The role of capacity, bandwidth, locality and memory pooling in training and inference cluster design.
Resource allocation, queueing, isolation, priorities and utilization for multi-tenant accelerator platforms.
Deployment patterns for model sharding, replicas, batching, KV cache and distributed inference.
A practical comparison of network choices, congestion management and operational trade-offs.
Facility power, rack design, liquid cooling, thermal limits and efficiency metrics for dense AI infrastructure.
Designing around component failures, job restarts, health monitoring, redundancy and graceful degradation.
How drivers, runtimes, schedulers and orchestration tools determine whether hardware can be used effectively.
Methods for evaluating throughput, tail latency, utilization, scaling efficiency and workload reproducibility.
Use cases and constraints for smaller regional, telecom, industrial and enterprise edge clusters.
Isolation, identity, firmware integrity, workload boundaries and governance in shared accelerator environments.
A practical framework for sizing compute, memory, networking, power, software and operational support.
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