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Dedicated Inference Cluster

Benched.ai Editorial Team

A dedicated inference cluster is a pool of compute instances reserved exclusively for serving a single tenant's AI workloads, providing isolation, predictable performance, and custom configuration.

  When to Use

  • Regulatory or data-residency requirements forbid multi-tenant GPU sharing.
  • Latency-sensitive applications cannot tolerate noisy-neighbor spikes.
  • Workload scale justifies 24/7 reserved hardware.

  Typical Cluster Topology

LayerHardwarePurpose
Front-end4× CPU load-balancer VMsTLS termination, auth
Worker GPUs32× H100 80 GBRun model shards
High-bandwidth fabric400 G InfiniBandAll-reduce, tensor parallel
StorageNVMe SSD RAIDCheckpoints, KV cache

  Design Trade-offs

  • Higher fixed cost vs shared SaaS model.
  • Longer lead time for capacity upgrades.
  • Full control over kernel, drivers, and security posture.

  Current Trends (2025)

  • AI-specific bare-metal offerings with PCIe Gen-5 and liquid cooling deliver 20 % more sustained throughput.
  • Providers expose on-demand "single-tenant slices" billed per minute.
  • GPU disaggregation over NVLink-Switch allows dynamic resizing of clusters1.

  Implementation Tips

  1. Benchmark model throughput at target batch sizes before locking in hardware.
  2. Enable MIG or MPS for sandbox tenants during QA without impacting prod.
  3. Automate driver and firmware patching via CI to avoid manual drift.

  References

  1. NVIDIA Technical Brief, GPU Disaggregation with NVSwitch-3, 2025. ↩