Collection: Inference Rack Pack

Production Inference Is a Different Problem Than Training. Solve It Differently.

Training and inference are not the same workload, and they should not be served by the same infrastructure. Training is a batch workload: you run it when you have data, you optimize for throughput, and a few minutes of latency in the job scheduler doesn't matter. Inference is a real-time workload: it runs continuously, it responds to user requests, and every millisecond of latency is visible to the end user. The infrastructure requirements are fundamentally different — and the Inference Rack Pack is built around those differences.

Where a training cluster optimizes for aggregate GPU throughput and all-to-all networking bandwidth, an inference infrastructure optimizes for request throughput, tail latency, and uptime. The GPU configuration is different: inference benefits from more, smaller GPU instances rather than fewer, larger ones. The networking is different: high-bandwidth north-south traffic from API clients matters more than east-west GPU-to-GPU communication. The storage is different: fast model weight loading matters more than dataset streaming throughput. And the reliability requirements are different: a training job can be restarted; a production inference service cannot go down.

The Inference Rack Pack addresses all of these differences in a single, pre-validated bundle. It's designed for teams deploying AI models to production — whether that's an internal enterprise application, a customer-facing AI product, or a private AI API service.

Optimized for Production Inference

• High-Throughput GPU Configuration: Multiple inference-optimized GPU servers configured for maximum concurrent request handling, with the GPU memory capacity to hold multiple model instances simultaneously.
• Load Balancing Infrastructure: High-speed networking with sufficient bandwidth and port density to distribute inference requests across multiple GPU servers without the network becoming a bottleneck.
• Fast Model Weight Storage: NVMe storage optimized for the random read patterns of model weight loading, enabling fast model initialization and multi-model serving without storage latency.
• Redundant Architecture: Dual-corded power, redundant networking paths, and storage data protection to meet the uptime requirements of production AI services.
• Monitoring-Ready Infrastructure: Intelligent PDUs with per-outlet power monitoring, out-of-band server management, and network management interfaces for comprehensive infrastructure observability.
• Inference Framework Compatibility: Pre-validated for TensorRT, vLLM, Triton Inference Server, and other major inference serving frameworks.

Performance Targets

The Inference Rack Pack is designed to deliver the following performance characteristics for typical LLM inference workloads: P50 latency under 100ms for 7B parameter models at moderate concurrency; P99 latency under 500ms under peak load conditions; throughput of 1,000+ tokens per second per GPU server for 7B parameter models in FP16; and 99.9% uptime with the redundant power and networking architecture included in the bundle. Pair with our Networking collection for additional load balancing and API gateway infrastructure, and our AI Storage Nodes for shared model weight storage across multiple inference nodes.

What's Included

• 3–6x inference-optimized GPU servers with redundant PSU configurations
• 1x High-port-density 25GbE/100GbE top-of-rack switch for north-south traffic
• 1x NVMe storage server for model weight serving and logging
• 2x Intelligent PDUs with per-outlet monitoring (dual-corded)
• 1x 42U rack enclosure with rails, cable management, and blanking panels
• All interconnect cables pre-selected and labeled
• Inference serving architecture documentation and framework configuration guides
• DVUN advisory support for initial deployment and performance tuning

Your Models Are Ready for Production. Is Your Infrastructure?

The gap between a model that works in development and a model that serves production traffic reliably is almost always an infrastructure gap. The Inference Rack Pack closes that gap — giving you a production-grade inference infrastructure that's ready to serve real users from day one. Request a quote for Inference Rack Pack configurations, or contact our team to discuss scaling for your specific throughput and latency requirements.