{"title":"Inference Rack Pack","description":"\u003ch2\u003eProduction Inference Is a Different Problem Than Training. Solve It Differently.\u003c\/h2\u003e\n\n\u003cp\u003eTraining 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.\u003c\/p\u003e\n\n\u003cp\u003eWhere 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.\u003c\/p\u003e\n\n\u003cp\u003eThe 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.\u003c\/p\u003e\n\n\u003ch3\u003eOptimized for Production Inference\u003c\/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eHigh-Throughput GPU Configuration:\u003c\/strong\u003e Multiple inference-optimized GPU servers configured for maximum concurrent request handling, with the GPU memory capacity to hold multiple model instances simultaneously.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eLoad Balancing Infrastructure:\u003c\/strong\u003e High-speed networking with sufficient bandwidth and port density to distribute inference requests across multiple GPU servers without the network becoming a bottleneck.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eFast Model Weight Storage:\u003c\/strong\u003e NVMe storage optimized for the random read patterns of model weight loading, enabling fast model initialization and multi-model serving without storage latency.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eRedundant Architecture:\u003c\/strong\u003e Dual-corded power, redundant networking paths, and storage data protection to meet the uptime requirements of production AI services.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eMonitoring-Ready Infrastructure:\u003c\/strong\u003e Intelligent PDUs with per-outlet power monitoring, out-of-band server management, and network management interfaces for comprehensive infrastructure observability.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eInference Framework Compatibility:\u003c\/strong\u003e Pre-validated for TensorRT, vLLM, Triton Inference Server, and other major inference serving frameworks.\u003c\/li\u003e\n\u003c\/ul\u003e\n\n\u003ch3\u003ePerformance Targets\u003c\/h3\u003e\n\u003cp\u003eThe Inference Rack Pack is designed to deliver the following performance characteristics for typical LLM inference workloads: \u003cstrong\u003eP50 latency under 100ms\u003c\/strong\u003e for 7B parameter models at moderate concurrency; \u003cstrong\u003eP99 latency under 500ms\u003c\/strong\u003e under peak load conditions; \u003cstrong\u003ethroughput of 1,000+ tokens per second\u003c\/strong\u003e per GPU server for 7B parameter models in FP16; and \u003cstrong\u003e99.9% uptime\u003c\/strong\u003e with the redundant power and networking architecture included in the bundle. Pair with our \u003ca href=\"\/collections\/networking\"\u003eNetworking\u003c\/a\u003e collection for additional load balancing and API gateway infrastructure, and our \u003ca href=\"\/collections\/ai-storage-nodes\"\u003eAI Storage Nodes\u003c\/a\u003e for shared model weight storage across multiple inference nodes.\u003c\/p\u003e\n\n\u003ch3\u003eWhat's Included\u003c\/h3\u003e\n\u003cul\u003e\n\u003cli\u003e3–6x inference-optimized GPU servers with redundant PSU configurations\u003c\/li\u003e\n\u003cli\u003e1x High-port-density 25GbE\/100GbE top-of-rack switch for north-south traffic\u003c\/li\u003e\n\u003cli\u003e1x NVMe storage server for model weight serving and logging\u003c\/li\u003e\n\u003cli\u003e2x Intelligent PDUs with per-outlet monitoring (dual-corded)\u003c\/li\u003e\n\u003cli\u003e1x 42U rack enclosure with rails, cable management, and blanking panels\u003c\/li\u003e\n\u003cli\u003eAll interconnect cables pre-selected and labeled\u003c\/li\u003e\n\u003cli\u003eInference serving architecture documentation and framework configuration guides\u003c\/li\u003e\n\u003cli\u003eDVUN advisory support for initial deployment and performance tuning\u003c\/li\u003e\n\u003c\/ul\u003e\n\n\u003ch3\u003eYour Models Are Ready for Production. Is Your Infrastructure?\u003c\/h3\u003e\n\u003cp\u003eThe 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. \u003ca href=\"\/pages\/request-a-quote\"\u003eRequest a quote\u003c\/a\u003e for Inference Rack Pack configurations, or contact our team to discuss scaling for your specific throughput and latency requirements.\u003c\/p\u003e","products":[],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0671\/0525\/9582\/collections\/inference-rack-pack.png?v=1782105284","url":"https:\/\/dvun.com\/collections\/inference-rack-pack.oembed","provider":"DVUN","version":"1.0","type":"link"}