Infrastructure & hardware resale
Centered Networks designs, sources, and integrates multi-vendor infrastructure for mid-market and enterprise teams. Cisco Meraki, HP, Dell, Nutanix, VMware, and Citrix sit alongside our Microsoft cloud practice. A dedicated AI infrastructure practice covers GPU compute, high-throughput storage, and the low-latency fabric organizations need to run serious AI workloads inside their own perimeter.
Multi-vendor by design · 20+ years of campus, branch, and datacenter work · Trusted by enterprise IT and engineering teams
Vendors we design with
Cisco Meraki HP Dell Nutanix VMware Citrix Microsoft AzureWhat we do
Most of our infrastructure work follows the same arc: a conversation about what the workload actually needs, a design that fits the team and the budget, a sourcing plan across the right vendors, and an integration that hands back a working environment — not a pile of boxes on a loading dock.
Networking
Wired and wireless on Cisco Meraki, HP Aruba, or whatever fits your environment. Site surveys, dashboard standup, segmentation, SD-WAN policy, branch rollouts. Designed so the people who keep the lights on can actually keep the lights on.
Compute
HP ProLiant, Dell PowerEdge, and Nutanix HCI. Capacity planning that maps to actual workload — not the spec sheet a vendor would prefer you bought. VMware vSphere and Nutanix AHV across virtualization, with Citrix where the desktop workload calls for it.
Storage
Dell PowerStore and HP Alletra for primary; Nutanix Files and object storage for unstructured and AI training corpora; immutable backup tiers for ransomware resilience. Sized against IOPS and throughput patterns, not gigabytes.
Datacenter buildouts
End-to-end colo and on-prem datacenter design: power and cooling sizing, top-of-rack and spine fabric, cabling, structured commissioning. We have stood up everything from a single cabinet to multi-row deployments with redundant power and dedicated fiber.
The deliverable is a working environment your team can run, not a procurement spreadsheet.
Selected clients
Centered Networks is best known for our nonprofit, foundation, and rural-hospital practice, but the infrastructure work has always run alongside it. A few of the for-profit and enterprise teams we have supported over the years:
How we work
Most resellers lead with a quote. We lead with the workload. The right gear depends on what you are actually running, how your team operates, what you already own, and where you want to be in 36 months.
01
What the environment runs today, what it needs to run in two years, who operates it, what failure modes you cannot tolerate. The vendor decision falls out of these answers, not the other way around.
02
A reference design with capacity, redundancy, and operating posture documented. A sourcing plan across the vendors that fit the workload — usually two or three. Pricing that reflects what the gear actually costs at our partner tier.
03
We place the orders, manage RMA risk, stage and image gear in our facilities where appropriate, and coordinate logistics to the site or colo. No surprise hardware arriving in a loading dock without a plan.
04
Engineering hands install, configure, and commission. Runbooks delivered for the team that will operate the environment. Optional CompleteCare wrap if you want us running it alongside you.
AI infrastructure
Hosted AI APIs are the right answer for many workloads. They are not the right answer for all of them. For mid-market and enterprise organizations doing serious AI work — sensitive data, steady-state inference, model fine-tuning, engineering teams iterating against bare metal — owning the iron starts to make sense. We design and integrate the compute, storage, and fabric that runs those workloads.
01
Sensitive IP, regulated data, customer PII, and proprietary training corpora never leave your environment. The compliance posture matches the data: HIPAA, SOC 2, ITAR, or whatever your customers and regulators require. No third-party logs, no shared inference endpoints, no surprises in a sub-processor list.
02
For steady-state inference at volume, or sustained training and fine-tuning, the per-token cost of hosted APIs eventually overtakes the amortized cost of owned GPUs. The crossover is workload-dependent, but for teams running production inference at scale it usually arrives faster than expected.
03
Engineering teams iterate without per-call cost, quota friction, or rate limits. ML engineers get bare-metal GPU access for fine-tuning experiments. The team that builds the agent does not have to choose between speed and budget on every commit.
04
Azure for elastic burst, Foundry, and Copilot. On-prem for steady-state inference and sovereign data. The two patterns coexist. We build the network and identity bridge between them so workloads land where they should — not where licensing or org politics put them.
A sketch of how AI workloads typically land on-prem. Real designs are sized against your workload, not picked from a catalog.
GPU compute
HP ProLiant DL380a Gen11 or Dell PowerEdge XE9680 with NVIDIA H100, H200, or L40S GPUs. Sized from a single 4-GPU box for dev and inference, up to 8-GPU nodes for fine-tuning. Liquid-cooled designs available where rack power density requires it.
High-throughput storage
Dell PowerScale, HP Alletra, or Nutanix Files for training corpora and model artifacts. NVMe tiers for active datasets, capacity tiers for cold corpora and checkpoints. Sustained read throughput is the spec that matters — not raw capacity.
Low-latency fabric
Spine-leaf 100/400 GbE or NVIDIA Spectrum and InfiniBand for GPU-to-GPU traffic during distributed training. Lossless fabric, RDMA where the workload calls for it. The network is often where on-prem AI projects underperform; we size it deliberately.
Orchestration and tooling
Nutanix or VMware for virtualization where it fits; Kubernetes with NVIDIA GPU Operator for containerized inference and training. NIM microservices, vLLM, Triton Inference Server, and the rest of the modern serving stack. Identity bridged to Entra so access maps to the same controls as everything else.
Hybrid bridge to cloud
Azure ExpressRoute or VPN, AWS Direct Connect, Google Partner Interconnect as the path back to whichever cloud you operate in. On-prem inference for steady-state and sensitive workloads; cloud AI runtimes (Azure AI Foundry, AWS Bedrock, Google Vertex AI) for elastic experimentation and burst. One operating model across both. See multi-cloud and AI on every cloud →
Production inference at scale
Customer-facing copilots, retrieval-heavy agents, real-time classification, and any workload running millions of calls per day. Owned inference hardware pays back inside 12 to 24 months at sustained volume — and the latency floor is yours to set.
Fine-tuning and model adaptation
LoRA, QLoRA, full fine-tunes, and continued pre-training on proprietary corpora. The model artifacts live with you. No data leaves the perimeter during training. Repeatable runs without negotiating quota with a hosted provider.
Engineering dev and test
ML engineers, applied scientists, and platform teams iterating against real hardware. No per-call meter, no quota throttling, no surprise bill at the end of the sprint. Shared cluster or dedicated nodes depending on team size.
Regulated and sovereign data
Healthcare records, financial transactions, defense and ITAR-controlled work, customer IP under NDA. On-prem inference and training where the regulatory or contractual posture rules out hosted endpoints. Documented controls a customer or auditor can verify.
AI infrastructure decisions are easy to get expensively wrong. Sizing the cluster to the workload, picking the right storage tier, getting the network fabric right the first time — these are the line items that determine whether the project succeeds. We have engineered around the failure modes. Bring us in before the purchase order goes out.
Why Centered Networks
We hold partnerships with Cisco Meraki, HP, Dell, Nutanix, VMware, and Citrix. We are not a single-vendor shop trying to make every problem look like the SKU we sell. The right design uses the right gear from the right vendors, sourced at partner pricing.
Every engagement is staffed by engineers who could operate the environment themselves. The bill of materials is shaped by the design, not the other way around. The integration hands back something a working team can actually run.
We are a Microsoft Solutions Partner across five designations, including Infrastructure (Azure) and Data & AI (Azure). Hybrid designs — on-prem inference with Azure burst, Entra identity across both — come from one team, not a finger-pointing contest between vendors.
Wrap CompleteCare around the environment if you want us operating it. Or take the runbooks and run it yourselves. The No-Lock-In Promise applies here as it does everywhere else: 30-day exit, your data, your environment, your call.
Whether you are refreshing a branch network, designing a colo footprint, or sizing a GPU cluster for AI workloads, we will scope the conversation around the workload first and the vendor second. No commitment beyond the conversation.
If your work is squarely on the AI infrastructure side, mention it in the form — an engineer with that background will take the call.
A senior engineer will reply within one business day about your infrastructure scope.