AI Workstations, AI Servers, and NVIDIA GPU Infrastructure

Why Petronella for AI Hardware

Most AI hardware resellers in the United States are either huge order desks or pure-play GPU brokers. Petronella Technology Group is neither. We are a 24-year-old IT, cybersecurity, and AI services firm based at 5540 Centerview Dr in Raleigh, NC, founded in 2002 by Craig Petronella (CMMC-RP, CCNA, CWNE, Digital Forensics Examiner #604180). Hardware is a service line we built because our clients started asking us to design, source, and run on-premises AI infrastructure for them. We do not sell a box and disappear. We architect the workload, recommend the right NVIDIA platform, build it, rack it, secure it, support it, and tune it.

That changes the conversation. Craig writes the network and security plan himself. The CWNE (one of only a few hundred globally) and CCNA backgrounds mean InfiniBand topology, RoCE versus pure NVLink, and 400GbE versus 800GbE uplinks are not abstract bullet points. They are the actual decisions we make for clients every quarter. The entire team is CMMC-RP certified, so if your AI workload touches CUI, ePHI, or anything covered by NIST 800-171, the hardware build is documented for the assessor on day one. And we are local to the Research Triangle, which means same-day on-site response for Raleigh, Durham, Cary, Chapel Hill, Morrisville, Apex, Wake Forest, Holly Springs, and Garner. When a B200 throws an XID 79 mid fine-tuning run, you get a human in the rack, not a UPS tracking number.

We deploy the full NVIDIA roadmap: Blackwell GB300 and B300, B200, Hopper H200, DGX Spark, DGX Station GB300, every HGX 8-GPU SXM platform, and the full RTX PRO Blackwell line including the 96GB RTX PRO 6000. We also deploy AMD Instinct MI300X for clients who want maximum HBM3 capacity per dollar on ROCm. Every system ships with Ubuntu Server LTS, NVIDIA driver and CUDA stack tested against your target framework version, NCCL tuned for the actual cluster topology, Docker with the NVIDIA Container Toolkit, and your choice of inference server, training framework, and orchestration layer.

NVIDIA HGX Platforms for Custom Training Clusters

The NVIDIA HGX platform is the same 8-GPU SXM5 baseboard NVIDIA ships inside DGX, but in an open form factor that OEMs put in their own chassis. HGX gives you the same NVLink and NVSwitch topology, the same H100/H200/B200/B300 options, and the same 900GB/s+ all-to-all GPU bandwidth, while letting you choose CPU, memory, NICs, storage layout, BMC, and chassis design.

HGX is the right call when:

• You want to standardize on a specific server vendor's chassis (PDU, depth, cabling, in-band management).
• You are building a 32-GPU, 64-GPU, or larger cluster and need to control NIC selection (ConnectX-7/8, BlueField-3 DPUs) for your InfiniBand fabric.
• You have an existing platform standard (Supermicro, Dell, HPE, Lenovo, ASUS) and want to keep service contracts and spare parts consistent.
• You need extreme storage density per node (24-bay NVMe direct to GPU, GPUDirect Storage, ZFS or BeeGFS scratch).

HGX B300 8-GPU servers typically land $480K-$520K depending on CPU, NVMe, NIC, and chassis. HGX B200 runs $320K-$385K. HGX H200 remains viable at $260K-$310K when allocation timing matters. We handle cluster sizing math, power and cooling planning (an HGX B300 node pulls ~10.2kW peak; a rack of three nodes plus head node and switching needs ~35kW of cooling), and InfiniBand fabric design (rail-optimized NDR400 or XDR800 leaf-spine, BlueField-3 DPU offload for storage and security).

AI Inference vs AI Training: How to Choose the Right Hardware

The most common question on the architecture call is "do I need a DGX?" The right answer starts with another: "are you training, fine-tuning, or just running inference?" Hardware shape changes dramatically across those workloads, and over-buying is one of the most expensive mistakes in AI infrastructure right now. Worked examples live in our AI inference server buying guide for 2026, but this table is the short form:

Three non-obvious points come up on every architecture call. Memory bandwidth, not raw FLOPS, is the dominant inference bottleneck for autoregressive LLMs (that is why H200 at 4.8TB/s HBM3e is sometimes a better pure-inference buy than B200). Quantization changes which GPU you need: an FP8 quantized 70B fits a 48GB GPU under vLLM, while the same model in FP16 requires 80GB+ once you account for KV cache. And batch size and concurrency target often matter more than model size: one user hitting a 70B can be served from a workstation; 200 concurrent users need an 8-GPU server with continuous batching.

If you are weighing build-versus-rent, our AI workstation vs cloud GPU cost guide walks through 24-month and 36-month TCO, and our private AI vs cloud AI comparison covers privacy, compliance, and latency. For most SMBs with steady AI workloads, on-premise math wins inside 9 to 14 months.

Edge AI Workstations for Small Business and Field Deployment

Not every AI workload belongs in a 35kW rack. Many clients run small models in remote offices, manufacturing cells, clinic exam rooms, legal review war rooms, and mobile incident response carts. The hardware that wins in those scenarios looks nothing like a DGX. It is a quiet, low-power, single-GPU workstation running a 7B to 30B parameter model and an embedding store locally, with no cloud round-trip for sensitive data.

Three patterns we deploy regularly:

• The compliance-aware desk inference unit: RTX PRO 4500 (32GB VRAM), Ryzen 9 9950X or Intel Core Ultra 9, 64GB DDR5, 2TB NVMe, Ubuntu 24.04 LTS with Ollama or vLLM, llama.cpp for CPU fallback. Runs a Llama 3.1 8B Instruct or Phi-4 14B model entirely on-device. Used by HIPAA-covered medical practices and CMMC defense subcontractors who want generative AI without exporting data.
• The mid-density branch office inference server: 2x RTX PRO 5000 (48GB VRAM each), EPYC 9005 4124, 256GB DDR5 RDIMM, 8TB NVMe, dual 10GbE, redundant 1600W PSUs in a 2U chassis. Serves 20-50 internal users at a regional office with a 70B parameter model via vLLM tensor-parallel.
• The field deployable AI cart: Compact mini-tower with RTX PRO 4500, hardened SSD, UPS, 4G/5G failover, 802.11be Wi-Fi 7 access. Used for on-site digital forensics review, field engineering computer vision, and tactical analysis where the data cannot leave the site.

This is where our two-decade managed IT background pays off. Building the workstation is the easy part. Managing patch cycles, driver updates, model versioning, secret rotation, and endpoint security across a fleet of 30 edge AI boxes is where most internal IT teams stall, and that is what we run as a managed service. Deeper context: our post on uncloud your AI with NPUs and small LLMs.

Procurement, Financing, and White-Glove Setup

Buying a $500,000 HGX cluster is not the same as buying a laptop. The procurement, financing, and deployment side of an AI hardware purchase is where most resellers fall down and where Petronella Technology Group earns its keep.

Procurement and lead time management

NVIDIA Blackwell allocation is still constrained in 2026. We hold quarterly allocation conversations with our distribution partners and can usually get a B200, B300, or HGX slot held for an active client a quarter ahead. We also keep Threadripper PRO 9000WX chassis, Xeon W-3500 boards, RTX PRO 6000 Blackwell cards, and high-end NVMe in inventory for fast custom workstation turnaround (typically 2 to 4 weeks from PO to ship).

Financing options

Capital leases, fair-market-value operating leases, and short-term notes. The right structure depends on your tax posture (Section 179 vs bonus depreciation), the GPU generation's useful life, and whether you want to refresh in 24, 36, or 48 months. Our finance partners specialize in technology equipment so underwriting is fast.

White-glove deployment includes:

• Pre-build firmware audit (BMC, BIOS, NIC, NVMe firmware all current and consistent across cluster).
• OS install: Ubuntu 24.04 LTS Server (default), RHEL 9, or DGX OS as appropriate.
• NVIDIA driver and CUDA stack matched to your target framework version (PyTorch 2.5/2.6, TensorFlow 2.x, JAX, vLLM, TGI).
• NVIDIA Container Toolkit and Docker, configured for non-root container execution.
• NCCL benchmark and tuning (all-reduce, all-gather, broadcast at multiple message sizes).
• InfiniBand fabric verification (UFM topology check, SHARP enabled where supported, perftest at line rate).
• Storage layer setup: GPUDirect Storage, BeeGFS, ZFS scratch, or NFS RDMA depending on workload.
• Inference layer: vLLM, NVIDIA Triton, TGI, Ollama, or text-generation-webui pre-installed against your model of choice.
• Observability: Prometheus + Grafana + DCGM exporter pre-deployed for GPU temperature, power, ECC, NVLink, and utilization metrics.
• Security hardening: STIG-aware baseline, FIPS-validated cryptography where required, full-disk LUKS, audit logging to a central syslog or SIEM.
• Documentation: as-built architecture diagram, IP plan, password vault entries (1Password/Bitwarden), runbook, and assessor-ready evidence package for HIPAA/CMMC/NIST 800-171.

For deeper background on the security and observability layer, see our blog posts on enterprise LLM observability and zero-trust AI for LLMs and autonomous agents.