What a Private GPU Cluster for 200 Users Actually Costs

Running a private GPU cluster for 200 users costs less than most CIOs fear — but far more than the hardware sticker price suggests. This episode breaks down every line item in a three-year TCO so you know exactly what moves the number.

Show Notes

Most budget conversations about private AI infrastructure start and end with GPU sticker prices — and that's exactly where the planning goes wrong. This episode of LLM.co delivers a rigorous, component-by-component total cost of ownership analysis for a private large language model cluster serving 200 knowledge workers at a regulated organization, drawing on this detailed GPU cluster cost breakdown current to late 2026. Whether you're at a mid-sized bank, a hospital network, or a defense contractor, the math is more tractable — and more illuminating — than most teams expect.

The episode walks through every major budget line over a three-year horizon, explaining not just what things cost but why each variable moves the total the way it does:

• Concurrency shapes hardware, not headcount. At 10–15% peak concurrency, 200 users typically require just two eight-GPU nodes — not a data center — and the right GPU choice (H100 vs. L40S) depends entirely on model size and inference workload.
• Capital expenditure for a two-node H100 build runs roughly $820K–$1.4M over three years, covering GPUs, high-speed networking, storage for weights and vector indexes, and rack infrastructure; an L40S equivalent comes in at about half.
• Power and cooling are a hidden budget line. Two nodes drawing ~22 kW of IT load, factoring in a realistic facility PUE, translate to $280K–$580K in colocation and power costs alone over three years.
• Software is free to download but expensive to run. Open-weight runtimes cost nothing upfront, but commercial support across 16 GPUs adds $210K–$300K over the life of the system — a line item many initial estimates omit entirely.
• Staffing is the largest non-hardware cost. A realistic 1.5–2 FTE model (platform engineer plus partial ML, security, and data engineering support) runs $400K–$700K over three years, dwarfing networking and storage budgets combined. For organizations exploring broader deployment, custom LLM deployment services can shift some of that operational burden off internal teams.
• All-in, the three-year TCO for a production-grade H100 cluster with high availability, software support, and staffing lands at $1.9M–$3.2M; an L40S build for smaller models runs $1.2M–$1.9M. For teams in regulated sectors like healthcare or finance — where private LLM infrastructure for financial services carries additional compliance requirements — understanding these ranges before procurement is essential.

The episode also covers a practical headroom rule: clusters running at 90% utilization leave no room for the fine-tuning jobs that inevitably surface next quarter, making 60% steady-state utilization the smarter design target. If you're preparing for procurement conversations, the related episode What to Put in a Private LLM RFP Before You Sign is the logical next listen.

LLM.co

cstm.ai

What is LLM.co?

Private and custom large language models — the build, the boundaries and the bill. Fine-tuning versus retrieval, running models in your own environment, evaluation you can actually trust, data governance, and the questions to ask before a vendor answers them for you.

Each episode takes one decision a team is facing — whether your problem needs a custom model at all, how to evaluate output without fooling yourself, what "private" has to mean contractually — and works it through concretely. Written for engineering and data leaders putting a model into production. Five or six minutes, one idea, no demos.

Topics include fine-tuning versus retrieval, self-hosted and private deployment, evaluation you can trust, prompt and context design, data governance and retention, cost and latency tradeoffs, and what "private" has to mean contractually.

Produced by LLM.co, private and custom large language models. Full details, services and further reading at https://llm.co