LLM.co

On-prem AI hardware is now the majority deployment choice for enterprise LLMs — but the numbers that actually drive smart procurement decisions aren't the ones most vendors lead with. This episode breaks down the figures that matter in 2026.

Show Notes

On-premises AI deployment has quietly crossed a tipping point, and the statistics behind that shift are reshaping how enterprise technology leaders build their business cases. This episode of LLM.co digs into the data from the 2026 on-prem AI hardware statistics report — cutting through vendor-polished figures to surface the numbers that hold up when someone technical enters the room.

From adoption trends to memory math to power and economics, the episode walks through the key metrics every infrastructure or IT leader should have at their fingertips before a procurement decision lands on a steering committee agenda:

  • Adoption has flipped fast. On-prem and edge inference accounted for just 12% of enterprise AI workloads in 2023. By 2026, that share has reached 55% — with on-premises deployments now holding roughly 59% of LLM deployment share, concentrated in regulated industries.
  • Security, not cost, is the dominant driver. Data privacy compliance ranks as the top LLM provider selection factor for 31% of enterprises, and 44% cite security as the single greatest barrier to AI adoption — making data residency and audit trails the most defensible arguments in a business case, not TCO projections alone.
  • VRAM is the number that shapes everything else. The working rule is approximately 0.6 GB of VRAM per billion parameters at 4-bit quantization — meaning a 70B model needs ~42 GB in INT4, before accounting for KV cache. At 128K token context, that cache alone can add ~41 GB on top of model weights, a figure that catches many buyers off-guard. Teams exploring their own requirements can get a practical starting point with the AI hardware estimator for GPUs and VRAM.
  • Power density is where budgets break. Traditional data center racks draw 7–10 kW. A modern AI rack draws 40–100 kW, and the most dense cluster hardware can demand up to ~142 kW in a single rack. Whether an existing facility can support that load — upstream feeds, transformer capacity, UPS — is a question that belongs in day one of the project plan, not as a late-stage footnote.
  • Cooling thresholds follow density. Air cooling becomes inadequate above roughly 30–40 kW per rack; direct-to-chip liquid cooling handles up to ~150 kW; immersion covers 100–200 kW and above. Current cluster-class hardware sits squarely in liquid-only territory, making cooling method a design-phase decision, not a procurement-phase one.
  • On-prem economics require honest utilization math. The break-even against cloud sits at consistent GPU utilization above 60–70% over three years, where on-prem can yield 30–50% long-term savings. Below that threshold, fixed hardware costs work against the investment — and the planning rule is simple: rent until projected workloads justify ownership.

For more on building a resilient private AI infrastructure, the episode How to Audit and Fix Your Private AI Stack Before It Breaks in Production covers how to stress-test what you already have before committing to new hardware. If your workloads extend to document-heavy workflows at scale, it's also worth exploring Document AI for extraction, classification, and vision as a way to scope real memory and throughput requirements before sizing a system.

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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