# On-Prem LLM Total Cost of Ownership (TCO) Calculator

> Source: https://netray.co/tools/on-prem-llm-tco-calculator
> Publisher: Netray (netray.co), AI and integration engineering for ERP and manufacturing systems.
> Section: Free Tools

## Short answer

Model the full multi-year cost of running LLMs on your own hardware, including GPU capex, power, cooling, support contracts, and operations staffing.

## Category

on-prem-ai

## Tool

**Kind:** calculator

**Inputs:**

#### Number of GPUs

**Key:** gpu_count

**Type:** number

**Default:** 4

**Min:** 1

**Max:** 64

**Step:** 1

**Unit:** GPUs

**Help:** Total inference GPUs across all servers in the deployment.

#### GPU class

**Key:** gpu_unit_cost

**Type:** select

**Default:** 30000

**Help:** Street price per card; select the class closest to your quote.

**Options:**

##### NVIDIA H100 80GB (~$30,000)

**Value:** 30000

##### NVIDIA A100 80GB (~$17,000)

**Value:** 17000

##### NVIDIA L40S 48GB (~$11,000)

**Value:** 11000

##### RTX 6000 Ada 48GB (~$7,500)

**Value:** 7500

#### Electricity cost

**Key:** power_cost_kwh

**Type:** slider

**Default:** 0.12

**Min:** 0.05

**Max:** 0.4

**Step:** 0.01

**Unit:** $/kWh

**Help:** Blended commercial rate; US industrial average is roughly $0.08-0.15.

#### Operations staffing

**Key:** ops_fte

**Type:** slider

**Default:** 0.5

**Min:** 0.1

**Max:** 3

**Step:** 0.1

**Unit:** FTE

**Help:** Fraction of a full-time engineer dedicated to running the platform.

#### Fully loaded engineer cost

**Key:** fte_cost

**Type:** number

**Default:** 140000

**Min:** 60000

**Max:** 300000

**Step:** 5000

**Unit:** $/yr

**Help:** Salary plus benefits and overhead for the engineer maintaining the stack.

#### Amortization horizon

**Key:** years

**Type:** slider

**Default:** 3

**Min:** 1

**Max:** 5

**Step:** 1

**Unit:** years

**Help:** Most enterprises depreciate GPU hardware over 3-5 years.

**Outputs:**

#### Hardware capex

**Key:** hardware_capex

**Expr:** gpu_count * gpu_unit_cost + ceil(gpu_count / 8) * 15000

**Format:** currency

**Description:** GPUs plus server chassis, networking, and storage at ~$15K per 8-GPU node.

#### Annual power and cooling

**Key:** annual_power_cooling

**Expr:** round(gpu_count * 0.7 * 8760 * power_cost_kwh * 1.5)

**Format:** currency

**Description:** Assumes ~700W average draw per GPU and a datacenter PUE of 1.5.

#### Total annual opex

**Key:** annual_opex

**Expr:** round(annual_power_cooling + ops_fte * fte_cost + hardware_capex * 0.05)

**Format:** currency

**Description:** Power, staffing, and a 5% of capex allowance for support and spares.

#### Total cost of ownership

**Key:** total_tco

**Expr:** hardware_capex + annual_opex * years

**Format:** currency

**Description:** Capex plus opex over your chosen amortization horizon.

**Primary:** true

#### Effective monthly run rate

**Key:** monthly_run_rate

**Expr:** round(total_tco / (years * 12))

**Format:** currency

**Description:** TCO spread evenly across every month of the horizon.

**Primary:** true

**Disclaimer:** Estimates only. Actual costs vary with vendor pricing, rack density, facility PUE, and support contracts. Use this as a planning baseline, not a quote.

## Content

**Intro:** This calculator estimates the full multi-year total cost of ownership for running large language models on your own infrastructure, built for IT and finance leaders at manufacturers evaluating on-prem AI. Cloud API bills are visible; on-prem costs hide in power, cooling, staffing, and support contracts. The model combines GPU and server capex with realistic operating assumptions - 700W average draw per GPU, a 1.5 PUE, and a 5% annual hardware support allowance - so you can compare an on-prem deployment against cloud spend on equal footing before committing budget.

**Sections:**

#### How the TCO math works

**Body:** Hardware capex is your GPU count times unit price, plus roughly $15,000 per 8-GPU node for chassis, CPUs, RAM, NVMe storage, and networking. Annual power assumes each GPU averages 700W under mixed inference load (peak TDP is higher, but utilization is rarely 100%), multiplied by 8,760 hours, your electricity rate, and a 1.5 PUE to capture cooling overhead. Opex adds a staffing fraction - most 4-8 GPU deployments need 0.3-0.7 FTE once stable - and a 5% of capex line for vendor support, warranty extensions, and spare parts. Total TCO is capex plus opex across your amortization horizon, and the monthly run rate spreads that evenly for budget comparisons.

#### Benchmarks behind the defaults

**Body:** The defaults reflect what Netray sees in mid-market manufacturing deployments in 2025-2026. A 4x H100 server lands between $130K and $150K fully configured. Industrial electricity in the US averages $0.08-0.15/kWh, while colocation adds a premium. Enterprise PUE ranges from 1.3 in modern facilities to 1.8+ in legacy server rooms. Steady-state operations for a single-purpose inference cluster typically consume half an engineer, though the first six months run closer to a full FTE during setup and tuning.

**Bullets:**

- 4x H100 server, fully configured: $130K-$150K capex
- Average GPU power draw under inference load: 600-750W
- Typical enterprise PUE: 1.3 (modern) to 1.8 (legacy server room)
- Steady-state ops staffing for a small cluster: 0.3-0.7 FTE

#### How to interpret your results

**Body:** Compare the monthly run rate against your current or projected LLM API bill at equivalent volume. If the on-prem run rate is lower and your workload is steady, on-prem usually wins - and the gap widens each year after hardware is paid off. If the numbers are close, weigh the non-financial factors: data sovereignty, air-gap requirements, latency, and vendor lock-in often justify a modest premium for regulated manufacturers. Also stress-test the staffing assumption; underestimating operations effort is the most common source of TCO surprise we see.

#### How Netray helps you act on these numbers

**Body:** Netray designs, procures, and operates on-prem LLM stacks for aerospace, defense, and discrete manufacturers - including fully air-gapped deployments. We validate your TCO model against real vendor quotes, right-size the GPU footprint to your actual workload instead of a spec sheet, and can run the platform under a managed service so the ops FTE line shrinks. Most engagements start with a fixed-fee architecture and cost-validation sprint that turns this estimate into a board-ready business case.

**Cta:** Run your numbers, then let Netray's on-prem AI team pressure-test the business case with real vendor quotes.

**Faq:**

#### Is on-prem LLM hosting really cheaper than cloud APIs?

**Answer:** It depends on volume and utilization. Below roughly 500 million tokens per month, per-token APIs are usually cheaper because you avoid capex and staffing. Above that, steady workloads on owned hardware typically cost 40-70% less over three years. The crossover moves earlier if you have data sovereignty or air-gap requirements that force expensive cloud isolation tiers, and later if your workload is spiky and hardware would sit idle.

#### Why does the calculator add 50% to raw GPU power consumption?

**Answer:** That is the PUE (power usage effectiveness) multiplier. Every watt a GPU consumes generates heat that cooling systems must remove, and facilities add distribution losses on top. A PUE of 1.5 means the facility draws 1.5 watts for every watt of IT load - a realistic mid-point between modern purpose-built datacenters (1.2-1.3) and typical enterprise server rooms (1.6-1.9).

#### What costs does this calculator not include?

**Answer:** It excludes software licensing (most inference stacks like vLLM are open source), model fine-tuning compute, initial implementation services, and facility buildout if you need new racks, power circuits, or cooling capacity. It also assumes no GPU refresh within the horizon. For a 5-year horizon, budget a mid-cycle refresh or accept declining relative performance versus newer silicon.

## Search terms

on-prem llm tco calculator, self-hosted llm cost, llm infrastructure total cost of ownership, gpu server cost calculator, on premise ai cost estimate, enterprise llm hosting cost, h100 server tco

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