---
title: "Full TCO of an on-premise GPU cluster: from capex to the all-in €/GPU-hour and the break-even against cloud"
url: https://blog.lo0.es/en/posts/full-tco-on-premise-gpu-cluster-capex-gpu-hour-break-even/
markdown: https://blog.lo0.es/en/posts/full-tco-on-premise-gpu-cluster-capex-gpu-hour-break-even/index.md
lang: en
date: 2026-06-16
lastmod: 2026-09-13
author: "José Manuel Román"
description: "A full TCO model of an on-premise GPU cluster with 4×H100 SXM nodes: capex breakdown (GPUs, HGX baseboard, InfiniBand NDR network, NVMe, rack/PDU), opex (energy × PUE, staff, maintenance), derivation of the all-in €/GPU-hour and the €/1M tokens, and a break-even analysis against cloud on-demand, reserved and spot with a sensitivity table."
tags: ["finops","tco","on-premise","capex","opex","break-even","h100","data-series"]
translations:
  es: https://blog.lo0.es/posts/tco-on-premise-gpu-cluster/index.md
---

# Full TCO of an on-premise GPU cluster: from capex to the all-in €/GPU-hour and the break-even against cloud


> Notation: amounts in **N €** or **N USD** (source denominated in dollars); decimal point; comma as thousands separator. The dollar sign is not used (it is the formula delimiter). Data centred on Europe/Spain. Generic example hardware: a cluster of N nodes, each with 4×H100 SXM5 80 GB.

## TL;DR

A 4×H100 SXM5 node costs between **150,000 USD and 200,000 USD** in total capex (GPUs + server + network + storage + prorated rack). Amortised over 3 years with European opex (energy at ~0.116 €/kWh industrial, PUE 1.54 average or 1.2 with liquid, 0.3 FTE of staff), the **all-in** cost ranges from **3.10 USD/GPU-hour** (100 % utilisation) to **6.20 USD/GPU-hour** (50 % utilisation). The break-even against AWS p5 on-demand (~6.88 USD/GPU-hour) is crossed at around **70 % utilisation**; against a neocloud 3-year reserved (~1.49–2.10 USD/GPU-hour), on-prem never closes the gap in that scenario. Utilisation is the variable that decides the cost axis, not the price of the hardware.

---

## The model: declared assumptions

Every calculation below starts from these assumptions. Changing any of them moves the conclusion; the sensitivity section quantifies by how much.

| Parameter | Base value | Sensitivity range |
|---|---|---|
| Node | 4×H100 SXM5 80 GB (HGX baseboard) | — |
| Cluster | N nodes (per-node model; scales linearly) | 1–32 nodes |
| Capex amortisation | 3 years (straight line) | 3–5 years |
| GPU utilisation | 70 % | 30 %–100 % |
| Energy price | 0.116 €/kWh (industrial Spain, Sept. 2025) | 0.06–0.20 €/kWh |
| PUE | 1.54 (global average, Uptime Institute 2025) | 1.15–1.80 |
| Exchange rate | 1 USD = 0.93 € (reference Jun. 2026) | — |

> Energy source: [GlobalPetrolPrices · Spain Business Electricity, Sept. 2025](https://www.globalpetrolprices.com/Spain/electricity_prices/). PUE source: [Uptime Institute Global Data Center Survey 2025](https://datacenter.uptimeinstitute.com/rs/711-RIA-145/images/2025.Annual.Survey.Report.pdf) — the global average PUE has been stuck at 1.54 for the sixth consecutive year; hyperscalers 1.10–1.15; colocation/enterprise 1.58–1.80; facilities less than 5 years old, 1.48. PUE 1.2 is achievable with direct-to-chip liquid cooling.

---

## Capex breakdown per 4×H100 SXM5 node

### GPUs

| Component | Unit price (USD) | Quantity | Subtotal (USD) | Source and date |
|---|---|---|---|---|
| H100 SXM5 80 GB (card) | 30,000–40,000 | 4 | 120,000–160,000 | [GMI Cloud, Apr. 2026](https://www.gmicloud.ai/en/blog/nvidia-h100-gpu-pricing-2026-rent-vs-buy-cost-analysis) · [Introl, Apr. 2026](https://introl.com/blog/gpu-infrastructure-tco-5-year-cost-model) |

The range reflects market variability and volume discounts (5–15 % for orders above 50 units). H100 SXM5 cards require NVIDIA's **HGX baseboard**; they are not sold loose for direct installation in standard servers.

### Server / HGX baseboard

A complete 4×H100 SXM node uses NVIDIA's **HGX H100 4-GPU baseboard** plus a compatible host server. Reference models: Supermicro SYS-421GU-TNXR (4U, dual Intel Xeon 4th Gen, HGX H100 4-GPU) and its Dell equivalent.

| Component | Estimated cost (USD) | Note |
|---|---|---|
| Server chassis + CPU (2× Xeon) + RAM (512 GB DDR5) + redundant PSU | 18,000–25,000 | Based on a Supermicro SYS-821GE bare without GPUs at ~24,806 USD ([xicomputer.com](https://www.xicomputer.com/Solutions/Data-Center-Servers/SYS-821GE-TNHR.asp), Jun. 2026); scaled to the 4-GPU |
| HGX H100 4-GPU baseboard | included in the GPU price | NVIDIA HGX platform; no separate public price |
| Inter-GPU NVLink (inside the node) | included in the baseboard | 4 GPUs connected by NVLink 4.0 on the HGX baseboard |

> Marketing claim (no independent verification): Supermicro announces datacenter energy cost reductions of up to 40 % with liquid cooling in its HGX H100 servers ([Supermicro press release](https://www.supermicro.com/en/pressreleases/supermicro-launches-industrys-first-nvidia-hgx-h100-8-and-4-gpu-h100-servers-with-liquid-cooling)).

### InfiniBand NDR network

For a multi-node cluster with tensor parallelism across nodes, the GPU-to-GPU network is critical. NDR InfiniBand (400 Gb/s per port) is the de facto standard for HGX clusters.

| Component | Estimated cost per node (USD) | Source / Note |
|---|---|---|
| NVIDIA Quantum-2 NDR 400G switch (64 ports, prorated across N nodes) | 2,000–4,000 | Switch ~35,000 USD ([Introl, Apr. 2026](https://introl.com/blog/gpu-infrastructure-tco-5-year-cost-model)); at 16 nodes, ~2,200 USD/node |
| InfiniBand NDR cables/transceivers (4 ports per node × ~1,000 USD/port) | 4,000 | Estimate based on ~1,000 USD per optical transceiver ([Introl, Apr. 2026](https://introl.com/blog/gpu-infrastructure-tco-5-year-cost-model)) |
| **InfiniBand network (prorated per 4-GPU node)** | **~6,000–8,000** | — |

> For inference serving inside a single node (4 GPUs with NVLink), the inter-node network is less critical than for multi-node training. For disaggregated prefill-decode workloads spread across nodes, InfiniBand NDR is necessary.

### NVMe storage

| Component | Estimated cost (USD) | Note |
|---|---|---|
| Local NVMe (4 TB × 2 U.2/E1.S drives, working datasets and checkpoints) | 2,000–4,000 | ~500–1,000 USD/TB enterprise NVMe, 2025 |
| Shared object storage (NAS/MinIO, prorated per node) | 2,000–5,000 | Varies with the total capacity of the cluster |
| **Total storage per node** | **~4,000–9,000** | — |

> Introl models 50 TB per GPU for effective operation in training clusters ([Introl, Apr. 2026](https://introl.com/blog/gpu-infrastructure-tco-5-year-cost-model)); for pure inference the requirement is significantly lower (model weights + logs).

### Rack, PDU and datacenter connectivity

| Component | Estimated cost per node (USD/year) | Source |
|---|---|---|
| Rack colocation (high density, 10–15 kW per node) | 5,000–12,000/year | [Encoradvisors · Colocation Pricing 2026](https://encoradvisors.com/data-center-colocation-pricing/): high density 3,000–6,000 USD/month per rack; at 2 nodes per rack, ~1,500–3,000 USD/month per node = 18,000–36,000 USD/year in tier-1; lower in Spain |
| Rack PDU, electrical cabling (prorated) | 500–1,000 per node (amortised capex) | Inside the colocation line item or in an own datacenter |

> Colocation in Spain/Europe is structurally cheaper than in US tier-1 markets (New York, Silicon Valley). For an own datacenter, replace it with the cost of your own space plus amortisation of the electrical and cooling infrastructure.

### Capex summary per 4×H100 SXM5 node

| Line item | Range (USD) | Midpoint |
|---|---|---|
| GPUs (4× H100 SXM5) | 120,000–160,000 | 140,000 |
| Server chassis + CPU/RAM/PSU | 18,000–25,000 | 21,500 |
| InfiniBand NDR network (prorated) | 6,000–8,000 | 7,000 |
| NVMe + object storage | 4,000–9,000 | 6,500 |
| PDU/rack/other (capex) | 2,000–5,000 | 3,500 |
| **Total capex per node** | **150,000–207,000** | **178,500** |

Sources: [GMI Cloud (Apr. 2026)](https://www.gmicloud.ai/en/blog/nvidia-h100-gpu-pricing-2026-rent-vs-buy-cost-analysis), [Introl (Apr. 2026)](https://introl.com/blog/gpu-infrastructure-tco-5-year-cost-model), [Spheron (Apr. 2026)](https://www.spheron.network/blog/llm-inference-on-premise-vs-cloud/), [xicomputer.com (Jun. 2026)](https://www.xicomputer.com/Solutions/Data-Center-Servers/SYS-821GE-TNHR.asp).

---

## Opex breakdown per 4×H100 SXM5 node (annual)

### Energy

A 4×H100 SXM5 node at full load draws approximately:

$$P_{\text{node}} = 4 \times 700\,\text{W (TDP H100 SXM5)} + 800\,\text{W (server)} \approx 3.6\,\text{kW (IT)}$$

The total datacenter power includes the cooling overhead, expressed by the **PUE**:

$$P_{\text{total}} = P_{\text{IT}} \times \text{PUE}$$

$$\text{annual energy cost} = P_{\text{IT}} \times \text{PUE} \times 8{,}760\,\text{h} \times \text{kWh price}$$

With the base values (PUE 1.54; 0.116 €/kWh):

$$\text{energy/year} = 3.6\,\text{kW} \times 1.54 \times 8{,}760\,\text{h} \times 0.116\,\text{EUR/kWh} \approx 5{,}475\,\text{EUR}$$

With a Spanish solar PPA (Q3 2025 reference price: ~34 €/MWh = 0.034 €/kWh according to [PV Tech, Oct. 2025](https://www.pv-tech.org/european-solar-ppa-prices-fall-below-35-mwh-q3-2025/)):

$$\text{energy/year (solar PPA)} = 3.6 \times 1.54 \times 8{,}760 \times 0.034 \approx 1{,}604\,\text{EUR}$$

| Energy scenario | Price (€/kWh) | Energy cost/year per 4-GPU node |
|---|---|---|
| Spanish solar PPA (Q3 2025) | 0.034 | ~1,604 € |
| Industrial Spain (Sept. 2025) | 0.116 | ~5,475 € |
| European average (industrial tariff) | 0.160 | ~7,550 € |
| Worst case (no PPA, high tariff) | 0.200 | ~9,437 € |

### Staff / operations

Staff cost is the most variable line item with cluster size. For a small cluster (2–8 nodes), the rule of thumb is **0.3–0.5 FTE per cluster** of GPU infrastructure support ([Spheron, Apr. 2026](https://www.spheron.network/blog/llm-inference-on-premise-vs-cloud/)).

| Cluster size | Estimated FTE | FTE cost (€/year, Western Europe) | Cost per 4-GPU node (€/year) |
|---|---|---|---|
| 2–4 nodes | 0.3 FTE | ~120,000 | 36,000–18,000 |
| 8–16 nodes | 0.5 FTE | ~120,000 | 7,500 |
| 32+ nodes | 1–2 FTE | ~120,000 | 3,750–7,500 |

> Indicative salary reference: a GPU infrastructure engineer with CUDA, InfiniBand and Kubernetes knowledge in Western Europe, ~90,000–140,000 €/year fully loaded. Introl's figures ([Apr. 2026](https://introl.com/blog/gpu-infrastructure-tco-5-year-cost-model)) in USD (~275,000 USD/year for the US) reflect the North American market, which is appreciably higher.

### Maintenance and support

| Line item | Annual cost (% of hardware capex) | Per 4-GPU node (midpoint) |
|---|---|---|
| Vendor maintenance / support | 5–10 % of capex | ~7,000–14,000 USD → ~6,500–13,000 € |
| GPU failure rate (~5 % annual) × replacement cost | 5 % × 4 GPUs × ~35,000 USD = ~7,000 USD expected | ~6,500 € (amortised as a provision) |
| Minor spares (cables, modules) | ~500–1,000 € | — |

> Introl quotes GPU failure rates of 2–3 % per year in small clusters; Google Research documented ~9 % annualised in Meta's 16,384-GPU H100 cluster ([Introl, Apr. 2026](https://introl.com/blog/gpu-infrastructure-tco-5-year-cost-model)). 5 % is used here as a conservative intermediate value.

### Depreciation (for accounting purposes)

Straight-line depreciation turns capex into an annual flow comparable to the cost of committed cloud:

$$\text{annual depreciation} = \frac{\text{node capex}}{\text{amortisation years}}$$

| Node capex (USD) | 3-year amortisation (USD/year) | 5-year amortisation (USD/year) |
|---|---|---|
| 150,000 (minimum) | 50,000 | 30,000 |
| 178,500 (average) | 59,500 | 35,700 |
| 207,000 (maximum) | 69,000 | 41,400 |

> H100 hardware depreciates fast: secondary-market analyses put the residual value at 20–40 % of the purchase price after 3 years ([Introl, Apr. 2026](https://introl.com/blog/gpu-infrastructure-tco-5-year-cost-model)). The arrival of Blackwell GB200/GB300 accelerates the perceived obsolescence.

### Annual opex summary per 4×H100 SXM5 node (base scenario, 8-node cluster)

| Line item | Base scenario (€/year) | Range |
|---|---|---|
| Energy (PUE 1.54; 0.116 €/kWh) | 5,475 | 1,604–9,437 |
| Staff (0.5 FTE × 8 nodes, prorated) | 7,500 | 3,750–36,000 |
| Maintenance / support / failures | 9,000 | 5,000–15,000 |
| Rack colocation (Spain, high density) | 6,000 | 3,000–15,000 |
| **Total opex per node** | **~28,000** | **~13,000–75,000** |

The extreme range reflects the difference between a well amortised own datacenter with a solar PPA and cheap energy (minimum opex) and tier-1 colocation with market tariffs and senior staff.

---

## Deriving the all-in €/GPU-hour

### Formula

$$\text{EUR/GPU-hour all-in} = \frac{\frac{\text{node capex}}{\text{years}} + \text{node annual opex}}{4\,\text{GPUs} \times 8{,}760\,\text{h} \times u}$$

where \(u\) is the average annual utilisation (0 to 1).

See the cost-per-token identity in [cost per token and per request](https://blog.lo0.es/en/posts/gpu-hour-to-cost-per-token-on-prem-vs-cloud/index.md) for the connection with throughput.

### €/GPU-hour table by utilisation and scenario

**Average capex (178,500 USD → ~166,000 €), 3-year amortisation → 55,300 €/year.**

| Utilisation | Opex/year (base, €) | Total cost/year (€) | Useful GPU-hours/year | **€/GPU-hour** |
|---|---|---|---|---|
| 30 % | 28,000 | 83,300 | 10,512 | **7.93** |
| 50 % | 28,000 | 83,300 | 17,520 | **4.75** |
| 70 % | 28,000 | 83,300 | 24,528 | **3.39** |
| 80 % | 28,000 | 83,300 | 28,032 | **2.97** |
| 100 % | 28,000 | 83,300 | 35,040 | **2.38** |

**Low-opex scenario** (solar PPA, own datacenter, large cluster): opex/year ~13,000 €.

| Utilisation | Total cost/year (€) | **€/GPU-hour** |
|---|---|---|
| 50 % | 68,300 | **3.90** |
| 70 % | 68,300 | **2.78** |
| 80 % | 68,300 | **2.43** |
| 100 % | 68,300 | **1.95** |

**High-opex scenario** (market tariff, expensive colocation, small cluster): opex/year ~75,000 €.

| Utilisation | Total cost/year (€) | **€/GPU-hour** |
|---|---|---|
| 50 % | 130,300 | **7.44** |
| 70 % | 130,300 | **5.31** |
| 80 % | 130,300 | **4.65** |
| 100 % | 130,300 | **3.72** |

---

## From €/GPU-hour to €/1M tokens

The cost-per-token identity connects the hardware cost with the inference cost:

$$\text{EUR/1M tokens} = \frac{\text{EUR/GPU-hour} \times 10^6}{\text{throughput (tok/s)} \times 3{,}600}$$

For reference throughputs on H100 SXM5 with vLLM (see [capacity planning for on-premise inference](https://blog.lo0.es/en/posts/capacity-planning-on-premise-llm-inference/index.md)):

| Model | Typical throughput (tok/s per GPU) | Source |
|---|---|---|
| Llama-3 70B FP8, high batch | ~2,800 | Series B benchmarks |
| Llama-3 8B FP16, medium batch | ~9,000 | Series B benchmarks |
| Mixtral 8×7B, high batch | ~4,500 | Series B benchmarks |

**€/1M tokens table in the base scenario (€/GPU-hour 3.39 at 70 % utilisation):**

| Model | Throughput (tok/s) | €/1M tokens |
|---|---|---|
| Llama-3 70B FP8 | 2,800 | **~0.336** |
| Llama-3 8B FP16 | 9,000 | **~0.105** |
| Mixtral 8×7B | 4,500 | **~0.209** |

**At 50 % utilisation (€/GPU-hour 4.75):**

| Model | €/1M tokens |
|---|---|
| Llama-3 70B FP8 | **~0.471** |
| Llama-3 8B FP16 | **~0.147** |

Occupancy (batching) multiplies the effective throughput and lowers the €/1M tokens without changing the hardware; it is analysed in [GPU utilisation as a FinOps lever](https://blog.lo0.es/en/posts/gpu-idle-cost-not-on-any-invoice/index.md).

---

## Break-even on-prem vs cloud

### The break-even formula

Break-even happens when the total annual on-prem cost equals the annual cloud cost at the same utilisation:

$$\text{annual cloud cost} = \text{cloud GPU-hour price} \times 4\,\text{GPUs} \times 8{,}760\,\text{h} \times u$$

$$\text{break-even}: \quad \frac{\text{capex/year} + \text{opex/year}}{4 \times 8{,}760 \times u} = \text{cloud GPU-hour price}$$

Solving for the break-even utilisation:

$$u^* = \frac{\text{capex/year} + \text{opex/year}}{4 \times 8{,}760 \times \text{cloud GPU-hour price}}$$

### Break-even table by cloud mode and on-prem scenario

**Base on-prem scenario** (capex/year 55,300 €, opex/year 28,000 €, total 83,300 €/year per 4-GPU node):

| Cloud reference (price/GPU-hour) | USD equiv. | Break-even utilisation \(u^*\) | Note |
|---|---|---|---|
| Neocloud on-demand (Lambda/Spheron ~2.90 USD) | 2.90 USD (~2.70 €) | **>100 %**, on-prem does not compete | Neocloud on-demand is cheaper even at full utilisation |
| Neocloud 3-year reserved (CoreWeave ~1.49–2.10 USD) | ~1.80 USD (~1.67 €) | **>100 %**, impossible | Neocloud reserved beats on-prem in every scenario of this model |
| AWS p5 on-demand (6.88 USD/GPU-hour) | 6.88 USD (~6.40 €) | **~47 %** | Above 47 %, the average on-prem beats AWS on-demand |
| AWS p5 3-year reserved (~2.97 USD/GPU-hour) | 2.97 USD (~2.76 €) | **>100 %** | — |
| GCP A3 on-demand (~10.98 USD/GPU-hour) | 10.98 USD (~10.21 €) | **~29 %** | Above 29 %, on-prem beats GCP on-demand |
| Azure ND H100 v5 on-demand (~12.29 USD/GPU-hour) | 12.29 USD (~11.43 €) | **~26 %** | — |

**Low-opex scenario** (total 68,300 €/year):

| Cloud reference | Break-even utilisation |
|---|---|
| AWS p5 on-demand (6.88 USD ≈ 6.40 €) | **~38 %** |
| Neocloud on-demand (2.90 USD ≈ 2.70 €) | **~91 %** |
| Neocloud 3-yr reserved (1.80 USD ≈ 1.67 €) | **>100 %** |

**High-opex scenario** (total 130,300 €/year):

| Cloud reference | Break-even utilisation |
|---|---|
| AWS p5 on-demand (6.88 USD ≈ 6.40 €) | **~72 %** |
| GCP A3 on-demand (~10.21 €) | **~45 %** |
| Azure on-demand (~11.43 €) | **~41 %** |

### Reading the break-even table

- Against **neoclouds** (on-demand or reserved), on-prem TCO **does not reach break-even** in any scenario of the base model. Neocloud reserved beats on-prem even at 100 % utilisation, because its hourly price is below the all-in cost of your own hardware. This is consistent with the analysis in [cloud GPU: on-demand, reserved and spot](https://blog.lo0.es/en/posts/cloud-gpu-on-demand-reserved-spot-neoclouds-2026-prices/index.md).
- Against **on-demand hyperscalers** (AWS, GCP, Azure), on-prem does have an achievable break-even: around **26–72 %** utilisation depending on the scenario. At medium-high utilisation (>70 %), on-prem clearly beats AWS/GCP/Azure on-demand.
- The variable that moves the break-even most is **opex** (staff above all), not hardware capex. A well sized cluster in cheap colocation with PPA energy can lower the threshold by 20 percentage points compared with the high scenario.
- For GDPR data, the break-even against US hyperscalers is skewed: the sovereignty axis rules out US hyperscalers before cost does (see [sovereign on-premise vs hyperscalers](https://blog.lo0.es/en/posts/sovereign-on-premise-vs-hyperscalers-data/index.md)).

> **Diagram:** Break-even on-prem vs cloud by utilisation: base on-prem crosses AWS on-demand at 47 per cent, GCP at 29 per cent and Azure at 26 per cent utilisation. Neoclouds have no feasible break-even. — [see the HTML version](https://blog.lo0.es/en/posts/full-tco-on-premise-gpu-cluster-capex-gpu-hour-break-even/)

---

## Sensitivity analysis

### TCO vs utilisation

The all-in cost per GPU-hour varies inversely with utilisation because capex is fixed:

$$\frac{d(\text{EUR/GPU-hour})}{du} = -\frac{\text{capex/year} + \text{opex/year}}{4 \times 8{,}760 \times u^2} < 0$$

Going from 50 % to 80 % utilisation cuts the €/GPU-hour by \(\frac{4.75 - 2.97}{4.75} \approx 37\,\%\) in the base scenario. That 37 % reduction requires no hardware change, only more efficient scheduling (see [GPU utilisation as a FinOps lever](https://blog.lo0.es/en/posts/gpu-idle-cost-not-on-any-invoice/index.md)).

| Utilisation | €/GPU-hour (base scenario) | Change vs 50 % |
|---|---|---|
| 30 % | 7.93 | +67 % |
| 50 % | 4.75 | reference |
| 70 % | 3.39 | −29 % |
| 80 % | 2.97 | −37 % |
| 100 % | 2.38 | −50 % |

### TCO vs energy price

| Energy price (€/kWh) | Energy opex/year | €/GPU-hour (70 % util.) | Change vs base |
|---|---|---|---|
| 0.034 (solar PPA) | 1,604 € | **3.00** | −12 % |
| 0.116 (industrial ES, base) | 5,475 € | **3.39** | reference |
| 0.160 (European average) | 7,550 € | **3.54** | +4 % |
| 0.200 (high tariff) | 9,437 € | **3.67** | +8 % |

Energy has a **moderate** impact on total TCO (8–12 % variation between the extremes), because hardware capex dominates. Over a very long amortisation (5 years) with a solar PPA, however, energy drops from 6 % to 1 % of total TCO and the differential is amplified. The price of energy matters more for the carbon footprint (CSRD) than for TCO when capex is dominant.

### TCO vs PUE

| PUE | Cooling overhead | Energy/year (0.116 €/kWh) | €/GPU-hour (70 % util.) |
|---|---|---|---|
| 1.15 (liquid cooling, new facilities) | +15 % | 2,166 € | **3.21** |
| 1.20 (liquid, modern datacenter) | +20 % | 2,259 € | **3.23** |
| 1.48 (facilities <5 years, Uptime 2025) | +48 % | 3,490 € | **3.33** |
| 1.54 (global average, Uptime 2025) | +54 % | 3,627 € | **3.39** |
| 1.80 (legacy colocation) | +80 % | 4,260 € | **3.47** |

The difference between PUE 1.15 (liquid) and 1.80 (legacy) is barely **~8 %** of the €/GPU-hour at 70 % utilisation, because energy is only a fraction of TCO. PUE matters far more for the **absolute energy cost** and for **CSRD reporting** than for total TCO when hardware is the dominant component.

### TCO vs amortisation years

| Amortisation | Capex/year (average node, USD) | €/GPU-hour (70 % util., base opex scenario) |
|---|---|---|
| 3 years | 59,500 USD (~55,300 €) | **3.39** |
| 4 years | 44,625 USD (~41,500 €) | **2.99** |
| 5 years | 35,700 USD (~33,200 €) | **2.72** |

Stretching the amortisation from 3 to 5 years lowers the €/GPU-hour by ~**20 %**, assuming the hardware remains competitive and the resale market supports the residual value. With the refresh cycle accelerated by Blackwell GB200/GB300, a 5-year amortisation carries a higher risk of technological obsolescence.

### Sensitivity heat map (€/GPU-hour at 70 % utilisation, base scenario)

| | PUE 1.15 | PUE 1.54 | PUE 1.80 |
|---|---|---|---|
| **3-yr amort., solar PPA (0.034 €)** | 2.72 | 2.74 | 2.76 |
| **3-yr amort., industrial (0.116 €)** | 3.21 | 3.39 | 3.47 |
| **5-yr amort., industrial (0.116 €)** | 2.54 | 2.72 | 2.80 |
| **3-yr amort., high tariff (0.200 €)** | 3.44 | 3.67 | 3.78 |

---

## Decision table: cost/control/sovereignty Pareto

The table below crosses the four dimensions with no implicit hierarchy; the ordinal reading depends on each organisation's constraints.

| Option | €/GPU-hour | Initial capex | Full stack control | EU sovereignty | Elasticity | Operational risk |
|---|---|---|---|---|---|---|
| **On-prem (util. >70 %, low opex)** | 2.40–3.00 | high (150–207k USD/node) | total | total | none | hardware failure, idle |
| **On-prem (util. <50 %, base opex)** | 4.75–7.93 | high | total | total | none | capex with no return |
| **Neocloud 3-year reserved (CoreWeave, Lambda)** | 1.49–2.10 USD | none | partial (API) | depends on the provider | rigid contract | minimal interruption |
| **Neocloud on-demand (Lambda, Spheron)** | 2.49–3.44 USD | none | partial | depends | total | no interruption |
| **AWS p5 on-demand** | 6.88 USD | none | minimal | NO (CLOUD Act) | total | no interruption |
| **AWS p5 3-year reserved** | ~2.97 USD | financial commitment | minimal | NO (CLOUD Act) | rigid | no interruption |
| **Sovereign EU cloud (Scaleway, Nebius EU)** | 2.15–3.85 USD | none | partial | yes (EU) | total | no interruption |
| **Hybrid on-prem base + EU cloud peak** | 2.00–3.50 (weighted) | medium | high | yes (EU) | elastic peak | operational complexity |

> "EU sovereignty" column: US hyperscalers (AWS, GCP, Azure) are subject to the US CLOUD Act regardless of the datacenter region. Nebius has a Dutch legal entity; CoreWeave is a US company. See the full analysis in [sovereign on-premise vs hyperscalers](https://blog.lo0.es/en/posts/sovereign-on-premise-vs-hyperscalers-data/index.md).

> "Full stack control" column: on-prem lets you choose the driver version, the kernel, the NCCL configuration, MIG partitioning, and any system parameter. Cloud options offer control at container/pod level, with the hypervisor and firmware opaque.

The cost/sovereignty Pareto frontier for GDPR data excludes US hyperscalers, leaving: **on-prem**, **sovereign EU cloud**, and the **hybrid**. Among those three, the deciding variable is sustained utilisation and the predictability of traffic (see [capacity planning for on-premise LLM inference](https://blog.lo0.es/en/posts/capacity-planning-on-premise-llm-inference/index.md)).

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## Integration with the series FinOps model

The all-in €/GPU-hour of on-prem is the number that feeds the cost allocation pipeline of the series:

1. **Cost-per-token identity** ([cost per token and per request](https://blog.lo0.es/en/posts/gpu-hour-to-cost-per-token-on-prem-vs-cloud/index.md)): engine throughput × €/GPU-hour → €/1M tokens.
2. **Chargeback and showback** ([chargeback and showback in GPU multitenancy](https://blog.lo0.es/en/posts/opencost-gpu-cost-allocation-kubernetes/index.md)): the all-in €/GPU-hour is the internal price charged to each tenant of the multi-tenant cluster.
3. **Utilisation as a lever** ([GPU utilisation as FinOps](https://blog.lo0.es/en/posts/gpu-idle-cost-not-on-any-invoice/index.md)): raising utilisation from 50 % to 80 % cuts the €/GPU-hour by 37 % without changing the hardware, the highest ROI in on-prem FinOps.
4. **Capacity planning** ([capacity planning for on-premise LLM inference](https://blog.lo0.es/en/posts/capacity-planning-on-premise-llm-inference/index.md)): the number of nodes to buy depends on the percentile of base load you want to cover in iron.
5. **Cloud comparison** ([cloud GPU: on-demand, reserved and spot](https://blog.lo0.es/en/posts/cloud-gpu-on-demand-reserved-spot-neoclouds-2026-prices/index.md)): the all-in €/GPU-hour goes head to head with the cloud price in table A7 to compute the break-even.

---

## Sources

- Spheron · LLM Inference On-Premise vs GPU Cloud: 2026 Cost and Break-Even Analysis (abr. 2026) — https://www.spheron.network/blog/llm-inference-on-premise-vs-cloud/
- Introl · GPU Infrastructure TCO Model: 5-Year Cost Analysis for Enterprise AI (abr. 2026) — https://introl.com/blog/gpu-infrastructure-tco-5-year-cost-model
- GMI Cloud · NVIDIA H100 GPU Pricing: 2026 Rent vs. Buy Cost Analysis (abr. 2026) — https://www.gmicloud.ai/en/blog/nvidia-h100-gpu-pricing-2026-rent-vs-buy-cost-analysis
- Uptime Institute · Global Data Center Survey 2025 (PDF oficial) — https://datacenter.uptimeinstitute.com/rs/711-RIA-145/images/2025.Annual.Survey.Report.pdf
- Uptime Institute · Global Data Center PUE Stalls at 1.54 (comunicado, oct. 2025) — https://mgrid.org/2025/10/01/uptime-institute-data-center-pue-stagnation-2025-liquid-cooling/
- GlobalPetrolPrices · Spain Business Electricity Price (sept. 2025) — https://www.globalpetrolprices.com/Spain/electricity_prices/
- PV Tech · European Solar PPA Prices Fall Below 35 €/MWh in Q3 2025 (oct. 2025) — https://www.pv-tech.org/european-solar-ppa-prices-fall-below-35-mwh-q3-2025/
- Xi Computers · Supermicro SYS-821GE-TNHR 8U GPU Server Pricing (jun. 2026) — https://www.xicomputer.com/Solutions/Data-Center-Servers/SYS-821GE-TNHR.asp
- Encoradvisors · Data Center Colocation Pricing 2026 — https://encoradvisors.com/data-center-colocation-pricing/
- CloudZero · H100 GPU Cost In 2026: Buy, Rent, and Cloud Pricing Compared — https://www.cloudzero.com/blog/h100-gpu-cost/
- Spheron · AI Inference Power Consumption and GPU Electricity Costs: 2026 Guide — https://www.spheron.network/blog/ai-inference-power-electricity-cost-2026/
- NVIDIA · DGX SuperPOD H100 Electrical Specifications (documentación oficial) — https://docs.nvidia.com/dgx-superpod/design-guides/dgx-superpod-data-center-design-h100/latest/electrical.html