Most of the cost in large-scale AI projects comes from the **infrastructure needed to train and run models**, not from the software itself. To get useful results, AI workloads need:

• High-performance accelerators (GPUs, TPUs, or specialized chips)
• High-speed networking for parallel training across thousands of GPUs
• High-throughput storage for massive datasets and frequent checkpoints
• Robust cooling and power to support dense compute clusters

Unlike traditional cloud workloads, AI training jobs:

• Run for **days, weeks, or even months**
• Consume **large volumes of GPU hours**
• Depend on parallel computing across many nodes, which increases complexity and failure risk

A simple example from the whitepaper:

• If a GPU hour costs $2 and you use 3,000 GPUs for a full day, that day costs about $144,000.
• Pre-training a foundation model over weeks can easily require **several million dollars** in GPU spend alone.

On top of raw GPU pricing, there are hidden cost drivers:

• Suboptimal GPU utilization – real-world utilization is often only 45–55% of theoretical peak (Model FLOPS Utilization, MFU).
• Job interruptions – in a 3,000-GPU cluster, failures can occur roughly every 9.8 hours, each adding about **1 hour** of recovery time in a typical environment.
• Checkpointing overhead – saving model state every few hours adds extra idle time.
• Setup and maintenance – cluster configuration and ongoing operations can consume several hours per training run.

All of this means that the **“headline” GPU price per hour** is only part of the story. The real cost is determined by how efficiently your infrastructure converts those GPU hours into actual training progress.

Nebius focuses on improving **effective throughput**, not just lowering the list price per GPU hour. The whitepaper compares Nebius to a fictional baseline provider, “Cloud X,” on a **3,000-GPU LLM training job** and shows how infrastructure design reshapes cost. Here are the key levers Nebius uses:

1. Higher effective GPU utilization

Nebius is tuned to deliver about **100% of the industry benchmark performance** for GPU utilization, while typical providers reach about **95–97%**. In the example:

• Cloud X achieves about **50% MFU** (Model FLOPS Utilization).
• Nebius, with better tuning, reaches about **51.55% MFU**.

That improvement alone reduces the core training time from **144 hours** (Cloud X) to about **139.7 hours** on Nebius.

2. Fewer interruptions and faster recovery

Large clusters are prone to failures. The difference is how often they happen and how quickly you recover:

• Cloud X: job interruptions roughly every **9.8 hours** on a 3,000-GPU cluster, with about **1 hour** to detect and recover each time.
• Nebius: average stable operation of **33 hours** (with peaks up to **56.6 hours**) and an automated recovery mechanism that restores the cluster in about **12 minutes**.

For the same 3,000-GPU job, this translates to:

• Cloud X: about **14.7 interruptions** and **14.7 hours** of recovery time.
• Nebius: about **4.2 interruptions** and only **0.8 hours** of recovery time.

3. Faster, shared-storage checkpointing

Both environments assume checkpointing every **3 hours**, but Nebius optimizes the overhead:

• Checkpoint duration with Nebius shared storage: about **3 minutes**.
• Checkpointing overhead for the example job: about **2.3 hours** on Nebius.

Because Nebius has **3.3x fewer interruptions** (4.2 vs. 14.7), the time lost rolling back to the last checkpoint is also lower—about **6.3 hours** vs. a much larger rollback overhead on Cloud X.

4. Lower setup and maintenance overhead

Nebius delivers clusters that are **ready to use**, with preinstalled drivers, libraries, and multi-stage hardware checks. This reduces setup and maintenance time to about **3 hours** at this scale, versus roughly **5 hours** in the Cloud X scenario.

5. Net impact on total training time

When you add everything up for the 3,000-GPU LLM job:

• Cloud X: about **192.3 hours** of reserved cluster time.
• Nebius: about **152.4 hours** of reserved cluster time.

That’s a saving of roughly **39.9 GPU-hours per GPU** across the cluster, which you can reinvest into additional experiments or faster iteration cycles. Even when Nebius has a higher **per-GPU-hour price** than a baseline provider, the **total cost to complete the job** is comparable or lower because you need fewer hours to reach the same training objective. In other words, Nebius helps you **reimagine TCO** by focusing on the cost of getting the job done, not just the sticker price per hour.

Nebius is designed as a **purpose-built AI cloud**, so the cost advantages extend beyond hardware efficiency. Here are the main features that influence TCO:

1. Free expert support

Nebius offers **white-glove support from experienced engineers at no extra charge**. For teams investing millions in training or inference, this has two direct benefits:

• Faster troubleshooting and optimization of training pipelines.
• Better utilization of prepaid GPU capacity, since less time is lost on misconfiguration or trial-and-error.

In contrast, many organizations would otherwise need to budget for external consultants or premium support tiers.

2. Managed AI orchestration

Nebius provides a **fully managed orchestration environment**, including solutions like **Managed Soperator** for Slurm clusters. This helps:

• Automate cluster configuration, scaling, and maintenance.
• Reduce manual DevOps work by **dozens of hours per week**.
• Lower the need for dedicated in-house DevOps headcount focused solely on AI cluster operations.

In the comparison, a typical alternative would be paying for about **192 hours of DevOps work** in the US at around **$75/hour** to configure and maintain a similar environment.

3. No buffer capacity charges

To enable fast node replacement after failures, many providers require customers to pay for **buffer capacity**—extra GPUs kept idle but reserved. Nebius takes a different approach:

• Maintains a **shared pool of spare nodes** at the platform level.
• Does **not charge** customers for this buffer capacity.
• This can reduce total cluster costs by about **10–20%** compared to vendors that bill for buffer nodes.

4. Vertically integrated AI cloud

Nebius continuously optimizes across the full stack—custom hardware, proprietary software, and energy-efficient data centers—to:

• Improve reliability and performance benchmarks.
• Increase the number of training steps or tokens you get per dollar spent.

Taken together, these capabilities help organizations **reshape their AI cost structure**. Instead of focusing only on the lowest GPU-hour price, Nebius helps teams optimize for:

• Time-to-completion of training runs
• Predictability and reliability of large clusters
• Reduced operational overhead and staffing costs
• Higher return on every GPU hour purchased

For companies scaling generative AI workloads, this combination of infrastructure performance and operational support is what ultimately drives a more sustainable AI economics model.