Training LLMs? Here's Why Startups Lose Money Fast

The recent surge of open-source LLMs like Meta’s Llama models and Mistral AI’s Mistral 7B has made it easier for startups to obtain cutting-edge model architectures. However, training these models comes with massive costs and operational challenges. In this post, we’ll break down the challenges startups face when training models and explore how Hyperstack can help manage and reduce these costs. 

The Infrastructure Gap for Startups in LLM Training

For startups building AI products, the cloud is often the go-to option for training large language models (LLMs). It offers scalability and removes the hassle of sourcing and managing physical GPUs, a difficult task given the global demand. But this flexibility comes at a cost, especially when dealing with resource-intensive LLMs.

To understand the scale, consider this: training a model like GPT-MoE-1.8T reportedly required 25,000 NVIDIA A100 GPUs running continuously for several months. Even more “approachable” models like Llama 2 (7B or 13B) require extensive compute power and careful cost planning. NVIDIA CEO Jensen Huang has shared that with the Hopper H100 GPUs, you'd still need roughly 8,000 of them running for 90 days to complete such a training cycle.

That level of infrastructure is far beyond what most startups can afford. As a result, very few early-stage teams train LLMs from scratch. Instead, the smarter and far more cost-effective approach is to start with pre-trained models like Meta’s Llama or OpenAI’s GPT and fine-tune them for your specific product or use case.

3 Reasons Startups Struggle with LLM Training

For AI startups (and even larger teams) operating on limited budgets, the costs of training LLMs can be a major hurdle. Beyond the raw expense, several common pain points make it difficult to predict and control spending:

Unpredictable Cloud Bills

It’s often hard to know exactly how long a training job will run or how much it will cost until it’s done. Model training is an iterative process and experiments can run longer than expected or require additional runs after debugging. This means your teams frequently get unpleasant surprises on their cloud invoice. In extreme cases, a single extended training job can blow past your monthly budget. The lack of real-time insight or alerts on usage can turn into a six-figure bill seemingly “out of nowhere” which is terrifying for a startup.

Idle or Underutilised Resources

During LLM development, there are many periods when expensive GPU instances sit idle, for example, waiting for code changes, data preprocessing or team decisions. Unfortunately, if you’ve rented a GPU by the hour, you pay for it even while it’s doing nothing. Many startups over-provision “just in case” leading to underused GPUs that still incur full cost. This can waste a huge chunk of your budget. 

A Complex Stack

Building and maintaining an infrastructure stack for training LLMs spanning data pipelines, model orchestration, distributed training and checkpointing requires DevOps and MLOps expertise that many early-stage teams may lack. This complexity slows down iteration and increases the chance of costly mistakes or downtime.

How Hyperstack Can Help

Hyperstack provides flexible and cost-efficient infrastructure to support LLM workloads. Here’s how Hyperstack can lower the cost of training LLMs, while giving teams much better control over their spending:

Flexible Pricing Models

With Hyperstack, you can choose between on-demand or reservation. Spin up GPUs without any commitment when experimenting, or lock in lower hourly pricing by reserving instances for longer-term projects. For instance, the powerful NVIDIA H100 SXM is available at around $2.40/hour on-demand or as low as $1.90/hour when reserved, ideal for scaling cost-effectively based on your needs. 

Usage-Based Billing

Hyperstack on-demand GPUaaS platform bills by the minute. This means you are only billed for what you use. This leads to substantial savings over time, especially during iterative model development, where jobs may frequently pause or end early.

Pause and Resume with Hibernation

Hyperstack’s hibernation feature lets you pause GPU when not in active use, saving its state to disk and halting billing until you resume. This is particularly useful for long-running LLM training jobs that involve idle time for analysis or debugging. Instead of wasting money on idle GPUs, you only pay for active compute time, resuming from exactly where you left off without losing progress.

Real-Time Usage Insights

Hyperstack’s billing dashboard gives you clear visibility into your resource usage and costs if you reserve a GPU. With real-time data, you can monitor GPU hours, storage and network expenses down to individual VMs or projects. Set budget alerts or spending limits to avoid surprises, making it easier to manage LLM training budgets proactively and make adjustments before costs spiral.

Team Management with RBAC and Org Billing

Hyperstack supports organisation-wide accounts with role-based access control (RBAC), so you can assign different permissions to different team members. All usage is consolidated under a single billing account, with the ability to restrict who can deploy high-cost resources. For example, some may only access a budget GPU such as the NVIDIA RTX A6000, while senior ML engineers can run powerful NVIDIA A100 or NVIDIA H100s. This ensures spending is monitored and controlled, preventing unexpected charges from unmonitored usage.

Ready to Cut Your LLM Training Costs?

Try Hyperstack today and get flexible, high-performance GPUs with transparent billing, hibernation and more. Start building smarter with Hyperstack.

FAQs

What makes LLM training so expensive on the cloud?

LLM training requires massive compute power, leading to high GPU rental costs, extended runtimes and unpredictable billing.

Why don’t most startups train LLMs from scratch?

Training from scratch is costly and complex. Startups often prefer fine-tuning existing models to save time and resources.

How does Hyperstack help reduce GPU costs?

Hyperstack offers per-minute billing, reservation options and hibernation features to eliminate idle GPU time and maximise budget efficiency.

Can I monitor and control my team’s usage on Hyperstack?

Yes, Hyperstack provides real-time usage insights, team-level permissions, and org-wide billing to help manage and control expenses.

What if my training job needs to pause temporarily?

You can use Hyperstack’s hibernation feature to pause training, save the session state and stop billing until you're ready to resume.