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The True Cost of Training LLMs (and How to Reduce It)
The cost of training large language models (LLMs) isn’t just about how much you pay per GPU-hour. The real cost includes hardware performance, infrastructure efficiency, network design, and support reliability. This guide breaks down what actually impacts the total cost of training and how to reduce it without sacrificing performance.
Why Hourly GPU Pricing Is Misleading
Paying $12/hour for an H200 might seem like the obvious premium option… but if the network is slow, the hardware is oversubscribed, or support is lacking, your model will take longer to train and cost more overall.
The better metric is cost per completed training run, not just cost per hour.
Key Factors That Impact Training Cost
- GPU Type and Generation
The latest GPUs like NVIDIA’s H200, B200, and GB200 offer major improvements in speed and efficiency. Older models slow things down and increase training time. - Network Throughput and Efficiency
Fast interconnects like InfiniBand and NVLink let GPUs talk to each other with minimal latency. Without them, training stalls—especially for multi-GPU workloads. - System Architecture
Optimized layouts like Rail Aligned Architectures reduce bottlenecks and allow clean scaling across GPUs. Design matters. - Support and Reliability
Job failures and misconfigurations can wreck your timeline and budget. 24/7 infrastructure experts are essential when your workload breaks at 2am. - Hidden Fees
Things like data egress, storage overages, or premium support tiers can add up fast and blow past your budget forecast.
Corvex: AI-Native Cloud Built for LLMs
Corvex is purpose-built to reduce the total cost of LLM training:
- Access to NVIDIA’s H200, B200, and GB200 GPUs
- InfiniBand and NVLink standard for every deployment
- Rail Aligned Architecture optimized for throughput and scaling
- Zero oversubscription—no noisy neighbors
- No hidden fees or surprise charges
- 24/7 access to real AI infrastructure engineers
Corvex doesn’t just offer the same hardware—it designs the full stack to help you train faster and cheaper.
Apples-to-Apples Cost Comparison: H200 vs H200
Imagine training a model that requires 10,000 GPU hours on the H200:
| Provider | GPU Type | Hourly Rate | Networking | Support | Hidden Fees | Total Cost |
|---|---|---|---|---|---|---|
| Hyperscaler A | H200 | ~$12/hr | Ethernet / EFA | Tiered Docs | High | ~$120,000 |
| Corvex | H200 | ~$3/hr | InfiniBand (Rail Aligned) | 24/7 Experts | None | ~$30,000 |
Same GPU, radically different outcome. Plus, Corvex’s dedicated network fabric and optimized layout can further shorten training time—compounding the savings.
When You Should Prioritize Total Cost
Take the full training cost into account if:
- You’re training very large models
- Time-to-deploy is critical
- Budget accuracy matters
- You need real support, not just ticket queues
Final Thoughts
The cheapest GPU isn’t always the most cost-effective. Real savings come from optimized architecture, better networking, no hidden fees, and hands-on support. Corvex delivers all of that, so you can train faster, spend less, and stay focused on your model.
More Resources
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