Large language model (LLM) development can be astronomically expensive. Some of the largest models require tens of thousands of GPU hours and individual runs have been known to cost more than $150 million. Even modest hyperparameter sweeps involve hundreds of jobs, quickly adding up to six‑figure bills.
Fortunately, many of these costs are optional. Cloud providers offer preemptible or spot instances at steep discounts – often 70–90 % cheaper than on‑demand. The key is to use them where interruption is acceptable and to orchestrate retries gracefully.
The chart below compares the relative cost of on‑demand training to training performed on spot instances or via other optimisations:
Strategies for Slashing Training Costs
- Use spot instances: For non‑time‑critical workloads such as hyperparameter sweeps, spot instances offer massive savings.
- Break up jobs: Design training to checkpoint frequently so that interrupted runs can resume without losing progress.
- Right‑size clusters: Allocate only the GPUs and memory needed; scale down once validation accuracy plateaus.
- Schedule intelligently: Run large jobs during off‑peak hours when spot capacity is plentiful and prices are lowest.
MLMind’s guard engine can automatically route workloads to the most cost‑effective infrastructure, making intelligent trade‑offs between speed and savings. The platform tracks spot utilisation, monitors interruption rates and provides recommendations on when to mix on‑demand and spot capacity.
Curious how much you could save on your next training run? Contact us for a personalised estimate and see why FinOps and AI belong together.