For most teams, GPU spend is the single largest line item in their AI infrastructure bill. But at high volumes, LLM production costs often include a significant amount of wasted "warm" capacity. To justify these expenses, teams must build a strong business case for their AI infrastructure investments. Managing this effectively requires a FinOps approach to AI infrastructure.
To fix this, we focus on four primary cost-saving strategies: spot instances, right-sizing, GPU sharing, and intelligent autoscaling.
Strategy 1: Fractional GPUs with NVIDIA MIG
Instead of giving every model a full A100 or H100, we use NVIDIA Multi-Instance GPU (MIG) to split high-end cards into up to seven smaller, isolated instances. This is especially effective for multi-model serving where several smaller models can share one physical card without performance interference.
Strategy 2: Spot Instance Orchestration
Training and asynchronous inference are perfect candidates for spot instances. By implementing robust checkpointing and automated rescheduling via Terraform, we've seen teams save up to 70% on raw compute costs. For a deeper dive into reducing inference spend, see our GPU cost optimization playbook.
Final Takeaway
Cost optimization isn't about buying cheaper hardware; it's about better utilization. By combining GPU sharing, spot instances, and smart scheduling, you can dramatically reduce your AI infrastructure spend while maintaining—or even improving—your production SLAs.
Struggling with spiraling GPU costs? We help teams optimize their AI infrastructure spend through better scheduling, hardware right-sizing, and cost-aware architecture. Book a free audit and we'll identify your top cost-saving opportunities.
