Building generative AI models captures the imagination, but most of the spend doesn’t come during training – it comes from running those models in production. Inference often accounts for 80–90 % of GenAI spend. Compounding the problem, utilisation of those inference clusters can be as low as 15–30 %, meaning GPUs are sitting idle for large portions of the day.
The pie chart below illustrates just how dominant inference spend is compared to other stages of the pipeline.
Why Is Utilisation So Low?
There are several factors:
- Unpredictable demand: GenAI services often see spikes followed by long troughs. Over‑provisioning for the peaks leaves resources idle most of the time.
- Static deployments: Without dynamic scaling, inference clusters remain at fixed sizes regardless of traffic.
- Poor pooling: Dedicating whole GPUs to single models leads to fragmentation and low average utilisation.
Optimisation Strategies
Organisations that achieve 30–50 % better outcomes adopt a few common practices:
- GPU pooling and time‑slicing: Sharing GPUs across many inference tasks keeps utilisation high without sacrificing performance.
- Autoscaling: Scaling pods up and down in response to real‑time demand prevents over‑provisioning.
- Right‑sizing models: Smaller distilled or quantised versions of models can serve requests at lower cost.
- Cross‑functional FinOps: Collaboration between data science, MLOps and finance teams ensures decisions balance cost, performance and business impact.
By bringing visibility to inference utilisation and enforcing pooling and autoscaling policies, MLMind helps teams dramatically reduce the hidden cost of GenAI. Ready to see how much you could save? Get your free estimate and start optimising today.