How to Achieve Truly Serverless GPUs
Serverless GPUs are essential for efficiently handling the variable and unpredictable demands of AI inference workloads. Modal has developed a system that reduces GPU replica scaling time from tens of minutes to tens of seconds using four key technologies. Their approach aims to maximize GPU allocation utilization by aligning resource costs with actual usage patterns.
- ▪Serverless computing is well-suited for inference workloads due to their variable and unpredictable nature.
- ▪Modal's system uses cloud buffers, a custom filesystem, and CPU/GPU checkpoint/restore to drastically reduce scaling latency.
- ▪GPU Allocation Utilization measures the efficiency of inference systems by comparing actual application runtime to paid GPU time.
- ▪Spiky demand patterns in inference lead to high peak-to-average traffic ratios, making efficient scaling economically critical.
- ▪The nvidia-smi 'GPU utilization' metric reflects kernel activity but does not fully capture allocation efficiency.
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All posts Back Engineering May 12, 2026•20 minute read How to achieve truly serverless GPUs Charles Frye@charles_irl Member of Technical Staff Jonathan Belotti@jonobelotti_IO Member of Technical Staff Erik Bernhardsson@bernhardsson CEO and Founder Akshat Bubna@akshat_b CTO and Founder We are in the age of inference. Billion- to trillion-parameter neural networks are run on specialized accelerators at quadrillions of operations per second to generate media, author software, and fold proteins at massive scale. Inference workloads are more variable and less predictable than the training workloads that previously dominated.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Modal.
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