27d • Cost Optimization
Your AI inference cluster is probably burning 60% of its GPU budget on idle silicon. Here's the fix.
Quick gut check: open your Kubernetes cluster right now and run kubectl describe node on any GPU node. Look at the nvidia.com/gpu requests vs. actual utilization in your monitoring. If you're like most teams I audit, you'll find pods that reserve a full A100 or L4 and then sit at 8-15% GPU utilization all day.
That's not a tuning problem. That's money on fire.
Here's why it happens. Kubernetes treats nvidia.com/gpu as a countable, non-divisible resource by default. Request 1 and you get the whole card, even if your model only needs a sliver of it. Most inference models (anything under ~7B params, embeddings, rerankers, OCR, classic CV) don't come close to saturating a modern GPU. So you end up with a 1:1 pod-to-GPU mapping and a fleet that's mostly idle.
Three levers fix the bulk of this, in order of effort:
1) GPU time-slicing (NVIDIA device plugin). Lets multiple pods share one physical GPU by oversubscribing time. Zero hardware requirement, works on almost any card, configured with a single ConfigMap. Best for bursty, latency-tolerant inference. Downside: no memory isolation, a leaky pod can OOM its neighbors.
2) MIG (Multi-Instance GPU) on A100/H100/H200. Hardware-partitions one GPU into up to 7 isolated instances, each with dedicated memory and compute. Real isolation, predictable performance. Best for production multi-tenant inference. Downside: only on data-center GPUs, fixed partition profiles.
3) Right-size the request, then bin-pack onto Spot. Once pods share GPUs cleanly, schedule them onto spot/preemptible GPU nodes with proper PodDisruptionBudgets and a fallback node pool. This is where the 60-80% savings actually land.
Real example from a recent audit: a team running 12 inference services on 12 dedicated L4s. After time-slicing the latency-tolerant services 4:1 and moving them to spot, they dropped to 4 GPUs with a 1-GPU on-demand safety pool. ~65% monthly GPU cost cut, no SLA regression.
The mistake almost everyone makes: jumping straight to "buy reserved instances" or "negotiate committed-use discounts" before fixing utilization. You're just locking in your waste at a discount. Fix density first, commit second.
Discussion question: What's your current pod-to-GPU ratio for inference, and have you tried time-slicing or MIG yet? If you haven't measured actual GPU utilization (not requests, utilization), that's your homework for today. Drop your numbers below and I'll help you spot the quick wins.
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Your AI inference cluster is probably burning 60% of its GPU budget on idle silicon. Here's the fix.
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