Kubernetes was never designed for the resource patterns of real-time inference. GPU resources are expensive, highly irregular, and tightly constrained by memory topology and model-level behavior. The result is that even well-run clusters routinely struggle to rise above 20–30% GPU utilization because the platform’s primitives don’t map to how AI workloads actually behave.
Static allocation created this efficiency ceiling. Dynamic resource management based on actual consumption is the only sustainable way out.
We built and launched AI Infra for one simple reason: Production AI requires a level of intelligence and adaptability that native Kubernetes simply wasn’t designed to deliver.
This deep-dive explains the underlying problems, where existing tools fall short, and why automated, workload-aware optimization is now required for production AI. It also shows how AI Infra changes the economics of GPUs in Kubernetes without forcing you to re-architect your stack.
What is AI Infra?
AI Infra is the end-to-end solution for managing self-hosted AI models in cloud-native environments. It augments Kubernetes, closing the loop between observability and action, without replacing any of its components. It doesn’t replace your cluster autoscaler or monitoring stack. Instead, it injects intelligence into the system, continuously observing actual workload behavior to automatically optimize everything from intelligent GPU sharing down to model-level configuration, all in real time.
AI infra delivers dynamic GPU sharing: it continuously observes real workload behavior and determines which workloads can safely share GPUs without modifying hardware or relying on static partitions. This shifts GPU management from static allocation to dynamic, usage-driven optimization.
In practice, that means the same GPUs can serve more models, at higher utilization, while maintaining – or often improving – the latency and reliability guarantees your users expect.
The Cloud-Native AI Inflection Point
For MLOps and DevOps teams managing production AI infrastructure, the economics create an impossible set of constraints:
- GPU costs are exploding: Cloud spend on AI infrastructure often exceeds the rest of the platform budget combined
- Manual tuning doesn’t scale: Every new model deployment requires hours of capacity planning
- Traditional Kubernetes autoscaling breaks: VPA, HPA, and KEDA weren’t designed for GPU v or real-time inference traffic patterns
- Performance is non-negotiable: Users still expect sub-second inference latency
Existing tools were built for batch workflows, not real-time inference at scale. Managed Kubernetes services like EKS, GKE, and AKS provide the infrastructure, but they don’t solve the utilization problem.
What’s needed is application-aware automation that understands how inference workloads actually behave and can act on that understanding continuously, not just at deployment time.
Three Architectural Gaps Kubernetes Can’t Close Alone
1. GPU Atomicity: The Indivisibility Problem
Kubernetes treats GPUs as indivisible, atomic resources. When a pod requests a GPU via `nvidia.com/gpu: 1`, the scheduler allocates an entire physical GPU to that pod.
There’s no native sharing mechanism. It’s binary: occupied or available.
This design made sense when GPUs were scarce and workloads were batch-oriented training jobs that consumed full GPU capacity for hours. But modern inference workloads tell a completely different story.
The Real-World Waste
Consider a production scenario running a quantized Llama 3 8B model for real-time inference. The model consumes 12GB of GPU memory and generates 40-60 tokens per second under typical load.
On an 80GB A100, that’s 15% memory utilization.
Compute usage stays around 30-35% because inference is memory-bound, not compute-bound. The bottleneck is memory bandwidth (HBM), as the system moves weights into GPU registers, not the calculations themselves.
But Kubernetes doesn’t see “15% memory, 30% compute utilized”. It just sees “GPU: occupied, unavailable”. That means the remaining 65GB of memory and 65-70% of compute capacity sit idle.
Now scale that across a production cluster running 50 inference services, each consuming ~30% of a GPU on average. You’re provisioning 50 full GPUs when the actual aggregate resource demand would fit on 15-20 GPUs with intelligent workload co-location.
The cost at scale: At $3-4/hour per GPU in most cloud environments, this atomicity tax translates to $100k+ annually for a mid-sized AI platform. For enterprises running hundreds of models across multiple regions, it’s tens of millions in wasted capacity.
Why The Standard Solutions Don’t Solve This
MIG (Multi-Instance GPU):
NVIDIA’s MIG technology partitions a physical GPU into fixed slices (e.g., 1/7th, 2/7th or 3/7th of an A100’s capacity). This enables sharing, but the partitioning is static. You configure MIG profiles at boot time, and changing them requires a node restart. If your workload patterns shift, you’re stuck with the wrong partitioning.
MIG is also hardware-exclusive. It’s only available on a subset of data‑center GPUs (such as A100- and H100‑class parts), so if you run inference on more cost‑effective GPUs like L4s, A10Gs, or T4s to optimize unit costs, MIG isn’t even an option.
Time-Slicing:
It allows multiple pods to share GPU access by time-multiplexing, but requires manual device plugin configuration and provides no guarantees about isolation or performance predictability. Two workloads sharing a GPU via time-slicing can interfere with each other’s memory access patterns, causing unpredictable tail latency. There’s no intelligence about which workloads can safely co-locate.
Custom Schedulers:
Building your own fractional GPU scheduler sounds appealing in theory. In practice, you’re now maintaining custom Kubernetes controller logic that needs deep understanding of GPU memory topology and workload-specific resource consumption patterns. Your custom code breaks with every Kubernetes version that changes scheduler interfaces.
The Real Gap
The problem isn’t a lack of GPU-sharing mechanisms. It’s the lack of intelligence around when, how, and which workloads can safely share GPU resources, and the automation required to execute those decisions continuously without degrading performance.
This is the gap AI Infra targets: safely co-locating compatible workloads on the same GPU based on their actual behavior, so you stop paying the atomicity tax without sacrificing performance.
2. Zero Visibility: Optimizing in the Dark
Kubernetes offers almost no workload-visibility into how GPU resources are actually being consumed. You can see that a GPU is “allocated”, but not which container is using how much memory, what the actual compute utilization looks like over time, or whether that allocation is necessary.
Traditional Kubernetes monitoring provides resource requests and limits (the values you declared at deployment time) but not runtime consumption.
For CPU and memory, tools like Prometheus and cAdvisor give you per-pod metrics. For GPUs, you’re mostly flying blind.
What You Can See
The primary tool available is `nvidia-smi`,NVIDIA’s System Management Interface, a command-line utility that reports GPU status on individual nodes. It shows GPU utilization as a single percentage.
But this single percentage is dangerously misleading. A GPU showing “85% utilization” could mean any of the following:
- Your inference workload is legitimately processing requests efficiently (valuable work)
- Your model is thrashing due to excessive batch sizes, burning GPU cycles on memory operations without productive output (waste)
- Multiple poorly-configured workloads are fighting for memory bandwidth, causing compute to wait on memory transfers (interference)
`nvidia-smi` can’t distinguish between these scenarios.
It tells you the GPU is “busy”, but not what’s making it busy, which workloads are responsible, or whether that activity is actually serving user requests vs. creating operational overhead.
What You Can’t See
- Which specific pods or containers are consuming GPU memory in real time
- How memory consumption fluctuates as traffic patterns shift
- Whether your LLM framework is over-allocating memory buffers relative to actual usage
- If multiple workloads sharing the same GPU are interfering with each other
- Which inference requests are actively using GPU compute vs. waiting in queues
- What percentage of GPU memory is allocated to model weights vs. KV-cache, vs. unused buffer space
The LLM Framework Complication
Modern LLM serving frameworks compound this problem. Tools like vLLM allocate GPU memory conservatively by default, pre-allocating large blocks for `PagedAttention` KV-caches to handle worst-case context lengths.
Real-World Example
A vLLM instance may reserve 60GB of GPU memory, even though it typically consumes only 35–40GB. That’s 20GB of waste per deployment, completely invisible without model-level metrics.
Now multiply that across dozens of models and nodes, and the efficiency losses compound fast.
The result: teams can’t separate real high-value GPU demand from overprovisioned workloads wasting capacity. Optimization becomes manual, expensive profiling work that disrupts production and never fully closes the gap.
AI Infra transforms that missing visibility into continuous, automated decisions: it understands which workloads are actually consuming GPU resources and uses that insight to reclaim wasted capacity safely, in real-time, without guessing from dashboards.
3. Cold Start Latency: The Provisioning Bottleneck
Getting GPU capacity ready when you need it is painfully slow.
For latency-sensitive workloads, this creates an impossible tradeoff between cost and performance.
The Cold Start Timeline
When Kubernetes needs to provision a new GPU node to handle increased load, here’s what actually happens
- Cloud provider provisions the instance: 60-120 seconds
- Node joins the cluster and passes health checks: 30-60 seconds
- Container runtime pulls large model images: 60-180 seconds (LLM container images are often 5-15GB in storage size)
Total: 3-7 minutes from “we need capacity” to “model is serving requests”.
For batch workloads, this delay is annoying but manageable. For user-facing inference applications, like chatbots, real-time recommendations, or content generation, this is a business critical failure. No user will wait 3 minutes for their first response.
This reality is why ‘Serverless GPU’ offerings often fail for production inference. While scaling to zero looks great on a billing report, the cold start penalty makes it unusable for real-time interactions. You cannot sacrifice user experience for idle cost savings.
The Overprovisioning Response
Teams do the only thing they can: keep expensive GPU nodes running 24/7 as availability insurance. Models stay preloaded in memory during idle periods, ready for the next request that might never come.
This ensures fast response times during traffic spikes but means GPUs sit reserved and underutilized 60-80% of the time. You’re paying $3-4/hour per GPU for capacity that only gets meaningful use during 3-4 hours of daily peak traffic.
This is why reactive provisioning forces overprovisioning and why usage-driven optimization is required.
By packing workloads based on real behavior, AI Infra lets you maintain hot capacity with far fewer always-on GPUs, so you keep latency guarantees without paying for an entire fleet of underutilized insurance nodes.
AI Infra delivers the performance of ‘always-on’ infrastructure with the efficiency usually reserved for serverless.
Why Recent Kubernetes Advances Don’t Close These Gaps
Dynamic Resource Allocation (DRA): Infrastructure, Not Intelligence
Kubernetes 1.34 (released August 2025) brought Dynamic Resource Allocation (DRA) to General Availability. DRA provides a sophisticated framework for the Kubernetes control plane to understand heterogeneous hardware, different GPU types, memory hierarchies, and topology constraints.
It’s a genuine improvement. With DRA, you can express requirements like “I need a GPU with at least 40GB memory and NVLink connectivity”. The scheduler can now make more intelligent placement decisions.
But DRA solves “what device” not “how much of the device.”
DRA tells Kubernetes how to allocate based on declared device properties. It doesn’t solve what to allocate, when to adapt, or how much resources each workload actually needs. The scheduler still requires manual policies, reactive decision-making, and human expertise to tune these policies as workload patterns evolve.
Emerging Schedulers: Better Awareness, Same Manual Burden
New GPU-aware schedulers like NVIDIA KAI improve hardware and topology awareness, make smarter placement choices, and integrate with MIG for partitioned GPU allocation. These are valuable building blocks, but they still rely on static policies and manual configuration: you define partitions and rules, the scheduler executes them, and it rarely revisits those decisions as workload behavior and traffic patterns evolve
For a platform team managing 5–10 models, that manual policy surface is painful but tolerable.
At 50+ models with heterogeneous traffic and model sizes, it turns into ongoing operational toil with little impact on overall utilization.
The key insight is that these tools improve allocation decisions at scheduling time, but they do not continuously optimize utilization over time.
AI Infra complements these schedulers by focusing specifically on utilization optimization rather than replacing your existing control plane..
AI Infra: Workload-Aware Automation for GPU Resources
Solving these three gaps requires moving beyond reactive scheduling and static configuration. What’s missing is an intelligence layer that continuously understands how workloads behave and automatically optimizes GPU allocation, placement, and utilization in real-time.
ScaleOps AI Infra brings the same application-context-aware automation we’ve proven for CPU and memory optimization to GPU-based workloads. Rather than treating GPU management as a pure scheduling problem, the platform combines continuous observation of actual workload behavior with automated, performance-safe optimization decisions.
How It Works: Application-Aware GPU Automation
AI Infra bridges the gap by moving from static allocation to application-aware automation. It combines signals like GPU memory usage, compute utilization, and workload-level context to decide which workloads can safely share capacity.
This enables safe sharing based on actual behavior without changing hardware, statically slicing GPUs at boot time, or modifying drivers. It is intelligent orchestration at the workload layer, designed to turn your existing Kubernetes and GPU investments into consistently high utilization instead of expensive headroom.
Core Capabilities
Automated GPU-Based Workload Rightsizing
The platform continuously monitors GPU memory and compute consumption to enable dynamic GPU sharing. Instead of relying on static resource requests set at deployment time (which are often guesses), it uses policy-driven optimization to manage fractional GPU allocations.
The system identifies the actual resource footprint and rightsizes the fractional allocation, enabling high-density bin-packing that allows more models to run on fewer GPUs without sacrificing performance.
Model-Level Optimization
Beyond container metrics, the platform understands behavior at the model level. By aligning model execution characteristics with GPU resource allocation, the platform surfaces resources that are reserved but underutilized and can be safely reclaimed.
Performance-Aware Observability
Deep visibility is the foundation for intelligent automation. You can see not just that a GPU is 80% utilized, but specifically which workloads are driving that demand, enabling efficient consolidation while maintaining the isolation needed for production inference.
Seamless Kubernetes Integration
AI Infra works with your existing infrastructure, delivering value in minutes, not months. The platform installs in just 2 minutes and provides immediate visibility into automation opportunities, allowing you to identify wasted GPU capacity and potential efficiency gains from day one.
Why This Approach Is Different
Automated action, not just alerts: Visibility is passive, optimization must be active. AI Infra doesn’t just report on underutilization—it resolves it. By applying dynamic GPU sharing in real-time, the system continuously reclaims wasted resources without human intervention. The result isn’t a dashboard showing what you could save, it’s a lower bill and a more efficient cluster, automatically.
Context-aware, not just usage-based: Standard tools look at generic infrastructure metrics. AI Infra looks at the model. By leveraging deep application context, the platform distinguishes between healthy processing, memory bloat, and underutilized resources. GPU resources are allocated based on inference demand and model-specific behavior, rather than just reacting to raw counters.
Amplifies Your Existing Infrastructure: It works with your ecosystem, not against it. AI Infra integrates seamlessly with your cluster autoscaler to ensure that every provisioned node is utilized to its absolute limit. By applying dynamic GPU sharing on top of your existing scaling logic, it transforms raw resources into business value, turning infrastructure efficiency into immediate, measurable cost savings.
What This Means in Production
Production deployments for self-hosted LLMs and other GPU-heavy inference services are achieving 70-80% GPU utilization compared to 20-30% baseline, translating to 50-70% reductions in infrastructure spend.
More importantly, platform teams are reclaiming the hours previously spent on manual GPU tuning (analyzing dashboards, adjusting requests, troubleshooting OOMKills) and redirecting that capacity toward shipping better models and features faster.
What’s Next
The gap between Kubernetes GPU model and what production AI workloads require is structural. Primitives like DRA and GPU-aware schedulers improve placement, but they don’t eliminate the atomicity waste, the visibility gaps, or the operational overhead that keeps GPU utilization stuck at 20–30%.
Static allocation created the GPU waste problem. Dynamic, context-aware decisions remove it.
The reason teams running AI Infra see 50–70% reductions in GPU waste is the shift from static allocation to dynamic GPU sharing. When workload behavior drives GPU usage, efficiency becomes the default.
If you’re spending nights explaining GPU bills, debugging OOMs, or trying to squeeze more throughput out of underutilized nodes, you’re doing work the platform can already automate.
Book a demo and we’ll walk through your workload patterns, show you where GPUs are currently being wasted, and explore how dynamic GPU sharing can change your unit economics. Or check the technical details to see how this approach fits into your existing Kubernetes and MLOps stack.
