How do I manage GPU capacity for real-time inference across multiple apps?

Managing GPU capacity for real-time inference across multiple apps is mostly about controlling latency, isolation, and utilization at the same time. A good setup usually combines scheduling, quotas, model serving, and observability.

1) Start with the workload shape

Before picking tools, classify each app by:

• Latency target: e.g. p95 < 50 ms
• Traffic pattern: steady vs bursty
• Model size / memory footprint
• Concurrency needs
• SLA priority: which apps must win during contention

This tells you whether you need:

• hard isolation for critical apps,
• shared pooling for efficiency,
• or hybrid for both.

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2) Use a model-serving layer, not raw GPU access

Don’t let every app manage GPUs directly. Put inference behind a serving layer such as:

• NVIDIA Triton
• KServe
• Ray Serve
• BentoML
• vLLM / TGI for LLMs

These help with:

• batching,
• dynamic batching,
• request queueing,
• model loading/unloading,
• concurrency controls,
• metrics.

This is usually the biggest win for capacity management.

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3) Separate “allocation” from “execution”

Use a resource manager to assign GPU capacity, and a serving layer to run inference.

Common patterns

• Kubernetes + GPU device plugins
• Node pools by GPU type
• Namespaces + quotas
• Priority classes / preemption
• MIG (Multi-Instance GPU) on supported NVIDIA GPUs for hard partitioning

Why this helps

• One app can’t starve others.
• You can reserve capacity for critical workloads.
• You can scale apps independently.

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4) Consider MIG or partitioning if you need strong isolation

If you have NVIDIA A100/H100-class GPUs, MIG can split one physical GPU into multiple isolated slices.

Good for:

• multiple small/medium inference apps,
• predictable latency,
• stronger isolation,
• reduced noisy-neighbor effects.

Tradeoff:

• less flexible than pooled sharing,
• not ideal if workloads vary a lot,
• capacity can fragment.

If you don’t have MIG, you can still approximate isolation with:

• per-app GPU pinning,
• request limits,
• separate node pools,
• admission control.

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5) Use batching carefully

Batching boosts throughput, but can hurt latency if unmanaged.

Best practice

• Enable dynamic batching with a short max delay.
• Set max batch size based on latency SLOs.
• Tune separately per model.

For real-time apps:

• small batches,
• low queue delay,
• aggressive timeout handling.

If the app is ultra-low-latency, you may prefer:

• no batching,
• smaller models,
• dedicated GPU slice.

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6) Enforce quotas and priorities

When multiple apps share GPUs, you need rules for contention.

Controls to use

• GPU quotas per app/team
• max concurrent requests
• per-tenant rate limits
• queue depth limits
• priority tiers
• preemption for critical traffic

Example policy

• Tier 1: customer-facing checkout fraud detection
• Tier 2: recommendation service
• Tier 3: offline/interactive analytics

If GPUs are exhausted, Tier 3 waits or gets throttled first.

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7) Autoscale on the right signals

GPU utilization alone is not enough.

Scale based on:

• request rate,
• queue length,
• p95/p99 latency,
• time spent waiting for GPU,
• KV cache or memory pressure for LLMs,
• model-specific throughput metrics.

Practical point

A GPU can show “low utilization” and still be overloaded if:

• requests are serialized,
• memory is fragmented,
• queue delay is growing.

So use application-level metrics, not just nvidia-smi.

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8) Avoid overcommitting GPU memory

For real-time serving, OOMs are deadly.

Strategies

• reserve memory headroom,
• cap per-model memory,
• pin model replicas to known GPU sizes,
• use smaller precision (FP16/BF16/INT8/4-bit if acceptable),
• monitor fragmentation and cache growth.

For LLMs specifically:

• track KV cache usage,
• limit max context length,
• cap concurrent sequences,
• use paged attention where available.

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9) Cache and warm models

Cold starts hurt latency.

Use:

• warm pools of replicas,
• preloaded models,
• lazy loading only where acceptable,
• keep frequently used models resident.

If multiple apps share a GPU:

• keep the hottest models always loaded,
• evict rarely used ones based on policy,
• use separate replicas for critical models.

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10) Design for graceful degradation

When capacity is tight, don’t fail hard.

Options:

• return lower-quality/faster model,
• reduce batch size,
• shorten context,
• shed noncritical traffic,
• fall back to CPU or smaller model,
• queue with timeout,
• serve cached/stale results if valid.

This is especially useful in multi-app environments.

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11) Observe and alert on the right metrics

Track per app, per model, and per GPU node:

• request rate,
• p50/p95/p99 latency,
• queue time,
• throughput,
• GPU utilization,
• GPU memory used,
• memory fragmentation,
• batch size distribution,
• error rate / OOMs,
• eviction / preemption events.

Set alerts for:

• rising queue latency,
• GPU memory near limit,
• sustained saturation,
• tail latency regression,
• noisy neighbor patterns.

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12) A practical architecture

A common setup looks like this:

1. Apps send inference requests to an internal API.
2. Gateway / router applies auth, rate limits, routing.
3. Serving layer (Triton/KServe/Ray Serve/vLLM) handles model execution.
4. Kubernetes / scheduler places replicas on GPU nodes.
5. MIG or node pools provide isolation.
6. Autoscaler scales replicas or nodes based on queue/latency metrics.
7. Monitoring feeds dashboards and alerts.

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13) If you want the simplest good solution

For many teams, a strong starting point is:

• Kubernetes
• NVIDIA GPU Operator
• Triton or vLLM
• per-app namespaces and quotas
• priority classes
• dynamic batching
• GPU + app-level metrics
• separate node pools for critical apps

Then add MIG if contention/isolation becomes a problem.

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14) Rules of thumb

• If latency matters most: prefer dedicated capacity or MIG slices
• If utilization matters most: prefer shared serving + batching
• If both matter: use hybrid with priority tiers
• If models are large and bursty: use autoscaling + warm pools
• If many apps compete: use quotas + admission control + observability

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If you want, I can also give you:

1. a Kubernetes-based reference architecture,
2. a capacity planning spreadsheet/template, or
3. a decision tree for choosing MIG vs shared GPUs vs dedicated GPUs.