A capacity-planning guide for multimodal AI workloads where text, voice, image and video generation have different latency, memory and GPU utilization profiles.

One GPU pool can hide four different businesses

Text generation, speech, image generation and video generation consume compute in very different patterns. Combining them into one utilization number makes capacity planning misleading. Each modality needs its own demand model. Model concurrency, not just daily volume.

A million requests spread evenly are easier than the same volume concentrated after a product event. Measure concurrent sessions, request duration and burst patterns. Voice and interactive video are especially sensitive because users notice queueing immediately.

Separate queues by service objective

Interactive text and voice should prioritize latency. Image and video jobs can often tolerate queueing. Separate scheduling policies prevent a burst of video generation from degrading conversational response time. Account for memory pressure.

Model size, KV cache, image resolution and video pipelines can all change GPU memory requirements. Capacity plans should use peak memory per workload, not only compute throughput.

Use representative load tests

Synthetic tests should reproduce prompt lengths, output lengths and modality mix from production. MidyTech’s coverage of AI inference autoscaling explains why GPU capacity cannot be scaled exactly like web servers. Plan graceful degradation.

When demand exceeds capacity, products can lower video resolution, delay non-urgent jobs, route to alternate models or temporarily cap expensive generations. Define these policies before an incident.

Connect capacity to economics

Reserved GPUs can reduce unit cost at high utilization but create fixed spend. Cloud APIs and burst capacity add flexibility. The right mix depends on predictable base load plus peak demand, measured as cost per successful user outcome. Define the service objective before optimizing infrastructure.

AI infrastructure decisions depend on the traffic class. Interactive inference cares about time to first token and tail latency, while batch workloads may care more about throughput and cost. Mixing those objectives without explicit priority rules causes one workload to degrade another in unpredictable ways. Start with service-level targets for each class, then design admission, scheduling and capacity around them. The system should know which work can wait, which can be rejected and which deserves reserved resources during overload.

Model bottlenecks across the whole request path

GPU utilization alone is not enough. Tokenization, network transfer, queue time, model loading, KV-cache pressure and downstream post-processing can all dominate latency. Instrument each stage so capacity decisions reflect the true critical path. Tail behavior matters especially in interactive systems. A low average can hide a small percentage of requests that wait long enough to damage the user experience.

Design overload behavior deliberately. When demand exceeds capacity, the system should degrade in a controlled way: route to a smaller model, reduce optional work, queue lower-priority jobs, or reject requests before the queue becomes unrecoverable. Allowing every request into the system can turn a short spike into prolonged latency for everyone.

Admission control, per-tenant fairness and bounded queues are therefore product reliability features, not only infrastructure details.

Measure economics together with latency

Track cost per successful request or engaged user outcome, not only GPU hours. Warm capacity can reduce cold starts but increase idle spend; aggressive batching can improve throughput while hurting interactivity. The best configuration depends on both user-facing SLOs and the business value of each traffic class. Separate interactive and elastic capacity.

Real-time traffic benefits from warm, predictable capacity, while background jobs can often use preemptible or delayed resources. Keeping at least some scheduling separation prevents batch work from consuming the headroom needed for user-facing requests. The boundary does not have to be fixed. Capacity can move between pools based on demand, but the transfer policy should preserve latency targets before maximizing utilization.

Use queue signals before GPU saturation becomes visible

Queue age, pending tokens and admission delay often reveal overload earlier than raw GPU utilization. A device can appear busy but healthy, or moderately utilized while a long queue forms because request shapes are mismatched. Control loops should therefore use several signals and apply hysteresis so autoscaling or rejection policies do not oscillate during bursty traffic.

Define service objectives before optimizing utilization

Interactive inference and batch workloads have different goals. User-facing traffic cares about time to first token and tail latency, while offline work can wait for cheaper capacity. Explicit traffic classes prevent one workload from consuming headroom needed by another.

Schedulers should know which requests may queue, which can route to a fallback model and which should be rejected early during overload.

Measure the full request path

GPU execution is only one component of latency. Admission delay, queue time, model loading, KV-cache pressure, transfer and downstream processing can dominate the user experience.

End-to-end traces and tail-percentile metrics show where capacity work will actually improve the product.