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Estimate AI serving capacity by measuring the exact model and serving configuration under your real prompt, output, concurrency, and latency requirements—not by counting GPUs or relying on a model’s advertised tokens per second. Use those benchmarks to find the sustainable request and token rates per replica, calculate how many replicas peak demand requires, and then reserve capacity for bursts, failures, and maintenance. When preferred accelerators are scarce, compare the feasible alternatives by the work they can serve within their service-level objectives (SLOs), as well as by procurement time and recovery risk.
What does AI serving capacity mean?
Serving capacity is the workload an inference deployment can sustain while meeting its required latency and queue targets. It is not simply the number of accelerators in a cluster, their peak theoretical performance, or their utilization percentage.
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For capacity planning, describe demand in three complementary units: requests per second, input tokens per second, and generated output tokens per second. Requests per second indicates how frequently users submit work. Input-token rate captures prompt-processing demand, while output-token rate captures generation demand. Prompt length, output length, concurrency, and batching affect how those units translate into accelerator work, so request rate by itself cannot tell you how much hardware you need.
The practical result is an SLO-compliant operating point for a particular combination of model, serving engine, accelerator, and settings. Change any of those—including context length or quantization—and the operating point may change too. Capacity estimates are therefore configuration-specific, not permanent properties of a model.
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Why are requests per second not enough?
Two workloads with the same request rate can impose very different loads if one has longer prompts or generated answers. Prompt processing and token generation also affect different parts of the serving experience, so a single aggregate throughput number can conceal a latency problem.
Track input and output token rates alongside requests per second, and replay the observed distributions of prompt and output lengths. Include representative low, normal, peak, and burst concurrency. A benchmark using short prompts and short generations may look healthy yet fail on the longer requests users actually submit.
Latency matters as much as throughput. First-token latency (TTFT) measures how long a user waits for the first generated token; inter-token latency, often reported as time per output token (TPOT), describes the pace of subsequent generation. End-to-end latency captures the entire request. The acceptable values and percentiles depend on your service’s SLO; a workload is not sustainable merely because its average throughput is high.
What should a capacity benchmark measure?
Measure the load the system serves and the symptoms that reveal saturation. GPU utilization is useful context, but it cannot establish how much more work the deployment can safely handle or whether users are receiving timely responses.
Capture workload, latency, and queue signals
- Work served: requests per second, input-token throughput, and generation-token throughput.
- User experience: TTFT, inter-token latency or TPOT, and end-to-end latency, with the percentiles your SLO requires.
- Backlog: queue depth, plus the numbers of running and waiting requests.
- Memory pressure: KV-cache usage and preemptions, which can indicate that the serving engine is running into capacity limits.
- Test conditions: model version, quantization, accelerator type, serving-engine configuration, prompt and output distributions, concurrency, and test-window duration.
vLLM’s production metrics include TTFT, inter-token latency, TPOT, end-to-end latency, prompt and generation tokens, running and waiting requests, and KV-cache usage. NVIDIA AIPerf defines output-token throughput, input-token throughput, requests per second, and first-response latency as server metrics. Its documentation identifies growing queues, KV-cache usage above 0.9, and preemptions as capacity-limit indicators. Treat the above-0.9 figure as a documented warning signal, not a universal threshold that replaces your own latency and queue SLOs.
Define sustainability by the test window, not a brief peak
Mark a rate as sustainable only when the queue remains stable and every required latency percentile meets its SLO for the full benchmark window. A short burst of high throughput is not enough if requests continue accumulating in the queue or latency degrades after the initial ramp-up.
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How do I estimate how many replicas or GPUs I need?
First measure sustainable per-replica rates at the required SLO. Then calculate the replica count required by requests, input tokens, and output tokens separately; use the largest result because any one of those dimensions can become the bottleneck.
Let:
- R = peak arrival rate in requests per second.
- I = peak input-token rate in tokens per second.
- O = peak output-token rate in tokens per second.
- r, i, o = benchmarked sustainable requests, input tokens, and output tokens per second for one replica, respectively.
Calculate candidate replica counts as follows:
- Requests:
ceil(R / r) - Input tokens:
ceil(I / i) - Output tokens:
ceil(O / o)
Use the largest of the three counts, then multiply it by a headroom factor that you choose for bursts, failures, and maintenance. This is a planning formula derived from measured throughput and saturation behavior; it is not a vendor guarantee. The counts are replica counts, not automatically GPU counts: translate replicas into accelerators only after deciding how many GPUs each replica uses.
Worksheet for each candidate configuration
- Set the demand envelope. Record peak R, I, and O, plus the observed prompt-length and output-length distributions and concurrency levels.
- Specify the configuration. Record model version, quantization, maximum context length, tensor-parallel size, pipeline-parallel size, GPU-memory-utilization setting, and maximum batched tokens.
- Replay the workload. Test representative low, normal, peak, and burst concurrency using the observed input and output distributions.
- Measure the SLO-compliant operating point. Capture work served, latency percentiles, queue behavior, running and waiting requests, KV-cache use, and preemptions over the full test window.
- Compute replicas. Use the three ceiling calculations and select the largest candidate count before applying explicit headroom.
- Repeat after configuration changes. Rebenchmark after changing quantization, batching, parallelism, or context length; each can shift the sustainable operating point.
How much headroom should I reserve?
There is no universal percentage that is valid for every model or service. Choose a headroom factor from the failure and demand scenarios your operation must survive, then document what that factor covers. At minimum, consider bursts above the stated peak, a failed replica or node, and capacity removed for maintenance or software rollout. If replacement accelerators have uncertain delivery, include the time required to restore the lost capacity, not just the instantaneous failure.
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Apply the factor after selecting the largest request-, input-token-, or output-token-based replica count. Keep the reserved capacity in the same tested configuration; a nominal standby made from a different accelerator or quantization level is not equivalent until it has passed the same SLO test. Revisit the factor when traffic distributions, availability requirements, or failure domains change.
How do I check whether context length and concurrency fit in GPU memory?
Before comparing throughput, confirm that model weights, activations, and the KV cache fit in usable accelerator memory. A configuration that cannot fit the model and required context is not a viable capacity option, regardless of its theoretical speed.
vLLM’s startup output reports the total number of tokens that can be stored in the GPU KV cache at once. It also estimates maximum concurrency for a specified tokens-per-request value. Compare those figures with the workload’s context and concurrency requirements; they provide a direct fit check rather than an inference from utilization alone. The vLLM documentation describes the “GPU KV cache size” line as the total number of tokens that can be stored in the GPU KV cache at once.
The concurrency estimate is tied to the tokens-per-request assumption. A request with a longer prompt or a larger generation consumes more KV-cache capacity than a shorter request, so evaluate several representative context lengths rather than treating one maximum-concurrency number as universal. Check both the maximum context permitted by the service and the distribution of active requests. A deployment can fit a single maximum-length request yet fail when many ordinary requests are resident simultaneously.
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If available KV-cache tokens or estimated concurrency fall below what the workload requires, possible responses include adding GPUs or nodes, reducing maximum context length, changing quantization, or altering parallelism. Each choice has a trade-off: reducing context changes what requests can be served, while changing quantization or parallelism may affect quality, latency, or efficiency. Measure the changed configuration rather than assuming the original benchmark still applies.
When should I use tensor parallelism or pipeline parallelism?
Tensor parallelism divides model work across GPUs within a node and is normally the within-node choice. Pipeline parallelism divides layers across nodes and can accommodate uneven GPU splits, which may help when the available hardware does not form the desired equal-sized configuration.
| Approach | How it divides work | When it fits the documented use |
|---|---|---|
| Tensor parallelism | Divides work across GPUs within a node. | Normally the choice for distributing a model across GPUs in one node. |
| Pipeline parallelism | Divides model layers across nodes. | Can handle uneven GPU splits and spans nodes. |
Parallelism changes the shape of a replica and the resources it needs; it does not remove the need to measure throughput and latency. Keep tensor-parallel size and pipeline-parallel size in the benchmark record, and include networking and node topology in the feasibility review when a configuration spans nodes. A configuration that fits memory but depends on an unavailable node layout is not an actionable capacity plan.
How should I compare accelerators when preferred hardware is unavailable?
Start with the required workload and SLO, then shortlist all accelerators that can fit the model and context with acceptable parallelism. Compare only configurations that are operationally feasible; a nominally fast accelerator that cannot be acquired, provisioned, or recovered in time may be a poor capacity choice.
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- Screen for fit. Check that weights, activations, and KV cache fit the usable memory, and that the context and concurrency requirements can be met.
- Benchmark a normalized matrix. Use the same model, workload distributions, test window, and SLO criteria across candidates where possible. Compute SLO-compliant requests per second and input and output tokens per second per accelerator.
- Compare user-facing performance. Include TTFT and inter-token latency percentiles, along with KV-cache capacity, concurrency, and parallelism shape—not just a peak throughput figure.
- Score operational constraints. Account for procurement lead time, quota risk, network topology, power and cooling requirements, failure domains, and migration effort.
- Evaluate substitutions deliberately. Consider quantization, batching, prefix caching, or a model change only after measuring their effects on quality and latency.
- Plan recovery. Reserve headroom and identify a fallback configuration; when supply is constrained, recovery time and substitution value are part of capacity planning.
For cost, compare the total resources assigned to a tested replica and the SLO-compliant work it delivers. Include reserved headroom, standby capacity, networking, power and cooling, and the operational effort of maintaining more complex parallelism. A lower purchase or rental rate does not establish a lower cost per served token if the alternative needs more replicas, has lower usable KV-cache capacity, or misses the SLO. Report cost per served token only after accounting for the benchmarked operating point and reserved headroom; no universal price or performance figure applies to every accelerator.
When preferred hardware such as H100s is unavailable, do not substitute by name alone. Test the available accelerator with the same model, context, quantization, batching, and SLO. A lower-memory option may require more GPUs, a shorter context, or a different parallelism shape; a different quantization may improve fit but alter output quality. The fallback is acceptable only when those effects are measured and the procurement and failure-domain requirements remain workable.
Why is GPU utilization a weak autoscaling signal on its own?
GPU utilization indicates whether a GPU is busy, not the degree to which it is productively serving additional demand. An inference server can stay near 100% utilization across a wide range of incoming requests, so utilization alone can fail to distinguish very different capacity conditions.
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Amazon Web Services’ EKS autoscaling documentation makes this distinction explicitly: GPU utilization (DCGM_FI_DEV_GPU_UTIL) shows whether the GPU is busy, not how busy it is. AWS also notes that because an inference server such as vLLM can remain near 100% across a wide range of incoming requests, utilization is an unreliable autoscaling signal.
Use leading indicators tied to user experience and backlog—especially queue depth, TTFT, and request latency—to detect when added capacity is needed. Interpret GPU utilization alongside those signals and the workload being served. That helps avoid treating a persistently busy GPU as proof that the service is either healthy or already beyond its useful capacity.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What mistakes make a capacity estimate unreliable?
- Counting requests without token rates. Different prompt and output lengths can make the same request rate represent materially different work.
- Benchmarking a different workload. A test that omits representative distributions or peak and burst concurrency does not establish capacity for those conditions.
- Calling a short-lived peak sustainable. Queues and latency must remain within the required bounds for the full test window.
- Optimizing only for utilization or throughput. Neither establishes that latency percentiles and queue behavior meet the SLO.
- Ignoring KV-cache fit. Model weights, activations, context, and cache requirements must fit before a throughput comparison is meaningful.
- Changing settings without retesting. Quantization, batching, context length, and parallelism alter the configuration and can change both performance and quality.
- Leaving no recovery capacity. A plan that works only under normal demand can fail during bursts, failures, or maintenance—especially when replacement accelerators have long lead times.
- Comparing unlike replicas. Throughput per accelerator is misleading when one candidate uses a different tensor- or pipeline-parallel shape, context limit, or batching policy. Normalize the workload and report the complete configuration.
- Confusing maximum context with typical capacity. A maximum-length setting may consume cache even when most requests are shorter; validate the active-request mix and the concurrency estimate.
What should I keep in the capacity record?
Maintain a versioned record for every benchmarked option. Include the model and serving-engine versions, quantization, maximum context length, tensor- and pipeline-parallel sizes, GPU-memory-utilization setting, maximum batched tokens, accelerator and node topology, workload distributions, concurrency levels, test duration, and all measured throughput, latency, queue, KV-cache, and preemption metrics. Record which percentiles define the SLO and the exact rate marked sustainable.
This record makes a future comparison possible when an accelerator becomes scarce, a model changes, or traffic shifts. It also prevents a throughput result from being reused after a setting changed. Re-run the matrix whenever those assumptions change, and keep a tested fallback alongside the primary configuration.
Frequently Asked Questions
Can GPU utilization tell me whether an inference server is overloaded?
Not by itself. A busy GPU can still be serving within SLOs, while a growing queue or deteriorating latency can reveal a problem that utilization does not distinguish; interpret it with queue and response-time signals.
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What is the difference between TTFT and TPOT?
TTFT is the wait from request submission until the first generated token arrives. TPOT describes time per output token after generation begins, so the two metrics describe different parts of the user’s wait.
Does a vLLM maximum-concurrency estimate guarantee that my traffic will fit?
No. It is an estimate tied to a specified tokens-per-request value and KV-cache capacity, not a guarantee of SLO-compliant serving under every prompt mix or load pattern. Validate it against the workload’s context lengths, concurrency, latency, and queue behavior.
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Should I increase maximum batched tokens to get more throughput?
Only if the resulting configuration still meets your output-token latency target. Larger batches can improve throughput while worsening output-token latency, so benchmark the setting against both requirements.
How many GPUs do I need for a model?
There is no model-only answer. Measure sustainable requests, input tokens, and output tokens per second for the exact model, engine, accelerator, context, batching, and SLO. Convert the largest of the three replica calculations into GPUs using the selected parallelism shape, then add headroom.
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Benchmark a complete replica under representative prompt and output lengths, concurrency, and batching. Divide its SLO-compliant input- or generation-token throughput by the number of accelerators in that replica, and report the configuration because the result is not portable across settings.
What is the maximum concurrent context length I can serve?
Check the vLLM GPU KV-cache token capacity and its maximum-concurrency estimate for the tokens-per-request value you specify. Compare those values with the active-request mix and latency SLO; maximum context and maximum concurrency trade off against each other.
What can I use when H100s or another preferred accelerator is unavailable?
Shortlist every available accelerator that fits the weights, activations, context, and KV cache, then run the same normalized benchmark. Compare SLO-compliant work per accelerator, memory and parallelism shape, procurement and quota risk, failure domains, power and networking, migration effort, and upgrade path rather than choosing by product name.
How much headroom should I reserve?
Choose a documented factor based on the bursts, replica or node failures, and maintenance events the service must survive. Apply it after the largest request-, input-token-, or output-token-based replica count, and ensure the reserved configuration has also passed the SLO benchmark.
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The Bottom Line
Capacity is the highest measured request and token load that keeps queues and latency within SLOs for the full test window. Benchmark the real workload, verify KV-cache and memory fit, size replicas from all three demand rates, and reserve tested capacity for bursts, failures, and maintenance. Under accelerator scarcity, the best option is the obtainable configuration that delivers compliant work with acceptable recovery and migration risk—not necessarily the accelerator with the highest theoretical throughput.
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