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Estimate AI serving capacity from the workload your users generate, not from a GPU count or a utilization target. Measure requests per second together with input and output tokens per second, using the exact model, quantization, serving engine, context limit and accelerator you may deploy. The sustainable rate is the highest load that keeps time to first token, inter-token latency, end-to-end latency and queue depth within your service-level objectives (SLOs) for the entire test window. Convert that measured per-replica rate into replicas, then add explicit capacity for bursts, failures and maintenance. When preferred accelerators are unavailable, choose among configurations that fit the model and context, meet the SLO, and offer acceptable procurement risk and recovery options.
What does AI serving capacity actually measure?
Serving capacity is the rate at which an inference deployment can accept and complete requests while meeting its latency and reliability SLOs. It has at least three dimensions:
- Request rate: completed requests per second (requests/s).
- Prompt processing: input tokens per second, which determines prefill work and often dominates time to first token (TTFT) for long prompts.
- Generation: output tokens per second, which determines decode work and inter-token latency or time per output token (TPOT).
A request-rate figure without token distributions is incomplete. Ten short prompts can require less work than one long context, while a response-heavy workload can keep decode kernels busy long after prefill has finished. Record the full distributions of prompt length, requested output length, and concurrency, not just their averages. Include rejected, timed-out and cancelled requests when checking the user-visible result; dropping difficult requests can make a saturated system appear healthy.
Capacity is also a memory constraint. Model weights, activations and the key-value (KV) cache must fit in usable accelerator memory before throughput comparisons mean anything. The KV cache stores attention keys and values for active sequences; its token capacity limits how many contexts can be resident at once.
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How do I turn traffic into a sizing model?
Start with the peak workload envelope and a benchmarked sustainable rate for one replica. Let R be peak requests/s, I peak input tokens/s and O peak output tokens/s. Let r, i and o be the corresponding rates for one replica at the required SLO.
Calculate three candidate replica counts:
ceil(R / r)
ceil(I / i)
ceil(O / o)Use the largest result, because the binding resource can change with traffic mix. Multiply that count by a documented headroom factor for bursts, node loss and maintenance. The formula is a planning method derived from measured saturation behavior, not a vendor guarantee; rerun it whenever the model, context limit, batching policy or hardware changes.
Describe the workload envelope
- Measure peak and normal arrival rates, plus burst duration and frequency.
- Capture prompt and completion-length percentiles, not only means.
- Separate interactive, streaming and batch traffic if they have different SLOs.
- Record maximum simultaneous sessions and whether requests share prefixes.
- State the region, model revision, tokenizer and any safety or tool-calling overhead that adds tokens.
For planning, keep a worksheet with one row per traffic class. A mixed deployment can then size replicas for each class or reserve a fraction of capacity for latency-sensitive requests instead of allowing batch work to consume every slot.
Which benchmark proves that a replica is sustainable?
Benchmark the exact model and serving stack you intend to run. A synthetic test using a different prompt length, quantization or engine can produce a number that does not transfer to production.
- Freeze the configuration. Record model version, tokenizer, quantization, maximum context length, tensor-parallel size, pipeline-parallel size, GPU-memory-utilization setting and maximum batched tokens.
- Replay representative traffic. Use the observed prompt-length and output-length distributions. Include low, normal, peak and burst concurrency, and preserve the ratio of streaming to non-streaming requests.
- Ramp gradually. Increase arrival rate until a latency or queue SLO fails. Hold each level long enough for caches, batching and memory usage to reach a steady state.
- Collect server and user metrics. Record requests/s, input-token throughput, generation-token throughput, TTFT, inter-token latency or TPOT, end-to-end latency, queue depth, running and waiting requests, KV-cache usage and preemptions.
- Mark the sustainable point. Use the highest load at which the queue remains stable and every required latency percentile stays within its SLO for the full test window. A brief high rate followed by an ever-growing queue is overload, not capacity.
- Repeat after every material change. Quantization, batching, parallelism, context length and memory settings all move the operating point.
GPU utilization belongs in the diagnostic record, but it cannot define capacity. An inference server can remain near 100 percent utilization over a wide range of incoming loads. Utilization indicates that a device is busy, not how much useful work it is completing or whether requests are waiting too long.
Metrics that expose the bottleneck
| Metric | What it tells you | Typical capacity warning |
|---|---|---|
| Requests/s | Completed request rate at the tested mix | Stops rising while arrivals continue |
| Input-token throughput | Prefill work handled per second | TTFT rises as long prompts arrive |
| Generation-token throughput | Decode work handled per second | TPOT worsens with more active sequences |
| TTFT | Time until the first generated token | Queueing or prefill contention breaches the interactive SLO |
| Inter-token latency / TPOT | Spacing between generated tokens | Streaming output becomes visibly slower |
| End-to-end latency | Total request completion time | Long-tail percentile exceeds its SLO |
| Queue depth and waiting requests | Whether demand is accumulating | Persistent positive slope during a supposedly steady test |
| KV-cache usage | How much context memory is occupied | NVIDIA AIPerf documentation flags usage above 0.9 as a capacity-limit indicator |
| Preemptions | Requests displaced because memory or scheduling is constrained | Retries, pauses or cancellations appear |
Use percentile targets that match your product contract, such as a p95 TTFT and p99 end-to-end latency. The exact percentile and time limit are decisions for your service; the benchmark must test them explicitly rather than reporting only an average.
How do I check context length and concurrency with vLLM?
vLLM reports a GPU KV cache size, the total number of tokens that can be stored in GPU KV cache at once, and an estimated maximum concurrency for a specified tokens-per-request value. Treat these as fit checks, not as throughput promises. If the reported token capacity or concurrency is below your requirement, the deployment cannot hold the intended workload without reducing context, adding memory or changing the parallel layout.
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Use the KV-cache output as a gate
- Set the intended maximum model length and GPU-memory-utilization value.
- Start the server with the planned tensor- and pipeline-parallel sizes and maximum batched tokens.
- Read the startup report for total KV-cache tokens and the estimated maximum concurrency at your chosen tokens-per-request value.
- Compare that concurrency with the number of simultaneous sessions and the longest contexts in your workload.
- If it fails, reduce context length only when the product can tolerate truncation; otherwise add GPUs or nodes, change quantization, or alter parallelism and rerun the benchmark.
Concurrency estimates assume a token budget. A workload with 4,000-token prompts and 1,000-token generations has a different memory requirement from one with 500-token prompts and 200-token generations, even at the same request rate. Keep separate checks for the common case and the worst supported context.
What are tensor and pipeline parallelism, and when should I use each?
Tensor parallelism (TP) splits model operations across GPUs, normally within one node with fast peer-to-peer links. It can reduce per-GPU memory and increase the size of the model that fits, but communication overhead grows as the TP group expands.
Pipeline parallelism (PP) divides model layers into stages, usually across nodes. It is useful when the available GPUs do not form an even tensor-parallel group and can accommodate uneven GPU splits. Pipeline bubbles, cross-node network latency and stage imbalance can reduce throughput, so measure them rather than assuming that adding a stage scales linearly.
| Decision question | Tensor parallelism | Pipeline parallelism |
|---|---|---|
| Where is work divided? | Within individual matrix and attention operations | Across groups of model layers |
| Typical placement | One node with high-bandwidth GPU links | Multiple nodes connected by the serving network |
| Best fit | Even GPU counts and strong intra-node interconnect | Uneven GPU availability or models that need more than one node |
| Main risk | Collective-communication overhead as the group grows | Pipeline bubbles, stage imbalance and network latency |
Choose the smallest parallel configuration that fits weights, activations and KV cache while meeting the SLO. Then test a larger or differently shaped configuration; a lower device count can sometimes win on latency or cost if it avoids cross-node communication.
How do batching and context limits change capacity?
Continuous batching keeps a scheduler supplied with work as sequences enter and leave decode. Increasing the maximum batched-token budget can improve aggregate throughput, especially for prefill-heavy traffic, but it can also delay decode work and worsen output-token latency. Select the setting against TTFT and TPOT SLOs, not against throughput alone.
Maximum context length reserves memory even when most requests are shorter, and long contexts consume KV-cache tokens that could have supported other sessions. If your product has a hard context ceiling, benchmark that ceiling and a realistic distribution separately. If truncation, summarization or retrieval can reduce prompt size, measure the quality impact and the latency benefit together.
Changes that require a fresh test
- Switching precision or quantization method.
- Changing maximum model length or per-request token limits.
- Increasing maximum batched tokens or concurrent sequences.
- Enabling prefix caching, speculative decoding or another scheduler feature.
- Moving from one node to several nodes or changing the TP/PP shape.
Prefix caching can lower repeated-prefill work when many requests share a prefix, but it consumes cache memory and offers little benefit for unrelated prompts. Treat it as a workload-specific optimization and verify both cache-hit rate and latency tails.
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How much headroom should I reserve?
There is no universal percentage that is safe for every service. Derive headroom from the largest burst, the failure you must survive and the maintenance policy you promise. If one replica is lost, the remaining replicas must still carry the declared traffic without violating the SLO; if maintenance removes a whole node or failure domain, model that event explicitly.
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Build headroom from scenarios
- Burst headroom: compare the sustained arrival rate with the highest short-window rate you must absorb before autoscaling reacts.
- Failure headroom: remove the largest planned failure unit—GPU, node, rack or zone—and recalculate the worksheet.
- Maintenance headroom: model rolling upgrades, driver changes and firmware work when capacity is intentionally reduced.
- Growth headroom: include expected traffic growth only for the planning horizon you can defend; do not hide speculative demand in a permanent overbuild.
Write the assumptions beside the replica count. A mathematically sufficient fleet with no recovery margin may be cheaper on paper but can leave users without service while replacement hardware is procured.
What should autoscaling watch?
Scale on leading indicators that show users are about to experience delay: queue depth, TTFT, end-to-end latency and the number of waiting requests. Pair them with request and token rates so the controller can distinguish a prompt-length surge from a connection spike. GPU utilization alone is unreliable because a server can sit near 100 percent across very different capacity conditions.
Use hysteresis and stabilization windows to prevent oscillation. Scale out when queue depth or latency breaches a threshold for a sustained interval; scale in only after the queue is empty and latency has remained comfortably below target. Streaming requests need a drain policy so a replica is not terminated while it still owns long-lived generations. Keep a minimum fleet that covers failure headroom, and test the scale-up time against the burst duration you intend to absorb.
How should I choose hardware when preferred accelerators are unavailable?
Start with the SLO and workload envelope, then shortlist every accelerator that can fit the model and context with an acceptable parallel layout. Do not substitute a device solely because its peak arithmetic rating is higher; serving performance depends on memory capacity and bandwidth, interconnects, kernel support, quantization behavior and the actual engine.
- Define the contract. Specify request, input-token and output-token peaks; latency percentiles; streaming behavior; maximum context; availability target; and the largest failure you must survive.
- Filter for fit. Eliminate configurations that cannot hold weights, activations and the required KV-cache tokens, or that require an impractical TP/PP shape.
- Run a normalized matrix. Keep model revision, prompts, output limits, concurrency, batching and SLOs constant while measuring each candidate.
- Normalize results. Compare SLO-compliant requests/s and input/output tokens/s per accelerator, not peak throughput at a relaxed latency target.
- Score operational constraints. Include procurement lead time, quota risk, power and cooling, network topology, failure domains, support maturity and migration effort.
- Price the resilient design. Calculate cost per served token after headroom and the extra replicas required for failure tolerance. Include the cost of keeping a fallback configuration warm or deployable.
- Rehearse substitution. Validate images, drivers, weights, quantization and monitoring on the alternate accelerator before the primary supply is interrupted.
| Comparison axis | Measurement to record | Why it matters |
|---|---|---|
| SLO-compliant throughput | Requests/s and input/output tokens/s at the required latency percentiles | Shows useful capacity rather than a peak number |
| Latency profile | TTFT, inter-token latency or TPOT, and end-to-end percentiles | Separates interactive experience from aggregate work |
| Memory fit | Weights, activations, usable KV-cache tokens and concurrency | Prevents out-of-memory failures and hidden queueing |
| Parallelism shape | TP and PP sizes, node count and interconnect requirements | Reveals communication and failure-domain trade-offs |
| Quality impact | Accuracy or task-quality change from quantization or model variants | A faster configuration is not acceptable if output quality fails |
| Supply and operations | Lead time, quota risk, power, cooling, networking and support | Determines whether capacity can actually be delivered and recovered |
Scarcity changes the value of a fallback. A slightly slower accelerator that is available in days may protect service better than a faster part with an uncertain delivery date. Keep the benchmark artifacts and deployment manifests portable so a substitution is an operational procedure, not a new science project.
Can quantization, model changes or caching replace more accelerators?
Often, but only after measurement. Quantization can reduce weight and KV-memory pressure and may increase throughput, yet quality loss, kernel support or dequantization overhead can erase the benefit. A smaller model may improve latency and supply flexibility while changing answer quality, tool use or safety behavior. Prefix caching can cut repeated prefill but increases memory pressure. Treat each as a controlled experiment with the same traffic replay and SLO checks.
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A safe optimization sequence
- Baseline the full-precision or currently approved model and record quality and latency.
- Change one variable—quantization, model, context policy, batching or caching.
- Run the same prompt and completion distributions at the same concurrency levels.
- Compare quality gates, TTFT, TPOT, queue depth, KV-cache use, preemptions and sustainable token throughput.
- Keep the change only if it meets both the product-quality and serving SLOs, then repeat capacity sizing with the new measured rates.
What does a worked sizing worksheet look like?
Suppose an application records a peak of R requests/s, I input tokens/s and O output tokens/s. A benchmark of one candidate replica at the required SLO produces r, i and o. Enter the three quotients in a worksheet, round each up, and select the largest. Next, remove one replica (or the largest failure unit) and verify that the surviving fleet still covers the declared peak. Finally, apply the burst and maintenance assumptions to determine the provisioned count.
For example, if output-token demand is the largest quotient, adding replicas for request rate alone will not protect streaming latency. If input-token demand dominates only during short bursts, a queue-aware autoscaler and a tested burst buffer may be more efficient than permanently provisioning for that instantaneous maximum. The decision comes from the measured queue and latency curves, not from a fixed industry ratio.
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Using average tokens
Averages hide long prompts and long generations that consume disproportionate prefill, decode and KV-cache resources. Replay percentile lengths and include the longest supported context as a separate test.
Declaring capacity at GPU saturation
A busy device can still have a growing queue or unacceptable TTFT. Require stable queues and passing latency percentiles for the entire hold period.
Ignoring memory fragmentation and preemptions
Two configurations with similar utilization can differ in usable KV-cache tokens. Watch cache usage, running and waiting requests, allocation failures and preemptions.
Benchmarking a different software stack
Changing the engine, kernel build, driver, quantization or tokenizer can alter scheduling and memory behavior. Freeze and record the complete serving image.
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Scaling in on utilization alone
Utilization may remain high while latency is acceptable or while a queue is exploding. Combine queue, latency and token-rate signals with a drain-aware scale-in policy.
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Forgetting recovery time
When accelerators are scarce, replacement lead time and quota risk are part of capacity. Maintain a tested fallback and document how weights and configuration move to it.
What should be in the final capacity plan?
A reviewable plan contains the workload envelope, benchmark artifacts, formulas, assumptions and operational controls. Include:
- Model and tokenizer versions, quantization and quality gates.
- Accelerator type, memory, node topology, TP/PP layout and interconnect.
- Maximum context, maximum batched tokens, GPU-memory-utilization setting and KV-cache capacity.
- Traffic distributions, concurrency levels and streaming behavior used in tests.
- Sustainable requests/s, input-token and output-token rates with TTFT, TPOT and end-to-end percentiles.
- Replica count before and after burst, failure and maintenance headroom.
- Autoscaling signals, thresholds, stabilization windows and drain behavior.
- Primary and fallback configurations, procurement assumptions, power and cooling limits, and migration steps.
Revisit the plan whenever traffic mix, model behavior, context policy, serving software or accelerator availability changes. Capacity is a measured operating point, not a permanent property of a GPU model.
Frequently Asked Questions
How often should an AI serving capacity benchmark be rerun?
Rerun after any model, tokenizer, quantization, driver, serving-engine, batching or context-limit change. Also schedule periodic replays when prompt and completion distributions drift, because a stable request rate can conceal a rising token load.
What is the difference between TTFT and TPOT for streaming users?
TTFT is the delay before the first generated token and is strongly affected by queueing and prompt prefill. TPOT, also called inter-token latency, describes the spacing of subsequent tokens, so it reflects decode contention after generation has begun.
How can I test a fallback accelerator without risking production?
Use the same container, model artifacts, tokenizer, traffic replay and SLO gates in an isolated environment. Verify numerical output quality, observability, autoscaling and failure recovery before placing live traffic on the alternate pool.
Why can a longer context limit reduce requests per second even when prompts are usually short?
A larger limit increases the memory reservation and lowers the KV-cache capacity available for concurrent sequences. It can also force a wider parallel configuration, so benchmark the configured maximum alongside the normal prompt distribution.
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