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Estimate AI serving capacity from a measured workload, not from a GPU count. Start with peak requests per second plus input- and output-token rates, then benchmark the exact model, quantization, serving engine, context length, batching and parallelism on every accelerator you can actually obtain. The sustainable point is the highest load that keeps the queue stable and all latency SLO percentiles within target for the whole test window. Convert that measured per-replica capacity into replicas, add explicit headroom for bursts, failures and maintenance, and compare substitutes by SLO-compliant throughput, memory fit, procurement risk and recovery options—not by theoretical FLOPS or utilization alone.
What does “serving capacity” mean for an AI system?
Serving capacity is the request and token rate a deployment can sustain while meeting its service-level objectives (SLOs). It is not the instant rate at which a GPU can produce tokens in a short test. A valid capacity number includes the workload mix, concurrency, queue behavior, first-token delay, generation delay, end-to-end latency and available KV-cache space.
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Why is requests per second alone insufficient?
Two requests per second workloads can impose very different load. Prompt processing (prefill) consumes compute in proportion to input tokens, while autoregressive generation (decode) repeatedly uses the model and KV cache for each output token. Prompt and completion lengths also determine memory pressure, batching efficiency and how long other requests wait.
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- Request rate: arrivals per second, including burst rate.
- Input-token rate: prompt tokens per second, with the distribution of prompt lengths.
- Output-token rate: generated tokens per second, with the distribution of completion lengths.
- Concurrency: active, queued and waiting requests at each load level.
- Context distribution: the combined prompt and generated-token lengths that must fit in KV cache.
Report all three rates—requests, input tokens and output tokens. A short-prompt, long-answer workload may be decode-bound; long documents with short answers may be prefill- or memory-bound. A single requests-per-second figure hides that distinction.
What workload information should you collect before sizing hardware?
Build an envelope from production telemetry or a representative trace before evaluating accelerators. Record the values below separately for normal, peak and burst periods.
| Input | What to record | Why it changes capacity |
|---|---|---|
| Arrival pattern | Average and peak requests/s, burst duration and inter-arrival distribution | Short bursts can overflow a queue even when the daily average is low. |
| Prompts | Input tokens/request, percentiles and prompt-sharing rate | Drives prefill work and determines whether prefix caching can help. |
| Completions | Output tokens/request, percentiles, stop behavior and streaming policy | Drives decode work and inter-token latency. |
| Concurrency | Simultaneous requests, queue depth and cancellation rate | Controls batching and KV-cache occupancy. |
| SLO | TTFT, inter-token latency or TPOT, end-to-end latency and percentile targets | A higher throughput number is unusable if it violates the user-visible limits. |
| Context policy | Maximum model length, truncation rules and expected long-context share | Sets per-request KV-cache demand and concurrency limits. |
Preserve the observed distributions rather than testing only one “typical” prompt. Include low, normal, peak and burst concurrency; otherwise a deployment can pass a benchmark while failing at the exact load that matters.
How do you benchmark a candidate accelerator correctly?
Benchmark the complete serving stack that will run in production. The model weights, quantization, driver and runtime, engine settings, networking and request scheduler all affect the result.
- Freeze the configuration. Record model and tokenizer version, quantization method, maximum context length, tensor-parallel size, pipeline-parallel size, GPU-memory-utilization setting and maximum batched tokens.
- Prepare a trace or generator. Replay the measured input- and output-length distributions, including long-context cases and realistic stop conditions. Keep sampling parameters and streaming behavior fixed across candidates.
- Warm the service. Load weights, compile kernels and fill any caches before measuring. Separate cold-start time from steady-state serving capacity.
- Ramp concurrency. Test low, normal, peak and burst levels. Hold each level long enough for queues and KV-cache occupancy to reach a stable pattern.
- Capture throughput. Measure requests/s, input-token throughput and generation-token throughput. A throughput increase that comes from an out-of-SLO queue is not sustainable capacity.
- Capture latency and queue metrics. Collect TTFT, inter-token latency (also reported as time per output token, or TPOT), end-to-end latency, queue depth, running requests and waiting requests.
- Capture memory behavior. Record GPU KV-cache usage, preemptions, out-of-memory events and any request evictions. Watch both aggregate values and high percentiles.
- Mark the sustainable point. The capacity result is the highest offered load for which the queue remains stable and every required latency percentile stays within its SLO for the entire test window.
Repeat the matrix after changing batching, quantization, context length or either form of parallelism. Each change creates a new operating point; results are not transferable by simple GPU-count scaling.
How do you convert measured throughput into a replica count?
Let peak arrival rate be R requests/s, input-token rate be I tokens/s and output-token rate be O tokens/s. Let a benchmarked replica sustain r, i and o at the required SLO. Calculate each constraint and use the largest:
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request_replicas = ceil(R / r)
input_token_replicas = ceil(I / i)
output_token_replicas = ceil(O / o)
base_replicas = max(request_replicas, input_token_replicas, output_token_replicas)
planned_replicas = ceil(base_replicas × headroom_factor)The headroom factor is a planning variable for bursts, failures and maintenance, not a vendor guarantee. Choose it from your burst profile and availability design, then validate the resulting queue and latency with a failover or degraded-capacity test.
| Worksheet field | Value to enter | Qualification |
|---|---|---|
R |
Peak requests/s | Use the peak window you promise to serve, not a daily average. |
I |
Peak input tokens/s | Derived from the observed prompt-length distribution. |
O |
Peak output tokens/s | Derived from generated-token counts, including streaming completions. |
r, i, o |
Sustainable per-replica rates | Measured on the exact configuration while all SLOs and queue conditions hold. |
| Headroom factor | Your burst, failure and maintenance allowance | State the assumptions; do not present it as a universal percentage. |
For heterogeneous fleets, apply the same method per replica class and sum only capacity that is genuinely interchangeable under your router, model and SLO. Keep a fallback class separate if it has different context, quality or latency behavior.
How can vLLM reveal context and concurrency limits?
vLLM exposes two useful fit checks during startup and operation. Its GPU KV cache size line reports the total number of tokens that can be stored in GPU KV cache at once. It also estimates maximum concurrency for a specified tokens-per-request value. Treat those as memory-fit checks, not as a complete throughput benchmark.
Compare the reported token capacity and estimated concurrency with your required context distribution. If the estimate is below the requirement, the practical remedies are to add GPUs or nodes, reduce the maximum context length, change quantization, or alter tensor- or pipeline-parallel settings. Re-run the workload benchmark after each remedy because memory relief can change latency and batching efficiency.
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When should you use tensor parallelism or pipeline parallelism?
Tensor parallelism splits each layer’s computation across GPUs and is normally the within-node choice when fast interconnects are available. Pipeline parallelism divides layers across stages, commonly spanning nodes, and can accommodate an uneven number of GPUs per stage. The choice changes communication, scheduling, memory placement and failure domains.
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| Dimension | Tensor parallelism | Pipeline parallelism |
|---|---|---|
| Partition | Work within layers | Different layers across stages |
| Typical placement | Inside one node | Across nodes or separate stage groups |
| GPU-count flexibility | Best when a node offers the required symmetric split | Can handle uneven GPU splits |
| What to validate | Intra-node bandwidth, synchronization and batch-size behavior | Stage balance, inter-node latency, pipeline bubbles and node-failure impact |
Record both parallel sizes in every benchmark result. A substitute accelerator may fit the weights only after increasing the parallel degree, and that change can reduce SLO-compliant throughput enough to require more replicas.
How should you compare accelerators when the preferred part is unavailable?
First eliminate configurations that cannot fit the model and context with an acceptable parallelism shape. For every remaining option, run a normalized benchmark and score the operational constraints that determine whether capacity can actually be delivered.
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| Comparison axis | Question to answer |
|---|---|
| SLO-compliant requests/s | How many requests remain within every latency percentile target? |
| Token throughput | What input- and output-tokens/s does each accelerator sustain? |
| Latency | What are TTFT and inter-token latency percentiles at the target load? |
| KV-cache and concurrency | How many usable KV-cache tokens and simultaneous requests fit? |
| Memory and parallelism | Do weights, activations and KV cache fit without an impractical split? |
| Quality impact | Does quantization or a smaller model change task quality or refusal behavior? |
| Supply and operations | What are procurement lead time, quota risk, power, cooling, networking and failure-domain requirements? |
| Recovery and migration | Can you fail over to it, and what code or kernel changes are needed to move later? |
| Economics | What is cost per served token after planned headroom, not cost at an overloaded peak? |
Scarcity makes substitution value part of capacity. A slightly slower accelerator that is available sooner, can be spread across independent failure domains and has a credible upgrade path may deliver more usable service than a faster part with an uncertain allocation.
How much headroom should you reserve?
There is no universal percentage that fits every workload. Derive headroom from three explicit cases: the largest measured burst, the capacity lost when a replica or node fails, and the capacity removed during maintenance or rolling upgrades. Your planned fleet must keep the remaining replicas inside the SLO-compliant region in each case.
- Bursts: use the burst duration and arrival distribution, not only the peak sample.
- Failures: model the loss of the largest failure domain you promise to tolerate.
- Maintenance: include drain time, cold-start time and any temporary reduction in parallelism.
- Model changes: reserve migration capacity if a new version needs more memory or a different parallel split.
If a burst is intentionally absorbed by a queue, define its maximum queue age and prove that TTFT and end-to-end SLOs still hold. Otherwise, provision enough replicas to serve that burst directly.
Why is GPU utilization a poor autoscaling signal?
GPU utilization shows that a device is busy, not how much useful capacity remains. An inference server can stay near 100% utilization across widely different incoming loads, so the same utilization value can coexist with a healthy queue or an exploding one.
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Scale on leading indicators tied to user impact and finite buffers:
- Queue depth and waiting-request count.
- TTFT and end-to-end latency percentiles.
- Inter-token latency or TPOT.
- Input- and generation-token throughput.
- KV-cache usage and preemptions.
NVIDIA’s AIPerf documentation identifies growing queues, KV-cache usage above 0.9 and preemptions as capacity-limit indicators. Use that 0.9 value as a warning threshold from the documented tool, not as a universal SLO; validate an appropriate alert level for your engine and workload.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What can improve capacity without adding scarce accelerators?
Optimization is useful only when measured against quality and latency. Test one change at a time and retain the complete metric set.
Quantization
Lower-precision weights can reduce memory use and enable a smaller parallel split or more replicas. Measure task quality, TTFT, TPOT and output-token rate again; memory savings do not guarantee faster decoding.
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Larger batches often improve aggregate throughput by keeping the accelerator busy, but they can increase output-token latency and queue age. Select the setting that meets the SLO at the target concurrency, not the setting with the highest isolated tokens/s.
Prefix caching
When many requests share an exact prefix, caching can reduce repeated prefill work. Include cache-hit and cache-miss mixes in the trace; a workload with unique prompts will not receive the same benefit.
Model or context changes
A smaller model, shorter maximum context or revised routing policy may fit available memory. Treat the resulting quality, safety and product behavior as a new acceptance test rather than assuming functional equivalence.
What are the most common sizing mistakes?
- Using vendor peak numbers: replace theoretical figures with a benchmark of your model, engine, lengths and SLO.
- Testing one prompt length: replay the full input and output distributions, including long-tail contexts.
- Watching only utilization: add queue, latency, token and KV-cache metrics.
- Counting memory but not KV cache: verify maximum context and simultaneous requests under load.
- Scaling replicas linearly by GPU count: re-measure after changing tensor or pipeline parallelism and network topology.
- Ignoring preemptions: treat evictions and repeated work as lost capacity, not harmless noise.
- Leaving headroom implicit: document burst, failure and maintenance assumptions in the replica calculation.
- Comparing purchase price alone: include lead time, quota risk, power, cooling, failure domains and migration effort.
What should an operational capacity review contain?
Keep a versioned record for every production candidate so a model or infrastructure change triggers a deliberate re-check.
- Workload envelope: request, input-token and output-token rates; length and concurrency distributions.
- SLO definition: TTFT, TPOT or inter-token latency, end-to-end latency and percentile targets.
- Exact stack: model, quantization, engine, driver, parallelism, context limit, memory-utilization setting and maximum batched tokens.
- Benchmark evidence: sustainable requests/s, input/output tokens/s, queue stability, latency percentiles, KV-cache usage and preemptions.
- Replica plan: the three ceiling calculations, headroom factor, failure-domain layout and maintenance procedure.
- Fallback plan: an already-tested substitute accelerator or reduced-service mode, with known quality and latency effects.
Re-run the review after a model revision, quantization change, context-policy change, serving-engine upgrade, hardware substitution or substantial workload shift. Capacity is a property of the complete system at a defined operating point, not a permanent attribute of a GPU model.
Frequently Asked Questions
Can I estimate capacity from the model’s parameter count?
Parameter count can indicate that a model may need more memory, but it cannot determine serving rate. Quantization, context length, batch shape, interconnects, engine kernels and the prompt/completion mix all change the measured operating point.
Should I benchmark streaming and non-streaming responses separately?
Yes. Streaming exposes first-token and inter-token behavior to users, while non-streaming requests emphasize total completion time and buffering. Use the response mode your SLO promises, and do not combine their latency measurements into one result.
How do I size a fleet for multiple models sharing one accelerator pool?
Create a workload envelope and sustainable-capacity result for each model, then model the router’s placement and interference rules. Reserve capacity for the model with the strictest latency or memory requirement; aggregate only the portions that can safely share without violating either model’s SLO.
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What is a useful sign that a benchmark run was too short?
A run is suspect when queue depth, KV-cache occupancy or latency is still trending at the end, or when it never exercises the long-tail prompt and completion lengths. Extend the hold period until those signals settle, then repeat the result at least at the planned peak and burst loads.
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