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There is no defensible answer to “how many GPUs do I need?” from model size alone. Estimate capacity by defining the request workload and latency objective, fitting the model and its runtime memory on a tested GPU configuration, measuring the throughput-versus-latency curve on the actual serving stack, and then converting the sustainable operating point into whole replicas with explicit resilience and growth capacity.
The practical result is a measured number such as “two replicas of this configuration sustain the busy-hour request mix at the required p99 latency,” not a vendor peak-token figure or a calculation based only on parameter count.
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What GPU capacity means for an inference service
Capacity is the amount of demand a deployment can serve while meeting its service objectives. Depending on the application, demand may be measured in requests per second, input (prefill) tokens per second, output tokens per second, or concurrent sequences. These are not interchangeable.
A chat service can appear to have high aggregate output-token throughput while individual users wait too long for the first token or receive tokens too slowly. Conversely, a batch summarization job may care primarily about total tokens processed and accept a longer queue. Capacity therefore has to be stated with the workload, latency target, model, precision, serving software and GPU topology attached.
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Keep these user-facing measurements separate:
- Time to first token (TTFT): time from request submission until the first generated token.
- Inter-token latency (ITL): the interval between generated tokens after generation starts.
- Total request latency: TTFT plus generation time, including queueing.
- Throughput: requests, input tokens or output tokens completed per unit of time.
- Concurrency: requests or sequences active at the same time, including work waiting for execution.
NVIDIA AIPerf exposes request rate, token rates, p99 latency, prompt and output lengths, cache utilization, preemptions and waiting requests. Those fields let you describe capacity in terms that match both infrastructure behavior and user experience.
1. Define demand before choosing hardware
Start with an observed trace or a forecast for the busiest operating period. A single average request is not a workload specification: long prompts, long generations and burst arrivals can determine the required memory and queue capacity.
Record the request mix
- Steady and peak request arrival rates, with peak-to-average ratios.
- Concurrent requests during normal, busy and burst conditions.
- Input-token distribution, including p50, p95 and p99 lengths.
- Output-token distribution and the maximum generation length accepted by the API.
- Streaming versus non-streaming behavior.
- Cancellation rate, retries and any priority classes.
- Whether traffic is interactive, asynchronous or a mixture of both.
Preserve correlations in the trace where possible. For example, a long prompt may also request a long answer; replacing that pair with two independent averages understates memory and scheduling pressure.
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Write explicit service objectives
Set separate targets for TTFT, ITL and total latency. A statement such as “p99 under 2 seconds” is incomplete unless it says whether the clock ends at the first token or at the final token. Also specify the load at which the target applies, such as the forecast busy-hour rate and a defined burst scenario.
Choose the optimization priority before benchmarking. NVIDIA’s guidance distinguishes per-user token speed, bulk throughput and fast first-token response. Increasing concurrency can improve aggregate throughput while reducing per-user generation speed, so a single “maximum tokens per second” number cannot select the right operating point.
2. Establish the model’s memory floor
Identify the exact model revision and serving requirements before comparing GPUs. Capture the architecture, parameter count, stored weight format, maximum context length, multimodal components and any adapters or fine-tuning modules. A model that fits in an unloaded memory calculation may not fit once the runtime starts.
Account for every major allocation
TensorRT-LLM describes inference memory as more than weights. The budget includes:
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- Activation tensors: temporary values needed during execution.
- Input/output tensors: tensors associated with requests and generated results.
- KV cache: per-layer key and value states retained for active sequences.
- Runtime buffers: workspaces and scheduler allocations sized from batch and sequence limits.
- Framework and communication allocations: memory used by the serving process, kernels and multi-GPU collectives.
Runtime buffers can be allocated from configured maximum batch size and sequence length rather than from the average request. Leave enough free memory for these allocations and for transient peaks; treating all nominal GPU memory as weight capacity will produce an unsafe estimate.
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Use parameter arithmetic only as a floor
A rough weight floor can be obtained by multiplying parameter count by bytes per stored parameter, then adding format metadata. That calculation does not include KV cache, activations, runtime buffers, fragmentation, batching or communication. It is useful for eliminating obviously undersized devices, not for declaring a production configuration.
Quantization can lower the weight footprint, but it can also change kernel support, throughput and quality. Measure the exact checkpoint and precision that will be deployed. Do not substitute a similarly named model or assume that a lower-bit format has the same latency profile.
3. Size KV cache and concurrent sequences
For autoregressive models, KV cache is often the allocation that turns a seemingly adequate GPU into a concurrency-limited system. Each active sequence consumes cache for its prompt and generated tokens. Longer contexts and more simultaneous requests therefore reduce the number of sequences that can remain resident.
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Use the engine’s own startup and runtime reports instead of inventing a cache formula from model parameters. vLLM reports the total GPU KV-cache tokens and an estimated maximum concurrency for a specified request context length. Compare that estimate with the actual concurrent-request distribution, especially the p95 and p99 context cases.
TensorRT-LLM exposes a maximum paged-cache token count and a fraction of free GPU memory for cache allocation. Its documented default fraction is 0.9 after model loading. That is a configuration behavior, not a universal recommendation: weights, activations, runtime buffers and other allocations must still fit, and a high reservation can leave too little safety margin for the workload.
Options when cache capacity is short
- Use GPUs with more usable memory, after verifying the complete runtime footprint.
- Add GPUs or nodes and select an appropriate tensor- or pipeline-parallel configuration.
- Reduce the maximum context or enforce application-level prompt limits.
- Use a supported memory-saving precision or quantization format and rebenchmark quality and speed.
- Adjust cache and scheduling limits, then test fragmentation, preemption and queue behavior.
- Separate long-context traffic from short interactive traffic when the serving architecture permits it.
These changes alter more than memory. Parallel execution can add synchronization and interconnect overhead, while shorter contexts may change product behavior. Re-measure after every material change.
4. Benchmark the throughput-versus-latency curve
Capacity is a curve, not a single specification. Benchmark the exact model, precision, GPU configuration, serving version and request shape that you intend to operate.
Build a representative test
- Load the production model revision and tokenizer on the candidate serving stack.
- Set the same maximum context, batch, cache and scheduling limits used in deployment.
- Generate requests using the measured input- and output-length distributions, including long-tail cases.
- Sweep concurrency and, separately, request arrival rate. Include steady load and burst arrivals.
- Warm the system, discard startup measurements and run long enough to observe queueing and cache reuse.
- Record TTFT, ITL, total p50/p95/p99 latency, request rate, input-token rate, output-token rate, cache use, preemptions and queued requests.
- Repeat for each candidate GPU count, precision and parallelism topology.
NVIDIA identifies GenAI-Perf as a command-line tool for measuring throughput and latency for models served with Triton. NVIDIA’s benchmark material also discusses trtllm-bench concurrency sweeps. Whichever tool you use, record model, GPU, software versions, precision, token limits and concurrency beside every result so that later comparisons remain valid.
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Choose an operating point, not the peak
Plot aggregate throughput and user-facing latency against concurrency. Select the highest demand that still satisfies the TTFT, ITL and total-latency objectives at the required percentile. A point beyond that boundary may produce more total tokens while violating the user experience.
One NVIDIA illustrative benchmark used a 50-output-tokens-per-second-per-user target. In that vendor-specific Llama 3.1 8B FP16 configuration, approximately 256 concurrent users reached about 72 output tokens per second per user before crossing the stated constraint. An optimized Llama 3.1 8B FP8 checkpoint in the cited setup reached approximately 512 concurrent users at about 66 output tokens per second per user under the same illustrative constraint. These figures apply only to the models, hardware, software and workload in that benchmark; they are not capacity promises for another service.
Do not multiply such a peak by a user count without checking first-token latency, queueing and per-user generation speed. A service that needs fast responses may have a lower sustainable operating point than a throughput-only batch job.
5. Convert measured capacity into a GPU and replica count
Once a benchmark supplies sustainable capacity at the chosen latency target, use compatible units:
replicas = ceil(peak demand / sustainable measured demand per replica)
If demand is measured in output tokens per second, the denominator must be output tokens per second from a workload with the same input/output mix. If requests vary substantially, use requests per second with the representative distribution or model the mix explicitly rather than dividing unrelated token and request figures.
Separate model fit from arithmetic capacity
A replica may require several GPUs simply to fit the model and cache. In that case, round the number of whole multi-GPU replicas, not individual accelerators. For distributed execution, benchmark scaling and communication overhead; two GPUs do not necessarily provide twice the single-GPU capacity.
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The arithmetic floor is not a production plan. Keep additional capacity for failures, maintenance, traffic bursts, rolling deployments and forecast growth. The appropriate reserve depends on your availability objective and traffic volatility; there is no universal percentage established for every inference service. Document the assumptions and test the loss of a replica or host.
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Example calculation with variables
Suppose the busy-hour workload is D requests per second and a tested replica sustains C requests per second while meeting the p99 latency objective. The capacity floor is ceil(D/C) replicas. If each replica needs G GPUs for model fit and parallelism, the compute floor is G × ceil(D/C) GPUs. Add separately documented replicas for failure and maintenance rather than hiding that requirement in a rounded token-throughput number.
6. Choose a scaling topology
| Topology | When it helps | What to validate |
|---|---|---|
| One GPU per replica | The model and required cache fit comfortably on one device. | Per-replica throughput, cache headroom and failure behavior. |
| Tensor parallelism | A model or cache does not fit on one GPU, or a larger memory pool is required. | Collective communication, interconnect bandwidth, synchronization overhead and scaling efficiency. |
| Pipeline parallelism | Layer partitioning across devices is appropriate for the model and workload. | Pipeline bubbles, scheduling, batch sensitivity and end-to-end latency. |
| Multiple nodes | One host cannot provide sufficient memory or the required replica count. | Network latency, failure domains, placement and cross-node communication. |
vLLM documents tensor and pipeline parallelism and advises adding GPUs or nodes when cache capacity or throughput requirements are unmet. Treat that as a scaling direction, not a linear-performance assumption.
7. Monitor capacity after deployment
Benchmark results describe a controlled experiment. Production traffic changes request lengths, arrival patterns and cache residency, so monitor the signals that reveal saturation.
| Signal | What it tells you |
|---|---|
| Output-token and request rate | Actual completed work and whether demand is approaching the tested operating point. |
| p99 TTFT, ITL and total latency | Whether the user experience still meets each SLO. |
| Prompt and output lengths | Whether production has drifted from the benchmark mix. |
| KV-cache utilization | How close active sequences are to the cache limit. |
| Preemptions | Whether sequences are being displaced because memory or scheduling capacity is constrained. |
| Waiting requests and queue age | Whether arrivals exceed service capacity or bursts are too large. |
| GPU memory use | Whether allocations are approaching the configured limit or leaving insufficient transient headroom. |
NVIDIA AIPerf’s guide describes KV-cache use above 0.9 as near full capacity and identifies growing queues and preemptions as pressure indicators. These are diagnostic signals, not universal SLO thresholds; interpret them with the engine’s scheduler and your latency objective.
Respond to common saturation patterns
- High TTFT, normal ITL: inspect queue depth, prefill throughput and burst arrivals.
- Normal TTFT, slow ITL: inspect decode concurrency, output lengths and memory bandwidth.
- Cache near full with preemptions: reduce resident context, add memory or add replicas.
- GPU utilization low while queues grow: check CPU tokenization, networking, synchronization, batching limits or an incorrectly sized scheduler.
- Latency rises only for long prompts: split or route long-context traffic, revise context limits or benchmark a larger cache.
- Production throughput below the test: compare request-length distributions, software versions, health checks, logging overhead and traffic routing.
8. Compare GPU options on useful capacity
When evaluating a GPU accelerator, GPU server or managed GPU allocation, compare the complete operating point rather than memory size or theoretical compute alone.
| Comparison axis | Questions to answer |
|---|---|
| Model fit | Do weights, activations, runtime buffers and the required KV cache fit at the target context and concurrency? |
| User experience | What are p95 and p99 TTFT, ITL and total latency at busy-hour load? |
| Throughput | What sustainable request, input-token and output-token rates meet the latency bound? |
| Concurrency | How many sequences remain resident, and when do preemptions or queue growth begin? |
| Topology | Does the design need tensor or pipeline parallelism, and what interconnect penalty does that impose? |
| Operations | Can the stack be upgraded, observed and recovered without violating availability objectives? |
| Economics | What is the cost per useful request or token at the target SLO, including reserve capacity and energy or hourly allocation? |
No current price is meaningful without a region, procurement model and measured utilization. Compare cost only after establishing a valid, SLO-compliant capacity figure.
Why simple GPU-count formulas fail
- Parameter count ignores runtime memory: KV cache, activations and buffers can dominate at high concurrency.
- Peak tokens per second ignores users: aggregate throughput can rise while per-user speed falls.
- Average lengths hide tails: p95 and p99 prompts can trigger cache exhaustion and queueing.
- Vendor results are conditional: model revision, precision, hardware and benchmark settings determine the result.
- Linear scaling is unsafe to assume: communication and scheduling overhead increase with parallelism.
- Documentation defaults are not SLOs: a cache fraction or utilization threshold is a starting behavior, not a guarantee.
NVIDIA’s 2024 sizing presentation reports its described simulation approach as accurate within 10–15% for small models and 3–5% for large models in the presentation’s scenarios. Those figures are not independent validation or universal accuracy guarantees; a representative benchmark remains the final authority for your deployment.
Quick Recap
A repeatable capacity worksheet
- Workload: record peak and burst request rates, concurrency, prompt/output p50–p99 lengths and streaming behavior.
- SLO: define TTFT, ITL and total-latency targets at explicit percentiles and load levels.
- Model: pin the revision, architecture, context limit, precision, adapters and multimodal requirements.
- Memory: account for weights, activations, I/O tensors, runtime buffers and KV cache.
- Engine: record serving software and version, scheduler, batch limits, cache limits and parallelism.
- Benchmark: sweep concurrency and arrival rate with representative and tail request shapes.
- Decision: select the highest sustainable point that meets every latency objective.
- Deployment: round up to whole replicas, then add documented failure, maintenance, burst and growth capacity.
- Operations: alert on latency, queueing, cache pressure, preemptions and workload drift, and rerun the benchmark after material changes.
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