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Estimate GPU capacity by measuring the workload you actually need to serve—not by dividing a model’s parameter count by a favorite GPU. Define peak requests, token lengths, concurrency, latency and availability targets; benchmark tokens per second on candidate GPUs; check that weights, activations and KV cache fit in memory; then round the resulting GPU count up for failover, maintenance and growth. Estimate cost from GPU-hours plus the host, storage, networking, orchestration, monitoring, licensing, support, idle and retry overhead.
The practical result is usually a capacity plan with a dependable core (on-premises or reserved capacity) and elastic GPUs for surges. The method below covers inference and other AI jobs, while showing how to compare L4, H100, H200 and B200 configurations, on-demand pricing, Spot interruptions and commitment discounts.
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What information do you need before sizing GPUs?
Start with a written workload definition. “We have an AI model” is not enough: infrastructure changes dramatically with model size, precision, prompt length, traffic shape and service-level objectives. NVIDIA’s 2026 inference-sizing guidance specifically calls out daily active users (DAUs), concurrency, input and output token lengths, cache-hit rate, time to first token, intertoken latency, requests per DAU and contract length.
Record the model and execution assumptions
- Model family and parameter count: include the exact checkpoint and any adapter, reranker or auxiliary model.
- Precision and quantization: record whether weights and compute use FP32, FP16, BF16, INT8 or another format. A change in precision alters both memory and measured throughput.
- Maximum and typical context: prompt and completion lengths drive activation and KV-cache memory.
- Serving stack: note the inference engine, batching policy, sequence parallelism and any prompt or response cache.
Describe demand at the peak, not only the daily average
- Requests per second (RPS) at normal, busy and launch-day peaks.
- DAUs, requests per user and the expected distribution of request sizes.
- Concurrent requests, including long-running streams.
- Growth assumptions and short-lived spikes that a daily average hides.
Set measurable service objectives
- Latency: specify time to first token (TTFT), intertoken latency and, where relevant, a complete-response percentile.
- Availability: define the target and whether one GPU, one host or one zone may fail without an outage.
- Utilization: choose a ceiling that leaves room for batching variability, failover and maintenance instead of planning for permanent 100% utilization.
- Region and contract term: GPU availability, price and commitment options depend on the location and whether capacity is needed for hours, months or years.
How do you convert requests into a GPU count?
Measure the throughput of one candidate GPU under representative prompts, output lengths, precision and serving settings. Then divide required peak throughput by that measured per-GPU result, round up, and add explicit headroom.
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Use token throughput for generative models
For a streaming language model, distinguish prompt-processing throughput from generated-token throughput. A useful capacity calculation is:
required_GPU_count = ceil(peak_required_throughput / (measured_per_GPU_throughput × target_utilization)) + failover_and_growth_headroom
“Throughput” must use the same unit on both sides: requests per second for a request-rate benchmark, or generated tokens per second for a token-rate benchmark. If your traffic contains materially different prompt lengths, benchmark separate short, median and long cases and size for the mix that occurs at the peak.
Illustrative calculation
Suppose a load test records 120 generated tokens per second on one GPU for your chosen model, precision and prompt mix. If peak demand is 900 generated tokens per second and your utilization target is 70%, the capacity before resilience is ceil(900 ÷ (120 × 0.70)) = 11 GPUs. That arithmetic is only an example; the 120-token measurement must come from your own representative test. Add the GPUs required to survive a host failure, perform maintenance and accommodate growth rather than treating 11 as a production minimum.
When request rate is the limiting metric
For image generation, embeddings, classification or short prompts, benchmark completed requests per second at the target batch size. If one GPU completes 18 requests per second and peak demand is 100 requests per second, calculate ceil(100 ÷ (18 × target_utilization)), then apply resilience and growth headroom. Keep separate pools when workloads have incompatible latency or memory requirements.
How should memory be checked before compute?
A GPU that has enough arithmetic throughput but not enough memory cannot serve the model. Verify memory fit first, using the full runtime footprint rather than the checkpoint file alone.
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- Weights: account for every parameter at the actual serving precision, plus adapters or auxiliary networks.
- Activations: include temporary tensors created during prefill and decoding.
- KV cache: size for the maximum simultaneous sequences, their context lengths and the cache policy.
- Framework and runtime overhead: include CUDA libraries, allocator fragmentation, communication buffers and batching metadata.
- Operating headroom: reserve space for request variability, model updates and telemetry rather than filling the device to its nominal capacity.
If the total does not fit on one GPU, choose a smaller or quantized model only if quality and latency remain acceptable, or shard the model with tensor or pipeline parallelism. Sharding introduces interconnect traffic and coordination overhead; measure the multi-GPU result instead of assuming that two GPUs deliver twice the single-GPU throughput.
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Memory specifications for common Google Cloud configurations
| GPU | Per-GPU memory | Configuration detail | What to verify |
|---|---|---|---|
| NVIDIA L4 | 24 GB GDDR6 | Google Cloud G2 configurations | Model, KV cache and runtime must fit within 24 GB per device. |
| NVIDIA H100 | 80 GB HBM3 | Google Cloud A3 configurations | Check whether one device is sufficient or whether sharding is required. |
| NVIDIA H200 | 141 GB HBM3e | Google Cloud A3 Ultra configurations | Confirm regional availability and the memory benefit for long contexts. |
| NVIDIA B200 | 180 GB HBM3e | Google Cloud A4 machine types provide 8 GPUs and 1,440 GB aggregate GPU memory | Aggregate memory does not remove interconnect and parallelism costs. |
These capacities describe the cited Google Cloud configurations; they are not guarantees that every region, machine type or provider offers the same combination. Check the current configuration before committing.
How do you validate latency, concurrency and scaling?
Run a representative load test before buying or reserving capacity. Throughput measured with one short prompt and no concurrent users is not a production estimate.
Construct the test matrix
- Replay the expected distribution of input and output token lengths, including the longest supported context.
- Test concurrency from one user through the projected peak, then continue until TTFT, intertoken latency or error rate breaches the SLO.
- Measure cache-hit and cache-miss paths separately when prompt caching is enabled.
- Record requests per second, input and output tokens per second, TTFT, intertoken latency, complete-response latency, GPU memory and utilization.
- Repeat with the intended batching, quantization, parallelism and autoscaling settings.
Turn results into a capacity limit
For each candidate GPU, identify the highest sustainable load that meets the latency and error-rate objectives. Use that value—not the instantaneous peak shown by a dashboard—as the per-GPU capacity in the formula. Test a GPU or host failure if the availability objective requires continued service during an outage.
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Choose the least expensive configuration that passes memory, latency, throughput, availability and regional-availability tests. A larger GPU can be cheaper overall if it avoids multi-GPU sharding or supports more concurrent sequences, while a smaller GPU may win for light models and highly parallel short jobs.
Compare on the dimensions that affect total cost
| Dimension | Questions to answer |
|---|---|
| Memory fit | Does the model, maximum KV cache and runtime overhead fit with operating headroom? |
| Measured throughput | How many requests or tokens per second are sustained at the target precision and prompt mix? |
| Latency | Does TTFT and intertoken latency remain within the SLO at peak concurrency? |
| Scaling | How efficiently do two or more GPUs communicate, and does the host provide the required interconnect? |
| Availability | Can the provider supply the machine in the required region and zone? |
| Interruption tolerance | Can the workload checkpoint, retry or drain when capacity disappears? |
| Utilization | Will traffic keep the GPU busy, or will a large device spend most of its time idle? |
| Price model | Is the rate on-demand, Spot, reserved or commitment-based, and for what term? |
| Unit economics | What is the resulting cost per request, token, image or training step after all host and platform charges? |
How do you calculate the monthly cost?
Start with GPU-hours, then add every component required to run the service. Google Cloud notes that each GPU adds to the instance cost in addition to the machine type; a GPU-only comparison therefore understates the bill.
Core cost formula
monthly_cost = GPU_count × operating_hours × GPU_rate + host_CPU_RAM + storage + network_egress + orchestration + monitoring + licenses + support + idle_and_retry_costs
Use actual scheduled hours rather than assuming a 24/7 service. For a continuously running pool, multiply the hourly rate by the hours in the billing month and document the month used. Separate capacity that is provisioned but idle from capacity that is actively processing requests; autoscaling can reduce idle hours but may add startup latency and warm-up cost.
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Illustrative T4 arithmetic
Google Cloud’s listed on-demand table shows an NVIDIA T4 at $0.35 per GPU-hour. At 730 operating hours, one T4’s GPU line item would be 730 × $0.35 = $255.50 for that month, before the machine type, CPU, RAM, storage, network and other charges. The cited price is a listed table value; regional and product availability must be checked before using it in a forecast.
Track a useful unit cost
For inference, divide fully loaded cost by completed requests or output tokens. For image generation, use cost per image; for training or fine-tuning, use cost per successful training step or completed run. Include retries, failed jobs, warm standby capacity and egress in the numerator so a low GPU-only figure does not mislead decision-makers.
Should you use on-demand, Spot, reservations or commitments?
The right purchase model depends on interruption tolerance and how predictable the workload is. Compare all scenarios using the same effective capacity and resilience requirement.
| Model | Best fit | Important qualification |
|---|---|---|
| On-demand | New services, variable traffic and workloads that need immediate flexibility | Highest flexibility, but the standard rate applies and capacity is not automatically guaranteed. |
| Spot | Checkpointed training, batch inference and interruptible experiments | Google Cloud reports discounts of 60–91% off corresponding on-demand prices for many machine types and GPUs. Prices are dynamic, and resources can be reclaimed with 30 seconds’ notice. |
| Reservation | Steady production capacity where a specific zone must have supply | Google Cloud says reservations provide high assurance of capacity for zone-specific resources, including GPUs. Confirm the reservation’s scope and term. |
| Committed use | Predictable, continuous accelerator demand | Google Cloud documents discounts of up to 37% for one-year and up to 55% for three-year commitments for eligible accelerator-optimized resources. The commitment can become wasteful if demand falls. |
Spot capacity should not carry the entire availability SLO unless the service has a tested fallback. Design checkpointing, queue replay, graceful draining and automatic replacement before moving production work to interruptible instances.
What is a core-and-flex GPU plan?
A core-and-flex plan keeps dependable capacity for the baseline and adds elastic capacity for peaks, launches and experiments. NVIDIA’s 2026 guidance recommends a stable core of on-premises or reserved cloud GPUs plus on-demand or Spot capacity for surges.
Size the core
Use the demand that must be served during ordinary operation and the minimum availability objective. The core should still meet latency targets when flex capacity is unavailable, unless the product explicitly permits degraded service.
Size the flex pool
Calculate the difference between the peak requirement and the core’s tested capacity. Use on-demand GPUs when rapid, reliable scale-out matters; use Spot for queueable or checkpointed work. Set limits so a traffic spike cannot create an uncontrolled bill.
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Define degradation behavior
- Queue non-urgent requests instead of violating latency SLOs for every user.
- Reduce maximum context or output length only when the product contract allows it.
- Route to a smaller model or lower-throughput mode with a clearly documented quality trade-off.
- Return an explicit retryable response when all safe capacity is exhausted.
How can you complete a repeatable sizing worksheet?
Keep one worksheet per model and deployment region so assumptions remain auditable when traffic or pricing changes.
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- Workload: enter model, precision, context limits, input and output token distributions, RPS, DAUs, requests per user, concurrency, latency SLO and availability SLO.
- Benchmark: test each candidate GPU and record sustained requests or tokens per second, TTFT, intertoken latency, memory use and error rate.
- Capacity: apply the peak rate, utilization target, failover allowance and growth headroom; round up to whole GPUs and hosts.
- Memory: document weights, activations, KV cache, framework overhead and operating headroom. Mark whether tensor or pipeline parallelism is required.
- Cost: calculate GPU-hours and add CPU/RAM, storage, networking, orchestration, monitoring, licenses, support, idle time, retries and egress.
- Scenarios: price an on-demand baseline, a Spot or preemptible case, a reservation or commitment case and a core-and-flex design.
- Decision: select the option that meets the SLO at the lowest acceptable fully loaded unit cost, then record region, date and pricing assumptions.
What commonly makes a GPU estimate wrong?
Using daily averages
A daily average hides launch bursts and synchronized user activity. Size from measured peak RPS, token rates and concurrency, then use averages only for energy, budget or utilization analysis.
Ignoring KV-cache growth
Long contexts and many simultaneous streams can consume more memory than the weights. Include the maximum concurrent cache in the memory budget and test cache eviction behavior.
Assuming linear multi-GPU scaling
Tensor and pipeline parallelism add communication and scheduling overhead. Benchmark the exact host, interconnect and parallelism configuration.
Pricing only the accelerator
CPU, RAM, storage, network egress, orchestration, monitoring, licenses, support, idle time and retries can materially change cost per request. Keep these line items visible in every scenario.
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Treating Spot savings as guaranteed
Spot discounts are dynamic and reclaim can occur with 30 seconds’ notice. Include replacement capacity, checkpoint storage and the cost of interrupted work in the model.
Skipping regional validation
The T4 price example and the listed L4, H100, H200 and B200 configurations are tied to cited Google Cloud offerings. Check current regional supply, machine types, quota and networking before presenting a quote as achievable.
When should the estimate be revised?
Re-run the worksheet after a model, precision, context limit, batching policy or serving engine changes. Re-test when peak concurrency, output length, cache-hit rate or latency targets move. Recalculate prices when changing region, purchasing term or provider, and compare cost per useful request rather than the hourly GPU rate alone.
A defensible estimate is therefore a measured range: a documented workload, a memory-feasible configuration, a tested per-GPU capacity, explicit resilience headroom and a fully loaded cost under several purchasing scenarios. That record makes the next capacity decision faster and exposes which assumption is responsible when production behavior changes.
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How many GPUs are needed for a model that fits in one GPU?
Memory fit only proves that the model can load. The required count still depends on peak request or token rate, measured throughput at the target prompt mix, concurrency, latency SLO, utilization target and resilience headroom.
Is a larger-memory GPU always cheaper for inference?
No. A larger GPU may avoid sharding and improve usable concurrency, but it can be wasteful when the workload is light or highly parallel. Compare fully loaded cost per request or token after benchmarking both configurations.
How much can Spot GPUs reduce an AI bill?
Google Cloud reports discounts of 60–91% off corresponding on-demand prices for many machine types and GPUs. The percentage is not fixed: Spot prices are dynamic and capacity may be reclaimed with 30 seconds’ notice, so interruption handling and replacement cost belong in the estimate.
What should be monitored after deployment?
Track request and token rates, concurrency, TTFT, intertoken latency, error and retry rates, GPU memory, utilization, queue time, cache-hit rate and effective cost per request or token. Alert when actual peak behavior approaches the tested capacity limit.
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