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1. Establish a baseline for your workload
Before changing infrastructure, record what the service delivers and what it costs under representative traffic. Segment results by model, endpoint, region, and workload type so that a busy interactive endpoint does not obscure an idle batch service.
- Track prompt and output length distributions, request concurrency, and requests or tokens successfully served.
- Measure p50 and p95 latency, time to first token, throughput, and output quality against a defined acceptance bar.
- Record GPU utilization and billed GPU-seconds alongside successful requests or useful tokens.
- Identify idle periods, queueing, failed requests, retries, and scale-up or scale-down behavior.
This baseline makes later comparisons meaningful: a configuration is not cheaper in practice if it misses the latency target, degrades acceptable output quality, or serves fewer successful requests.
2. Right-size the accelerator for memory, throughput, and latency
Start by checking whether the complete serving workload fits in accelerator memory. Account for model weights, activations, KV cache, and runtime overhead—not just the model’s advertised parameter count. The KV cache can grow with context length and concurrent requests, so a setup that fits a short prompt at low concurrency may not fit the real workload. AWS recommends defining workload requirements first, assessing memory fit, and choosing instance types against throughput and latency goals (AWS guidance).
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Benchmark candidate configurations with representative prompt and response lengths, concurrency, and request patterns. Theoretical peak throughput alone does not establish that an instance can meet your service target. Compare viable candidates on the same model, quality bar, region assumptions, and latency target.
3. Increase useful work per GPU
Test lower precision and quantization
Lower precision or quantized weights can reduce memory use and may let a GPU serve more work concurrently. Google Cloud recommends considering 4-bit quantized models to maximize concurrency unless there is evidence of a quality impact; its documentation explains that quantization reduces model size and GPU memory needs and may increase runtime parallelism. Treat this as a configuration to validate, not a guaranteed improvement for every model or task. Measure output quality, memory use, throughput, and latency together.
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Tune batching and concurrency together
Batching can improve GPU efficiency by combining work, but waiting to form a batch can add latency. Concurrency also has a useful range: too much can create queues for GPU access, while too little can leave the accelerator underused and prompt unnecessary scale-out. Google Cloud warns that excessive maximum concurrency can increase latency and that a setting that is too low can underutilize the GPU and lead Cloud Run to scale out more instances than necessary (Cloud Run GPU configuration).
Test batch size and maximum concurrency as a pair, using realistic traffic and measuring queue time, p95 latency, throughput, and GPU utilization. The appropriate values depend on the model instances, parallel queries, batch configuration, and work performed outside the GPU.
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Reduce avoidable inference work
Where correctness and freshness allow, cache repeated or stable results. Route simpler tasks to a smaller model that meets the task’s quality bar, and use batching only where its added waiting time fits the latency budget. Microsoft’s Azure guidance also identifies caching, batching, request routing, and model selection as request-path cost levers (Azure inference guidance). Measure each change against your baseline rather than assuming it saves money.
4. Match provisioned capacity to demand
For variable traffic, autoscaling can reduce idle capacity, but the scaling signal must reflect the actual bottleneck. Cloud Run’s GPU autoscaling does not directly use GPU utilization by default; its default scaling considers CPU and request concurrency. Tune concurrency against measured serving capacity, and verify that scaling responds before queues or latency breach your target (Cloud Run GPU configuration).
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Decide whether to scale to zero
Scaling to zero can avoid paying for idle provisioned GPU capacity, but a new instance must start and load the model before it can serve traffic. Microsoft says GPU cold starts are typically tens of seconds and recommends benchmarking with the actual model (Azure online endpoints guidance). For latency-sensitive traffic, compare the savings from scaling down with the delay to recover capacity; keeping some warm capacity may be necessary.
5. Choose capacity terms to fit the workload
| Capacity option | When to evaluate it | Main trade-off |
|---|---|---|
| On-demand | Variable, short-term, or uncertain demand where flexibility matters. | Flexible, but may cost more than options tied to sustained use or interruption tolerance. |
| Commitment or reservation | Stable, predictable usage when the expected utilization and capacity need justify the term. | Terms and applicable scope constrain flexibility; compare the obligation with realistic demand. |
| Spot or other interruptible capacity | Batch or otherwise fault-tolerant inference that can recover from interruption. | Capacity may be reclaimed; retries, checkpointing, fallback capacity, and interruption costs affect effective cost. |
Commit only against stable demand
AWS describes Compute Savings Plans and Reserved Instances with one- or three-year terms for sustained use. Its 2025 guidance says Compute Savings Plans offer flexibility across instance family, size, availability zone, and region, while EC2 Instance Savings Plans are tied to a family in a region (AWS Cloud Financial Management). These are term structures, not a quote for today’s price. Compare current offers and capacity terms with expected usage before committing.
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Use Spot only when recovery is part of the design
AWS stated in a June 23, 2025 article that Spot discounts can be up to 90% versus On-Demand; this is a stated maximum, not a guaranteed saving or current quote (AWS Cloud Financial Management). Google Cloud describes Spot as suitable for fault-tolerant workloads and notes that instances can be preempted; Microsoft likewise warns that Azure Spot can be reclaimed and recommends checkpointing (Google Cloud Spot VMs; Azure Spot VMs). Include the cost of retries, lost work, fallback capacity, and interruption handling when assessing the effective rate.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.6. Compare all-in cost, not just the GPU-hour rate
A cloud GPU’s hourly price is only one part of the bill. Google Cloud says GPU charges are additional to the base machine type; prices vary by region, and GPU availability can vary by zone. Use the provider’s calculator and current account pricing to estimate the combined configuration (Google Cloud GPU pricing).
Include the GPU and base VM, CPU and memory, storage, networking, model storage, idle time, scaling behavior, and any commitment or interruption-related costs that apply. Provider prices, availability, Spot discounts, and commitment offers change, so check the relevant region and current terms when making a decision.
Use outcome measures that account for service delivered:
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- Cost per useful token: total relevant spend divided by tokens that count toward your task’s accepted output, using the same quality and latency bar across candidates.
Compare these alongside throughput, p95 latency, time to first token, memory fit, and utilization. A lower GPU-hour price can still yield a higher cost per useful result if the configuration serves less work or fails the service target.
Quick Recap
A practical benchmark loop
- Fix the acceptance bar. Define output quality, p95 latency, time-to-first-token, and throughput requirements for the workload.
- Choose representative traffic. Include real prompt and response lengths, concurrency, and peak as well as typical demand.
- Build a memory-safe baseline. Confirm weights, KV cache, activations, and runtime overhead fit the candidate accelerator.
- Change one class of settings at a time. Compare accelerator size, precision, batching, concurrency, caching, routing, or scaling behavior without obscuring which change caused the result.
- Calculate delivered cost. Use billable GPU and supporting resource costs, then divide by successful requests or useful tokens.
- Check operational risk. For scale-to-zero, account for model startup delay; for Spot, test recovery and fallback behavior; for commitments, confirm usage stability and term fit.
- Recheck after deployment changes. Traffic shape, model versions, regional rates, and capacity availability can shift the best configuration.
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