Reduce GPU inference costs by serving more requests that meet your latency and quality requirements for the same spend—not by chasing peak tokens per second. Start with representative traffic, find the actual bottleneck, change one thing at a time, and keep only changes that improve cost per acceptable, SLO-compliant request.
Optimize for useful requests, not peak throughput
Raw throughput can rise while users wait longer or more requests miss their service-level objectives (SLOs). NVIDIA defines goodput as completed requests per second that meet specified metric constraints. For cost optimization, track goodput alongside the GPU spend required to achieve it, and count a request as useful only if it meets both the latency target and your application’s quality bar.
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Measure service behavior end to end, not only model-kernel speed. The core signals are time to first token (TTFT), inter-token latency (ITL), end-to-end request latency, output throughput, concurrency, errors, GPU utilization and memory, and the share of requests meeting the SLO. Queueing, batching delay, and network time can erase a kernel-level improvement. Tools may define metrics differently, so compare results only when measurement definitions and test conditions match. See NVIDIA’s metric definitions and reference architecture signals.
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Build a representative baseline before tuning
Benchmark a privacy-appropriate workload that reflects production traffic. Prompt and completion length, request arrival patterns, concurrency, and shared prefixes all affect which optimizations help. Longer prompts increase prefill work and can raise TTFT; longer generations increase decode work and can affect ITL. NVIDIA’s NIM 1.0.0 benchmark parameters describe these workload inputs.
Record the conditions needed to reproduce the run:
- Model and tokenizer versions, GPU type and count, serving engine and version, and precision.
- Input- and output-length distributions, request arrival pattern, concurrency, and any common prefixes.
- Metric definitions, latency percentiles, errors, GPU memory and utilization, output throughput, and SLO attainment.
- End-to-end results as well as engine-level measurements, at expected and peak load.
Keep prompts, output budgets, sampling settings, and test duration consistent across comparisons. NVIDIA’s TensorRT performance best practices likewise frame benchmarking and optimization as a measure-change-remeasure feedback loop.
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Find the bottleneck before choosing an optimization
Use the observed symptoms to narrow the search rather than changing several settings at once. High TTFT with long prompts points toward prefill pressure; poor ITL during generation can point toward decode throughput or memory-bandwidth pressure. If GPU execution looks healthy but end-to-end latency is poor, investigate queueing, batching waits, routing, and network time. Inspect batch size, KV-cache behavior, memory headroom, and prefill/decode saturation alongside user-visible metrics.
Metric guidance from NVIDIA NIM and signals in its inference reference architecture can help organize that diagnosis, but the measurements must reflect your deployment path.
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Tune batching and concurrency against the SLO
Batching can improve GPU utilization by scheduling work from multiple active requests together. Continuous or in-flight batching can admit and schedule active requests as they progress. By contrast, a batching policy that waits to collect a larger batch can add delay before execution. More concurrency may raise system throughput while worsening an individual user’s latency.
Sweep concurrency and batching settings under the representative workload. For each run, compare goodput, latency percentiles, error rate, and memory—not just aggregate tokens per second. Choose the highest useful throughput that still meets latency and error objectives. NVIDIA’s TensorRT optimization guidance and NIM metrics discuss these performance trade-offs.
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Choose workload-specific changes and test their trade-offs
| Option | Most relevant when | What to verify |
|---|---|---|
| Prefix or KV-cache reuse | Requests repeatedly share context, so repeating prefill work may be avoidable. | Cache hit behavior, memory use, routing effects, and whether reuse improves end-to-end latency and cost for the real traffic mix. |
| Chunked prefill | Long prompts or prefill work interfere with ongoing token generation. | TTFT, ITL, throughput, and memory under the same arrival pattern; gains depend on engine support and workload. |
| Separate prefill and decode | The service needs to manage different prefill and generation compute patterns independently. | Cache transfer overhead, routing, added infrastructure and operational complexity, and end-to-end SLO goodput. |
| Lower precision / quantization | Memory capacity or bandwidth is a demonstrated constraint, and suitable kernels are available for the hardware and model. | Kernel support, serving cost, memory, latency, and task-specific quality and safety results against the original configuration. |
| Speculative or other decoding methods | Generation latency or decode throughput is the bottleneck and the serving engine supports a suitable method. | Quality, output behavior, latency, and throughput with identical prompts, output budgets, and sampling settings. |
These are experiments, not universal wins. KV-cache reuse can avoid repeating work for common context, while chunked prefill and disaggregated serving address different scheduling needs. Cache transmission, memory consumption, routing, and deployment complexity belong in the cost comparison. NVIDIA covers these approaches in its inference optimization overview and disaggregated serving documentation.
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Quantization may reduce memory and bandwidth pressure, but benefits depend on the bottleneck, model, hardware, and supported kernels. Check the relevant TensorRT quantization documentation and the capabilities of your serving engine, such as the rolling vLLM documentation. For a decoding change, NVIDIA’s TensorRT-LLM guide is another implementation reference.
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Gate every change on latency, quality, and cost
- Establish the baseline. Save the workload, software and hardware configuration, measurement definitions, latency percentiles, errors, quality results, and GPU costs.
- Make one targeted change. Start with the bottleneck identified in the baseline. Keep other settings and evaluation conditions fixed.
- Rerun at expected and peak load. Compare end-to-end latency, SLO goodput, errors, memory headroom, output throughput, and task-specific quality.
- Calculate cost per good request. Divide serving spend over the measured interval by the number of completed requests that meet both the latency and quality bar. Use the same interval and workload when comparing configurations.
- Roll out cautiously. Increase exposure incrementally, monitor latency, errors, quality signals, and GPU memory, and retain a rollback configuration.
For quality gates, use evaluations representative of the application’s tasks and compare the candidate configuration with the unmodified baseline. Include safety checks where relevant; a general-purpose benchmark alone does not establish that your application’s answers remain acceptable.
Keep benchmark claims in their proper context
Performance depends on the model, accelerator, runtime and version, request distribution, and measurement setup. NVIDIA’s speculative-decoding post reports a “3x” throughput result for a named Llama 3.3 70B configuration; that is a vendor demonstration for that setup, not a general forecast for other models or services. The official documentation cited here includes rolling pages, TensorRT quantization material under version 10.x, and NIM benchmark parameters from version 1.0.0. Check the documentation and feature support for the exact versions deployed before relying on a configuration.
No broadly generalizable cost-reduction percentage follows from these sources. The defensible target is the measured cost of requests that meet your own SLO and quality requirements.
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