The Tool Desk
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To benchmark AI inference throughput credibly on NVIDIA DGX or RTX hardware, hold the model, checkpoint, precision, tokenizer, prompt and output distributions, software stack, parallelism, concurrency, and clock settings constant. Warm up the engine, measure a stable window repeatedly, and report tokens per second together with time to first token (TTFT), inter-token latency (ITL), requests per second, and tail latency. Use trtllm-bench throughput for maximum-load offline capacity, trtllm-bench latency for low-latency tests, and GenAI-Perf for an online OpenAI-compatible endpoint. Compare DGX and RTX only when GPU count, topology, and whether throughput is per GPU or total are explicit.
What does an AI inference throughput benchmark need to prove?
A useful benchmark answers a specific capacity or user-experience question, not merely “how fast is this GPU?” Throughput is workload- and configuration-dependent. The same accelerator can produce very different results when you change quantization, context length, batch policy, request arrival rate, KV-cache settings, or the number of GPUs participating in one request.
Define the decision before running anything. A capacity test may ask how many output tokens a server can generate at maximum load. A production test may ask how much throughput is available while P95 TTFT stays below a service-level objective (SLO). A streaming application may care more about first-token delay and token cadence than aggregate tokens per second.
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- Low-latency behavior: use a lightly loaded or single-request pattern to see prompt processing and decode responsiveness.
- Online capacity: send requests to the same network service shape used in production and sweep concurrency or arrival rate.
- Standardized comparison: follow a prescribed benchmark such as MLPerf Inference when external comparability is the priority.
Write down the target model and workload first. A synthetic 128-input/128-output-token workload is repeatable, but it is not workload-neutral: applications with long prompts, short answers, tool calls, or uneven output lengths will behave differently.
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Which configuration variables must remain fixed?
Every comparison run should have an immutable test recipe. If a variable changes between DGX and RTX, or between two software builds, the result is not an accelerator comparison; it is a mixed configuration comparison.
| Category | Record exactly | Why it changes the result |
|---|---|---|
| Model | Model family, exact checkpoint or commit, architecture, tokenizer, vocabulary files | Weights, tokenizer behavior, and architecture determine compute and memory traffic. |
| Numerics | FP32, FP16, BF16, FP8, INT8, or another quantization; calibration method and scales | Precision changes arithmetic throughput, memory use, and sometimes output quality. |
| Workload | Prompt text or input IDs, input-token distribution, requested output-token distribution, stop conditions | Prefill and decode costs depend on sequence lengths and early stopping. |
| Runtime | TensorRT-LLM or PyTorch backend version, engine-build options, CUDA, cuDNN, NCCL, driver, operating system | Kernels, scheduling, communication, and compatibility vary by release. |
| Parallelism | GPU count, tensor-parallel and pipeline-parallel degrees, data-parallel replicas, rank placement | Communication and interconnect topology determine scaling efficiency. |
| Serving policy | Batching or continuous batching, maximum batch size, maximum sequence length, admission and queue limits | Schedulers trade latency for utilization and can change which requests share a batch. |
| Cache and memory | KV-cache data type, capacity limits, paging or reuse policy, reserved workspace | Cache pressure can trigger eviction, recomputation, or admission failures. |
| Clocks and power | Persistence mode, application clocks or boost policy, power limit, thermal state | Frequency changes and thermal throttling can swamp small software differences. |
| Measurement | Warm-up duration, measurement-window boundaries, repetitions, concurrency or request-rate sweep | Including startup, cooldown, or queueing time changes reported statistics. |
NVIDIA’s TensorRT-LLM guidance recommends persistence mode and standardized clock, power-limit, and boost settings for rigorous comparisons. Apply the same policy to every system and record the actual values, not just the intended settings.
How should I prepare a deterministic workload?
Use a versioned dataset so another engineer can send the same requests. TensorRT-LLM’s throughput schema requires a task ID, either a prompt or input IDs, and an output-token count. Store the dataset beside the benchmark configuration and checksum both files.
Choose representative length distributions
Measure the application’s real prompt and completion lengths when possible. Preserve percentiles rather than replacing everything with an average: a chat service may have many short prompts and a small number of very long contexts. Keep requested output lengths explicit so a request that stops early can be distinguished from one that was configured for fewer tokens.
Use synthetic data deliberately
A fixed 128-input/128-output-token set is useful for repeatability and for checking that a new engine behaves as expected. Label it as a synthetic operating point. Do not present its throughput as a universal property of a DGX or RTX card, because changing either length changes prefill and decode work.
Control randomness and stopping
For deterministic comparisons, fix the random seed where the runtime supports it, use identical sampling parameters, and make stop sequences explicit. If production uses variable sampling or tool-triggered early exits, create a second, application-shaped dataset instead of quietly changing the synthetic test.
Which NVIDIA benchmark tool should I use?
Choose the tool based on where you need to measure. trtllm-bench is appropriate for an engine or PyTorch-backend workflow running directly on the host. GenAI-Perf is appropriate when an OpenAI-compatible network endpoint is part of the system under test. MLPerf Inference is for prescribed, externally comparable results.
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| Question | Recommended mode | What it reveals |
|---|---|---|
| What is the maximum output capacity of this engine? | trtllm-bench throughput |
Maximum-load TPS and RPS under the selected dataset and batching policy. |
| How quickly does a lightly loaded request respond? | trtllm-bench latency |
Prompt and decode latency with low queueing pressure. |
| How does a production endpoint behave as load rises? | GenAI-Perf against an OpenAI-compatible service | TTFT, ITL, end-to-end latency, throughput, and tails while concurrency or request rate changes. |
| How does this platform compare under a formal industry workload? | MLPerf Inference | Results produced under prescribed rules and audit conditions. |
TensorRT-LLM can build tuned engines, execute Python-based workflows, and emit throughput and latency summaries. Keep the tool version with the result because output fields and defaults can change between releases.
How do I run an offline throughput benchmark with trtllm-bench?
The offline procedure isolates engine capacity from network clients and service infrastructure. It is the right first measurement for finding a maximum-load upper bound, but it does not replace an online test.
- Freeze the environment. Record GPU model and count, PCIe or NVLink topology, driver and CUDA versions, TensorRT-LLM version, backend, model revision, precision, and all parallelism settings. Set persistence mode and the agreed clock and power policy.
- Build or load the engine. Use the same build options for every system. Record workspace limits, fused-kernel choices, quantization calibration, maximum sequence length, and KV-cache configuration.
- Validate one request. Confirm that tokenization, generated text, stop behavior, and output-token accounting are correct before stressing the engine. A fast benchmark that counts the wrong tokens is invalid.
- Prepare the fixed dataset. Supply each task ID, prompt or input IDs, and requested output-token count. Keep the file immutable during the run.
- Warm up. Start the server or engine and issue requests until compilation, memory allocation, and cache initialization have completed. Exclude this phase from the reported measurement.
- Run
trtllm-bench throughput. Use the tool’s documented options for the chosen backend and load policy. Save its complete summary, raw output, and the exact command line. - Repeat. Run multiple independent measurement windows after warm-up. Note thermal or clock changes and discard a run only with a documented reason.
- Run the latency mode separately. Use
trtllm-bench latencywith a low-load policy when you need responsiveness rather than maximum utilization. Do not compare its latency number directly with a maximum-load throughput run.
One current TensorRT-LLM documentation example reports 20,685.5510 output tokens per second and 161.6059 requests per second for a documented Llama 3.1 8B PyTorch-backend run with 128 input and 128 output tokens. The same sample reports 18,563.6825 ms total latency. Those are documentation outputs, not universal DGX or RTX figures; reproduce them only when the model, backend, dataset, software, and configuration match exactly.
How do I benchmark an online OpenAI-compatible endpoint?
An online test includes request scheduling, queueing, serialization, and server admission behavior. Expose the identical service interface on each system, then use GenAI-Perf or TensorRT-LLM’s serving benchmark to sweep load.
- Define the endpoint contract. Fix model name, authentication path, streaming setting, sampling parameters, maximum tokens, and timeout. Use the same network location or document the network difference.
- Verify a clean baseline. Send a small number of requests and check that prompt and completion token counts match the offline dataset. Confirm that streaming chunks are parsed consistently.
- Choose a load sweep. Increase concurrency, or increase request rate when the tool supports open-loop arrival. Record the offered load, not only the number of active clients.
- Warm the service. Allow engine caches, memory pools, and connection pools to settle. GenAI-Perf uses a sliding window so warm-up and cool-down requests are excluded from representative statistics.
- Save machine-readable output. Preserve CSV or JSON for every load point, plus server logs and configuration. Plot throughput against TTFT, ITL, end-to-end latency, and the chosen SLO.
- Find the knee. Stop increasing concurrency when latency rises sharply while throughput gains become small. That knee is often a more useful operating limit than the absolute maximum TPS.
Repeat the sweep with the application’s real length distribution. A service that looks stable on 128/128 synthetic requests may queue badly when a few long-context requests consume most of the KV cache.
What do TTFT, ITL, TPS, RPS, and latency actually mean?
These metrics describe different parts of the user and capacity experience. Report them together; no single number can characterize an interactive language-model service.
| Metric | Definition | Use it to answer |
|---|---|---|
| TTFT | Time from request acceptance until the first output token is available | How long a user waits before seeing a response. |
| ITL / TPOT | Average delay between consecutive output tokens; GenAI-Perf excludes the first token from the interval calculation | How smoothly tokens arrive after streaming starts. |
| End-to-end latency | TTFT plus generation time | How long the complete request takes. |
| TPS | Total output tokens divided by the measured interval | Aggregate generation capacity; state exactly which interval is counted. |
| RPS | Completed requests divided by the measured interval | Request-serving capacity at the tested length distribution. |
| P50, P90, P95, P99 | Latency percentiles across requests | Typical and tail behavior under queueing and contention. |
Measurement intervals differ between tools. Some include request preparation or client overhead; others measure only server processing. State the interval and token-counting rule beside every TPS and RPS value.
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Use equations consistently
End-to-end latency = TTFT + generation time. For throughput, TPS = total output tokens ÷ measured interval; for request rate, RPS = completed requests ÷ measured interval. If retries, failed requests, or truncated generations are excluded, disclose that policy and report the error count separately.
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How should I compare DGX and RTX systems fairly?
Never compare a multi-GPU DGX total with a single RTX result as though they were equivalent. State GPU count, model, memory configuration, interconnect, tensor- and pipeline-parallel degrees, and whether the number is per GPU or for the whole server.
| Comparison axis | Required control | Interpretation |
|---|---|---|
| Throughput at a latency budget | Same model, dataset, precision, software, and fixed TTFT or tail-latency limit | Shows useful capacity under a user-facing SLO. |
| Latency at a throughput target | Same offered or completed load and same output-length distribution | Shows responsiveness when both systems serve equal work. |
| Per-GPU versus total | Label the aggregation and GPU count | Prevents a four- or eight-GPU result being mistaken for single-card performance. |
| Scaling efficiency | Run one, two, and more GPUs with identical parallelism policy where possible | Reveals communication and topology costs. |
| Power or cost context | Include only measured power, price, or operating data | Do not infer economics from a throughput number alone. |
NVIDIA’s published TensorRT-LLM overview includes reference output-throughput measurements for H100 SXM in DGX H100, H200 SXM in DGX H200, B200 in DGX B200, GB200 NVL72, and RTX 6000 Pro Blackwell Server Edition. NVIDIA cautions that these are reference measurements rather than guaranteed peak performance and that hardware variants can differ in TDP, memory bandwidth, core count, and features. Treat them as orientation, not as a substitute for your recipe.
Account for topology and parallelism
A DGX’s high-bandwidth GPU fabric can reduce synchronization cost compared with a workstation or server whose GPUs communicate over PCIe. Record the topology and rank placement, then keep tensor and pipeline parallelism fixed when comparing hardware. If a model fits on one RTX but requires multiple DGX GPUs, report that capacity difference explicitly instead of presenting a misleading per-card ratio.
How do I interpret the throughput–latency curve?
Plot completed throughput on the vertical axis and TTFT, P95 latency, or another user metric on the horizontal axis for each concurrency or request-rate point. At low load, adding requests usually raises utilization and throughput. Near saturation, queueing makes latency climb while throughput flattens; that knee is the practical capacity boundary.
Choose an operating point
- For interactive chat, select the highest throughput whose TTFT and P95/P99 end-to-end latency remain within the SLO.
- For batch generation, select the point with the desired completion rate, even if TTFT is high, and verify that memory and thermal limits remain stable.
- For streaming, enforce both a TTFT ceiling and an ITL ceiling; a high TPS average can hide a long first-token wait or irregular token cadence.
Annotate each point with concurrency, offered request rate, input and output length percentiles, and error or timeout counts. A curve without those labels cannot be reproduced or used for capacity planning.
When should I use MLPerf Inference instead?
Use MLPerf Inference when you need a prescribed, externally comparable result across hardware, software, or services. Its evaluations are designed to be unbiased and conducted under prescribed conditions. Follow the applicable rules, scenarios, and submission requirements rather than substituting a locally convenient dataset.
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Use TensorRT-LLM benchmarks or GenAI-Perf for application-specific tuning: selecting quantization, sizing a serving cluster, tuning batching, or checking an OpenAI-compatible production endpoint. Those tools let you preserve your real prompts and service policies, but the results are only comparable when the full recipe is shared.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What should a benchmark report contain?
Publish a compact result table plus an appendix containing the reproducibility record. The following fields prevent most ambiguous claims.
- System name, exact GPU model, GPU count, memory size, topology, host CPU, and interconnect.
- Model identifier, checkpoint revision, tokenizer revision, precision or quantization, and calibration details.
- Driver, CUDA, cuDNN, NCCL, TensorRT-LLM, backend, operating-system, and container versions.
- Tensor, pipeline, and data parallelism; rank placement; batching and scheduler limits.
- KV-cache type and limits, maximum sequence length, workspace allocation, and memory utilization.
- Dataset checksum, prompt and output length distributions, sampling and stopping parameters.
- Persistence mode, clocks or boost policy, power limit, ambient or thermal observations.
- Tool mode, command line, warm-up policy, measurement-window definition, repetition count, and discarded-run policy.
- TPS, RPS, TTFT, ITL, end-to-end latency, P50/P90/P95/P99, failures, timeouts, and whether values are per GPU or total.
Include raw CSV or JSON and a plot of throughput against latency. If a figure comes from vendor documentation, label it as a reference result and retain the vendor’s workload description.
What are the most common benchmark mistakes?
Mixing workloads
Comparing 128-token prompts on one system with long-context production traces on another invalidates the comparison. Keep distributions identical and show both input and output statistics.
Reporting only aggregate TPS
Aggregate TPS can rise while TTFT or P99 becomes unacceptable. Always pair throughput with latency percentiles and the concurrency or request rate that produced them.
Counting warm-up and cooldown
Engine compilation and cache allocation distort short tests. Warm up first, use a stable window, and use a sliding-window method or equivalent exclusion for online runs.
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A total from eight GPUs is not a single-GPU result. Put “per GPU” or “total across N GPUs” in the column heading and explain the parallelism.
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Changing clocks or power policy
Thermal throttling and opportunistic boost can create run-to-run variation. Standardize persistence, clocks, and power limits, and log temperatures and observed frequencies.
Assuming vendor reference numbers are guarantees
Reference tables are useful sanity checks, but board design, TDP, memory bandwidth, software versions, and workload details differ. Reproduce the documented setup before making a direct claim.
How can I troubleshoot an unstable or misleading result?
- Large variance between repeats: check thermal throttling, background processes, clock policy, and whether the measurement window is too short.
- Throughput stops scaling with concurrency: inspect queue depth, KV-cache exhaustion, synchronization time, and host-side request generation.
- TTFT rises immediately: check prompt length, prefill saturation, admission limits, and network or serialization overhead.
- ITL has periodic pauses: investigate GPU memory pressure, cache eviction, batch reshaping, and communication stalls.
- Per-GPU numbers look better than total service capacity: verify whether tokens were divided by the same interval and whether replicas were aggregated correctly.
- Offline and online results disagree: compare tokenization, streaming parsing, client concurrency, retries, queueing, and the exact measurement boundary.
- Output-token counts differ: check stop sequences, maximum-token settings, truncation, and tokenizer versions before comparing TPS.
Do not “fix” an outlier by silently deleting it. Record the symptom, diagnostic evidence, and reason for exclusion, then rerun the affected condition.
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Frequently Asked Questions
How many repetitions should an inference benchmark use?
Use enough independent windows to show that the median and tail metrics have stabilized, rather than choosing a universal run count. Report the individual runs or a confidence interval when the result will guide capacity purchases, and keep failed or thermally throttled runs visible in the log.
Should I benchmark streaming and non-streaming requests separately?
Yes. Streaming exposes TTFT and token cadence, while non-streaming clients may measure only completion time and can add buffering before the response is visible. Keep the protocol, chunk parsing, and timeout policy consistent within each test.
How do prompt-cache hits affect DGX or RTX throughput?
Cache hits reduce prefill work and can substantially lower TTFT, so they must be represented explicitly. Report cache-hit and cache-miss populations separately or disable reuse when the goal is a cold-prompt comparison.
Can I use requests per second to estimate user capacity directly?
Only after mapping the benchmark’s prompt and completion distributions to real user behavior. RPS is a workload-specific completion rate; session arrival patterns, think time, retries, and SLO limits determine how many concurrent users that rate can support.
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