There is no evidence-based universal winner among vLLM, SGLang, and TensorRT-LLM for high-concurrency AI agents. The right choice is the runtime and configuration that support your exact model and hardware while meeting your service’s latency and capacity objectives under realistic agent traffic. Define that workload first, then compare candidates under the same conditions.
Start with the service objective, not a peak tokens-per-second result
A maximum-throughput test answers how much work a system can push at saturation; it does not show whether users will get responses quickly at the arrival rates your product actually sees. Agent systems may combine short interactive requests with long prompts, extended generation, retries, streaming, or repeated context. Those patterns affect queueing and interference as well as model execution.
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Before choosing a runtime, define the service envelope you need to meet:
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- Latency: set targets for time to first token (TTFT), inter-token latency, end-to-end response time, and tail percentiles such as p95 or p99. A fast first token does not guarantee a fast completed response.
- Capacity: specify request rate and concurrent requests, including the point at which added load must be queued, throttled, or rejected.
- Traffic shape: describe prompt and output lengths, streaming behavior, burstiness, retries, and any mix of text and multimodal requests.
- Context reuse: include shared or repeated prefixes only if the production workload has them; otherwise a benchmark that relies on reuse can misrepresent capacity.
- Operational boundary: include the gateway, API, deployment topology, and resource limits that will sit around the inference server.
These are inputs to a fair comparison, not details to tune after a winner has been selected.
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What the documented tools let you compare
The projects expose different benchmark and serving capabilities. The following is a comparison of documented features, not a performance ranking; flags, endpoint compatibility, and supported deployment modes can change between releases.
| Candidate | Benchmark or serving details documented | What to verify for your deployment |
|---|---|---|
| vLLM | The benchmark CLI guide describes finite or infinite request rates, burstiness controls, and a maximum outstanding-request limit. It also covers workload patterns for throughput, realistic traffic, stress, latency profiling, capacity planning, and SLA validation. | Check the options against the exact vLLM release you will run: the cited CLI page follows the moving main branch. Pay attention to client-queue time and KV-cache capacity when interpreting load and throughput results. |
| SGLang | The serving benchmark guide describes streaming and non-streaming tests, rate control, concurrency limits, and measurements including TTFT, inter-token latency, throughput, and end-to-end latency. | Confirm current script and backend compatibility for your installed version. The guide lists endpoints for SGLang, vLLM, LMDeploy, and TensorRT-LLM, but endpoint support should not be assumed to remain unchanged. |
| TensorRT-LLM | NVIDIA documents an OpenAI-compatible API for trtllm-serve and online benchmarking in its benchmarking guide. The Triton backend guide describes GPU and multi-node modes, tensor, pipeline and expert parallelism, scheduler policies, and KV-cache options. |
Match the documented mode to your exact GPU count and topology; deployment modes have constraints. NVIDIA’s sample results are reference-only and hardware-dependent, so they are not portable evidence of a general lead. |
NVIDIA’s AIPerf server-metrics reference maps throughput and latency fields across vLLM, SGLang, TensorRT-LLM, Triton, and NVIDIA Dynamo. A shared collection tool can make measurement easier to align, but it does not make different instrumented points or metric formulas equivalent. Check what each reported field actually measures.
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Run a controlled benchmark that resembles agent traffic
- Freeze the workload. Use the same model weights, tokenizer, precision or quantization policy, prompt templates, sampling settings, and input/output token distributions for every candidate. Replay representative agent traces where possible. Enable prefix or KV-cache reuse only if that behavior exists in production.
- Freeze the system envelope. Record runtime and model-build versions, GPU type and count, interconnect and topology, parallelism, memory settings, serving API, and gateway limits. Keep these constant when comparing systems unless the configuration difference is itself the decision being tested.
- Separate cold and warm behavior. Measure startup and model loading separately from warmed request serving. Do not combine first-load costs with steady-state request results.
- Sweep realistic load before saturation. Test finite arrival rates at low and moderate load, then ramp toward the target concurrency and saturation. Reproduce production burstiness and queue or backpressure limits. Run a maximum-throughput test too, but report it as a separate capacity result rather than a proxy for production latency.
- Measure both client and server outcomes. Capture request rate, successful completions, generated tokens per second, TTFT, inter-token latency, end-to-end p50/p95/p99, queue time, errors and timeouts, GPU and memory use, and context or KV-cache occupancy when available. Metric names are not standardized: compare definitions and measurement points, not labels alone.
- Test interference. If production mixes short requests with long-context or multimodal work, run them together. Track the short request’s tail latency as well as aggregate throughput; a benchmark of isolated requests can conceal queueing effects.
- Repeat and disclose variation. Repeat runs, record warm or cold state and variance, and use enough observations to characterize tails. A small sample cannot support a dependable p99 estimate.
The vLLM guide describes load patterns and probe requests for checking how the main workload affects unrelated requests. SGLang documents streaming and non-streaming benchmark modes. NVIDIA documents online benchmark options for TensorRT-LLM. Use the current instructions for the specific releases under evaluation rather than assuming command flags or endpoint coverage are stable.
Choose the candidate that meets the whole deployment requirement
First remove any system that does not support the exact model, accelerator, topology, or integration you need. For the remaining candidates, compare the measured results at the same arrival rates and workload mix against your service objectives. A runtime that produces the most tokens at saturation is not automatically the best choice if it misses your latency target at normal load.
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Evaluate the remaining trade-offs in the context of the deployment:
- Model and hardware fit: confirm model compatibility, supported accelerator, GPU count, interconnect, and whether the required single-node or multi-node setup is documented.
- Memory and context behavior: assess KV-cache capacity and reuse, maximum context, and the effect of long-running or shared-context agent requests on concurrent capacity.
- Scheduling and overload behavior: inspect available scheduler controls and observe what happens to queue time, errors, and timeouts as load approaches the concurrency cap.
- Operations: verify API compatibility, observability, startup and warmup behavior, rollout and failure handling, and integration with your gateway and orchestration.
- Cost at the same objective: compare the hardware or hosted compute required to meet the same throughput and latency targets. The sources do not establish a universal cost winner; calculate using your actual deployment and pricing.
Make the decision from the configuration that passes the workload-specific test, not from a framework reputation or an unrelated benchmark. Keep the tested configuration and results together so the choice can be revisited when models, traffic, hardware, or runtime versions change.
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Make the result reproducible
A useful internal or published comparison lets another team understand what was actually tested. Report the model and workload distribution, arrival rates and concurrency, hardware and topology, runtime versions and configuration, warm or cold state, metric definitions, sample size, and run-to-run variation. Include the distinction between maximum-throughput results and production-like load results. Without those details, a ranking cannot reliably guide another deployment.
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