No inference server wins for every high-concurrency agent workload. The published documentation for vLLM, SGLang and TensorRT-LLM does not support a universal ranking. The right choice depends on your model, accelerator and topology, prompt and output lengths, traffic shape, concurrency cap and latency objective. The reliable way to choose is to define your service objectives, shortlist the servers that support your model and hardware, and benchmark them under identical, production-like conditions. Then pick the configuration that meets your latency target at your required load. This guide walks through that process.
Why agent traffic needs its own selection criteria
A single peak tokens-per-second figure tells you how a server behaves when it is saturated with uniform requests. It does not tell you whether users will see a first token quickly at the arrival rate you expect. Agent products also tend to produce traffic that simple benchmarks miss:
- Many short, chained calls (planning, tool selection, summarisation) mixed with occasional long-context calls.
- Bursts when one user action fans out into several parallel model calls.
- Repeated or shared context such as system prompts, tool definitions and growing conversation history, which makes cache behaviour matter.
- Streaming, where first-token and per-token delays are what a person or a downstream tool actually waits on.
These are properties of typical agent designs, not measurements of any particular server. Your own traces decide which ones apply to you.
Step 1: Write down the service objectives first
Before comparing software, fix the targets a candidate must hit:
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- The latency metrics that matter (time to first token, inter-token latency, end-to-end latency) and the percentile you commit to, such as p95 or p99.
- The request rate and the maximum number of simultaneous requests you must sustain.
- Acceptable error and timeout rates, and what should happen when the system is full (queue, shed or reject).
- The budget: the hardware or hosted compute you can spend to meet those targets.
A server that is faster at maximum load but misses your p99 first-token target at your real arrival rate has failed. A slower one that meets it has passed.
Step 2: Shortlist by fit, not speed
Eliminate candidates that cannot run your deployment before measuring anything. Check the exact versions you would run for each of these axes.
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| Axis | What to verify |
|---|---|
| Model compatibility | Your exact model architecture, precision and quantisation are supported in the release you will deploy. |
| Hardware and topology | Supported accelerator, GPU count, interconnect, and whether you need tensor, pipeline or expert parallelism across one node or several. |
| Memory and context | KV-cache capacity, whether cache is reused across requests that share a prefix, and the maximum context you need. |
| Scheduling | Available scheduler policies and controls over how requests are queued and batched. |
| API and integration | Whether the serving API fits your gateway, SDKs and orchestration layer, including streaming. |
| Operations | Observability, startup and warm-up time, model rollout, and behaviour on failure. |
| Cost | Hardware needed to meet the same objectives. No reviewed source establishes a universal cost winner, so calculate against your own pricing. |
What the vendor documentation tells you about each option
- vLLM ships a benchmarking CLI that supports finite or unlimited request rates, burstiness control and a cap on outstanding requests. Its guide also explains client-queue time and KV-cache capacity when you interpret results. These flags are documented on a moving main-branch page, so confirm them against the release you install. vLLM Benchmarking CLI
- SGLang provides a serving benchmark script with streaming and non-streaming modes, rate control and concurrency limits. It reports TTFT, inter-token latency, throughput and end-to-end latency. The same script lists endpoint support for SGLang, vLLM, LMDeploy and TensorRT-LLM, so one client can drive several servers. The surfaced guide may be older than the current release, so check compatibility. SGLang Bench Serving Guide
- TensorRT-LLM can be served through an OpenAI-compatible API with
trtllm-serve, with online benchmark options in NVIDIA’s tooling. TensorRT-LLM Benchmarking The Triton backend guide describes GPU and multi-node deployment modes, tensor, pipeline and expert parallelism, scheduler policies and KV-cache options. Some modes carry constraints, so read the guide for your target topology. NVIDIA TensorRT-LLM Backend for Triton
Treat sample numbers in vendor pages as illustrations. NVIDIA’s Triton backend page, for example, shows benchmark output but labels it reference-only and says results depend on the GPU. Do not reuse such figures as a comparison between servers.
Step 3: Build a workload that looks like production
The most common benchmarking error is testing a clean synthetic workload. Hold these constant across every candidate:
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- The same model weights, tokenizer, precision or quantisation policy, prompt templates and sampling settings.
- The same distribution of input and output lengths, taken from real agent traces where you can.
- The same arrival pattern: a finite request rate with realistic burstiness, plus a concurrency cap that matches your gateway.
- Repeated prefixes or shared context only if production has them. Including them when you do not (or leaving them out when you do) will skew any server that reuses cache.
Record the system envelope too: exact runtime versions, model build, GPU type and count, topology, parallelism settings, memory settings, serving API and any gateway limits. Without these a result cannot be reproduced.
Step 4: Run the benchmark
- Separate cold from warm. Measure model loading and startup on their own. Warm each server before collecting request-serving data.
- Use enough requests. A small sample cannot characterise tails, so do not quote a p99 from a handful of requests.
- Sweep the load. Start at low and moderate finite rates, then ramp toward your target concurrency and on to saturation. Use the benchmark tool’s request-rate, burstiness and maximum-concurrency controls (the vLLM and SGLang guides both document equivalents; check the flag names in your installed version’s help output).
- Include a maximum-throughput run, but report it separately. It shows capacity limits. It does not show production-like latency.
- Capture both client and server views. Record request rate, successful completions, output tokens per second, TTFT, inter-token latency, end-to-end p50, p95 and p99, queue time, errors and timeouts, GPU and memory use, and KV-cache occupancy where the server exposes it.
- Probe interference (next section).
- Repeat the runs. Report variance and warm or cold state, then publish the full workload and configuration.
Probe for interference between requests
Agents often mix short control calls with long-context or multimodal calls. A server can post excellent aggregate throughput while the short calls wait behind heavy ones. The vLLM guide describes probe requests for this: run a lightweight request stream alongside the main workload and record the probe’s tail latency, not just the main workload’s numbers. Do this whenever your production traffic mixes request types.
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Test the full path, not just the model core
Include queueing and backpressure behaviour in the test. What happens when the cap is reached, whether clients time out, and how quickly the server recovers all affect an agent loop that retries or fans out. Testing only an isolated model core hides these effects.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Step 5: Compare metrics on the same definitions
Metric names are not standardised across tools, and the vLLM guide says so explicitly. Two tools can both report “throughput” or “latency” while counting different things or measuring at different points. Check the formula and measurement point for each figure.
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| Measure | What it answers | Watch for |
|---|---|---|
| Requests per second | How many completed requests the system handles | Only meaningful if request lengths are comparable between runs |
| Output tokens per second | Generation capacity | Can rise at the cost of latency; never read it alone |
| TTFT | Time until the first streamed token | Includes queueing; sensitive to prompt length and interference |
| Inter-token latency | Smoothness of streaming and tool-call generation | Check whether it is averaged or per token, and whether the first token is excluded |
| End-to-end latency | Total wait for a complete response | Depends heavily on output length, so compare at matched lengths |
| p95 / p99 tails | What the unlucky requests experience | Needs a large sample |
If you want one collection layer for several servers, NVIDIA’s AIPerf documents throughput and latency fields for vLLM, SGLang, TensorRT-LLM, Triton and NVIDIA Dynamo. That helps map metric names, but it does not make unlike setups equivalent. Field semantics, instrumented points and backend coverage still need checking. NVIDIA AIPerf Server Metrics Reference
Step 6: Decide
Apply the decision in this order:
- Drop any candidate that cannot run your model, precision, accelerator or topology.
- Drop any candidate that misses your latency percentile at your target rate and concurrency, even if its peak throughput is highest.
- Among the survivors, prefer the one that meets the objectives with the least hardware. Where the cost is close, let operational fit break the tie: API compatibility with your gateway, observability, rollout and failure behaviour, and scheduler controls.
- Keep a margin. Choose the configuration that holds the target with headroom above your expected peak, because agent fan-out makes bursts larger than average rates suggest.
If two servers tie on your own tests, the tie is real. Choose the one your team can operate more confidently.
Quick Recap
Common mistakes to avoid
- Choosing from a blog or vendor chart produced on different hardware, models, lengths or software versions.
- Comparing tokens per second without matching TTFT, tail latency and error rates.
- Running only an unlimited-rate test and treating it as a capacity plan.
- Ignoring cold-start and warm-up when your deployment scales up or rolls models often.
- Pinning conclusions to a version. Every flag and feature here is version-sensitive, so repeat the test after upgrades.
- Publishing a result without the workload, hardware and version details needed for someone else to reproduce it.
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