vLLM vs SGLang vs TensorRT-LLM: Which to Choose in 2026

Q: Which is better in 2026, vLLM, SGLang or TensorRT-LLM?

None of them is better in general, and any article that names a single winner is describing one benchmark. vLLM is the correct default when you run mixed hardware, many models or traffic you cannot yet characterise, because it has the widest hardware list and model coverage. SGLang is the first candidate when your traffic re-sends long shared prefixes, as agents, tool loops, long system prompts, few-shot templates and RL rollouts do, because it was built around RadixAttention cache reuse. TensorRT-LLM fits a fleet committed to NVIDIA Hopper or Blackwell running a small set of models where a team will do per-model tuning. Decide by replaying a day of your own traffic on the candidates.

Q: What is the prefix-reuse ratio and why does it decide the engine?

It is the token-weighted share of each request's prompt that an earlier request inside your cache window has already computed: for each request, the longest previously seen prefix divided by the prompt length, averaged by tokens over a replayed day of real traffic. It decides the engine because a prefix-cache hit skips prefill for those tokens entirely, and no kernel optimisation can make recomputation cheaper than not computing. As a working heuristic from serving engagements, below about 0.2 the engines converge and you choose on operations and hardware, and above about 0.5 aggressive cache reuse dominates and SGLang, or vLLM with prefix caching tuned, should lead the shortlist.

Q: Is TensorRT-LLM still a compile-the-engine-and-serve-it-with-Triton workflow?

Not by default. NVIDIA's README now describes TensorRT-LLM as architected on PyTorch, with a high-level Python LLM API and its own trtllm-serve command that exposes an OpenAI-compatible server, alongside custom kernels for attention, GEMMs and mixture-of-experts, prefill-decode disaggregation and wide expert parallelism. Triton Inference Server remains a valid front end when you need its model repository and multi-framework serving, but it is no longer the required path. Our earlier Kubernetes deployment guide recommended the Triton route; this article corrects that recommendation, and a team building an engine-compile pipeline because an older guide said so may be adding a step it does not need.

Q: Does TensorRT-LLM send telemetry, and how do you turn it off?

Yes. Its README states that TensorRT-LLM collects anonymous telemetry by default and lists what is collected, such as the ingress point, environment details and selected configuration values like parallelism, dtype and KV cache settings, and what is not, including prompts, outputs, model weights, model paths and user-identifying information. Telemetry is automatically disabled in CI and test environments. To opt out, set TRTLLM_NO_USAGE_STATS=1, DO_NOT_TRACK=1 or TELEMETRY_DISABLED=true, create ~/.config/trtllm/do_not_track, pass TelemetryConfig(disabled=True) to LLM(), or use --no-telemetry on trtllm-serve, trtllm-bench or trtllm-eval. In regulated or air-gapped deployments, set the opt-out explicitly and block egress as well.

Q: Is the claim that SGLang is five times faster than vLLM still true?

Treat it as historical. LMSYS's January 2024 post introducing SGLang and RadixAttention reported up to five times higher throughput than existing systems including vLLM and Guidance on workloads with heavy KV-cache reuse. That was measured against the vLLM of that month, before vLLM shipped the automatic prefix caching its README now lists as a core feature. The gap on prefix-heavy traffic is therefore narrower than that figure implies, and how narrow depends on your traffic, model, hardware and both engines' current versions. Every vendor-published comparison is a statement about a competitor's version at a date; check the date and replay your own traffic before quoting it.

Q: Why does load balancing affect which inference engine performs best?

Because cache reuse only happens on the replica that holds the cached prefix. If a round-robin load balancer spreads requests that share a long system prompt across eight replicas, each replica sees roughly an eighth of the reuse, and an engine whose advantage is cache reuse looks no better than one without it. Prefix-aware routing, which sends requests sharing a prefix to the replica that already holds it, is part of the engine decision rather than a later optimisation. Applications must also keep prefixes stable: a timestamp, request ID or shuffled tool list at the top of the prompt turns a high reuse ratio into zero on every engine.

Q: What metrics should decide between inference engines?

Measure on a replay of your own traffic at your expected concurrency, not at saturation. The decisive metrics are time to first token at p95, output tokens per second per GPU, prefix-cache hit rate, queue depth under load, and cost per million output tokens at your real utilisation. Peak throughput at saturation, which most public benchmarks report, only matters if your fleet runs saturated, and most fleets spend much of the day well below it. Record the engine version, model, quantisation format and hardware with every result, because a benchmark without those four facts cannot be reproduced and will be wrong within a quarter given how often all three engines release.

Q: When should you not self-host vLLM, SGLang or TensorRT-LLM at all?

When your volume is low, roughly under a few million tokens a day, when the models you need are closed frontier models available only through an API, or when you lack the people to own GPU capacity, version pinning, upgrades and on-call for the serving layer. In those cases the engine comparison is irrelevant and a hosted API behind a gateway is cheaper and safer. Self-hosting starts to pay when volume is high and steady, when data residency or privacy rules out third-party processing, or when you need a fine-tuned open-weight model. Even then, start with one engine and one qualified fallback behind an OpenAI-compatible gateway so the choice stays reversible.

Satyam Kumar

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vLLM vs SGLang vs TensorRT-LLM: Your Prefix-Reuse Ratio Picks the Inference Server, Not the Leaderboard (2026)

By Satyam KumarOctober 10, 202616 min read

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vLLM vs SGLang vs TensorRT-LLM: Your Prefix-Reuse Ratio Picks the Inference Server, Not the Leaderboard (2026)

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Satyam Kumar

Founder & AI Architect, AppScale LLP

人工智能和云架构师。帮助团队构建可扩展到数百万的系统。

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vLLM vs SGLang vs TensorRT-LLM: Your Prefix-Reuse Ratio Picks the Inference Server, Not the Leaderboard (2026)

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