Originally published on AI Tech Connect.
Why vLLM is still the operational default in 2026 vLLM is not the throughput leader anymore. PremAI's measurements on H100 put SGLang at roughly 16,200 tokens per second against vLLM's 12,500 on the smaller-model benchmarks that get quoted in launch posts. TensorRT-LLM goes further still when you accept the engine-rebuild tax. So why does vLLM remain the default that most teams in Bengaluru and London actually run in production? Because raw throughput is almost never the constraint that matters. What matters is the operator surface: how many models you can serve without a code change, how predictable scaling behaviour is, how good the documentation is when a junior engineer takes the pager at 2 a.m., and how easily the stack composes with the rest of your platform — ingress, autoscaler,…
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