Choose an engine by measured fit

Picture it

An inference engine isn't just a raw motor; it's the entire transmission, suspension, and steering system wrapped around the model. Two cars might boast the same horsepower on paper, but one is tuned for smooth highway cruising while the other excels at stop-and-go city traffic. Choosing the right engine means aligning the software's deep internal choreography of kernels and caches with the unique rhythm of your specific workload.

What to understand

• vLLM, SGLang, TensorRT LLM, and local runtimes
• Model/backend compatibility, attention kernels, compile/warmup costs, APIs, and reproducibility

Follow the references

1. 1 · Documentation

   NVIDIA · TensorRT LLM overview ↗

   Inspect architecture and supported optimization features; note model and hardware requirements.

2. 2 · Documentation

   vLLM · Optimization and tuning ↗

   Use the same workload to compare engine behavior, with supported settings and explicit versions.

3. 3 · Documentation

   SGLang · Serving documentation ↗

   Check supported models and serving features before making a like-for-like comparison.

Make it concrete

Build a two-engine comparison table: model compatibility, dtype, template, quality, startup time, memory, latency SLOs, goodput, failure behavior, and cost. Explain every intentional difference.