Tensorrt LLM Optimization Services
Packages & Pricing.

TensorRT LLM optimization for low-latency NVIDIA serving

TensorRT LLM optimization compiles your model into an engine tuned for NVIDIA GPUs, so each token arrives faster and each GPU serves more users. Omeiro builds the engine, applies in-flight batching and FP8 or INT8 precision, and serves it through Triton.

Benchmarks on your model show what changed.

TensorRT LLM optimization: what changes, and what to expect

Engines are built for a specific model, GPU type and set of settings. TensorRT LLM optimization therefore starts with the exact model version, precision target, maximum batch size and sequence length you expect, because the engine is compiled around them. Before ordering, tell us the GPUs, driver and CUDA environment, plus the latency or throughput target. A change of model or hardware later means a rebuild, which is why CI rebuilds exist on Premium.

Precision is the quality trade-off. FP8 and INT8 reduce memory and speed up generation, but outputs can change slightly, so TensorRT LLM optimization should be validated on your own prompts, not only on benchmark averages. In-flight batching raises throughput by mixing requests at different stages, which helps when users arrive constantly. Keep the original serving setup available for comparison, and agree acceptance numbers in advance so the benchmark gives a clear pass or fail.