AGUASTO

Inference engine

TensorRT-LLM

Optimize and serve supported generative models using NVIDIA GPU kernels, cache management and parallel inference.

Original Aguasto conceptual illustration. Not manufacturer geometry, an operating Aguasto asset, or a measured benchmark.

When it is relevant

  • Evaluate an inference path on a specified NVIDIA GPU generation
  • Study supported lower-precision formats or distributed serving
NVIDIA TensorRT-LLM overview (external source)

Hardware and software assumptions

Documented ecosystem: NVIDIA. Platform support is not a guarantee that every checkpoint, optimization or feature works on every device.

  • Check the current model and GPU support matrix and the exact quantization format
  • The documented runtime has a PyTorch-native API; do not assume all versions require the same engine-build workflow
  • Aguasto has not evaluated or deployed it
NVIDIA TensorRT-LLM overview (external source)

Operating trade-offs

  • This profile targets NVIDIA GPUs; it is not a ROCm alternative
  • Precision and optimization support varies by model and GPU generation
NVIDIA TensorRT-LLM overview (external source)

Continue the research

Choosing an inference frameworkPrefill, decoding and disaggregationBenchmark methodologyModel referencesInfrastructure collaboration

Sources & verification

Reference revision 1 · Reviewed . Manufacturer claims and documentation are attributed; Aguasto has not run the cited products in production.

  1. NVIDIA TensorRT-LLM overview ↗

    NVIDIA GPU inference; PyTorch-native API; multi-GPU / multi-node inference; Dynamo and Triton integration; hardware-dependent FP4 / FP8 features. Publication: date not stated in the reviewed source. Verified: 2026-10-12.