--- title: "TensorRT-LLM | Aguasto" description: "Technical reference for TensorRT-LLM: Optimize and serve supported generative models using NVIDIA GPU kernels, cache management and parallel inference. Hardware assumptions and operating trade-offs are documented." canonical: "https://www.aguasto.com/inference-stack/tensorrt-llm" last-updated: "2026-10-12" --- # TensorRT-LLM | Aguasto Canonical HTML: [TensorRT-LLM](https://www.aguasto.com/inference-stack/tensorrt-llm) > Technical reference for TensorRT-LLM: Optimize and serve supported generative models using NVIDIA GPU kernels, cache management and parallel inference. Hardware assumptions and operating trade-offs are documented. [Aguasto](https://www.aguasto.com/) [Inference Stack](https://www.aguasto.com/inference-stack) TensorRT-LLM [Resources](https://www.aguasto.com/resources) [GPU Library](https://www.aguasto.com/gpus) [Model Index](https://www.aguasto.com/models) [Inference Stack](https://www.aguasto.com/inference-stack) [Learn](https://www.aguasto.com/learn) [Insights](https://www.aguasto.com/insights) [Research](https://www.aguasto.com/research) [Engineering tools](https://www.aguasto.com/tools) 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. Aguasto Research documents third-party hardware, models and software. References do not represent Aguasto inventory, evaluated deployments or available inference services. ### 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)](https://nvidia.github.io/TensorRT-LLM/overview.html) ### 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)](https://nvidia.github.io/TensorRT-LLM/overview.html) ### 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)](https://nvidia.github.io/TensorRT-LLM/overview.html) ### Continue the research [Choosing an inference framework](https://www.aguasto.com/learn/choosing-an-inference-serving-framework) [Prefill, decoding and disaggregation](https://www.aguasto.com/learn/prefill-decode-and-disaggregated-serving) [Benchmark methodology](https://www.aguasto.com/research/inference-benchmark-methodology) [Model references](https://www.aguasto.com/models) [Infrastructure collaboration](https://www.aguasto.com/partners) ### Sources & verification Reference revision 1 · Reviewed October 12, 2026. Manufacturer claims and documentation are attributed; Aguasto has not run the cited products in production. - [NVIDIA TensorRT-LLM overview ↗](https://nvidia.github.io/TensorRT-LLM/overview.html) 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. ### Reference journey [Browse the stack ↗](https://www.aguasto.com/inference-stack) [Model Index ↗](https://www.aguasto.com/models) [GPU Library ↗](https://www.aguasto.com/gpus) [Sources & verification ↓](https://www.aguasto.com/inference-stack/tensorrt-llm#sources) Research reference · No available Aguasto service ## Company navigation - [Home](https://www.aguasto.com/) - [Infrastructure](https://www.aguasto.com/infrastructure) - [GPU Cloud](https://www.aguasto.com/gpu-cloud) - [AI Inference](https://www.aguasto.com/ai-inference) - [Development & Investment](https://www.aguasto.com/development) - [Company](https://www.aguasto.com/company) - [Contact](https://www.aguasto.com/contact) - [Privacy](https://www.aguasto.com/privacy) - [Resources](https://www.aguasto.com/resources) - [GPU Library](https://www.aguasto.com/gpus) - [Model Index](https://www.aguasto.com/models) - [Inference Stack](https://www.aguasto.com/inference-stack) - [Learn](https://www.aguasto.com/learn) - [Insights](https://www.aguasto.com/insights) - [Research](https://www.aguasto.com/research) - [Industry cases](https://www.aguasto.com/research/cases) - [Why Modular](https://www.aguasto.com/why-modular) - [Partners](https://www.aguasto.com/partners) - [GPU comparison](https://www.aguasto.com/gpus/compare) - [Model comparison](https://www.aguasto.com/models/compare) - [Engineering tools](https://www.aguasto.com/tools) - [Development friction](https://www.aguasto.com/research/hyperscale-development-friction) - [NVIDIA H100 SXM](https://www.aguasto.com/gpus/h100) - [NVIDIA H200 SXM](https://www.aguasto.com/gpus/h200) - [NVIDIA B200 SXM](https://www.aguasto.com/gpus/b200) - [NVIDIA B300](https://www.aguasto.com/gpus/b300) - [NVIDIA GB200 NVL72](https://www.aguasto.com/gpus/gb200-nvl72) - [NVIDIA GB300 NVL72](https://www.aguasto.com/gpus/gb300-nvl72) - [NVIDIA Vera Rubin NVL72](https://www.aguasto.com/gpus/vera-rubin-nvl72) - [AMD Instinct MI300X](https://www.aguasto.com/gpus/mi300x) - [AMD Instinct MI325X](https://www.aguasto.com/gpus/mi325x) - [AMD Instinct MI355X](https://www.aguasto.com/gpus/mi355x) - [AMD Instinct MI455X](https://www.aguasto.com/gpus/mi455x) - [AMD Helios](https://www.aguasto.com/gpus/helios) - [GLM-4.5](https://www.aguasto.com/models/glm-4-5) - [Qwen3-235B-A22B](https://www.aguasto.com/models/qwen3-235b-a22b) - [DeepSeek-R1](https://www.aguasto.com/models/deepseek-r1) - [Llama 3.3 70B Instruct](https://www.aguasto.com/models/llama-3-3-70b-instruct) - [Gemma 3 27B IT](https://www.aguasto.com/models/gemma-3-27b-it) - [gpt-oss-120b](https://www.aguasto.com/models/gpt-oss-120b) - [Kimi-K2-Instruct](https://www.aguasto.com/models/kimi-k2-instruct) - [vLLM](https://www.aguasto.com/inference-stack/vllm) - [SGLang](https://www.aguasto.com/inference-stack/sglang) - [TensorRT-LLM](https://www.aguasto.com/inference-stack/tensorrt-llm) - [NVIDIA Dynamo](https://www.aguasto.com/inference-stack/dynamo) - [NVIDIA Triton Inference Server](https://www.aguasto.com/inference-stack/triton) - [AMD ROCm](https://www.aguasto.com/inference-stack/rocm) - [Ray Serve](https://www.aguasto.com/inference-stack/ray-serve) - [Kubernetes GPU orchestration — NVIDIA device plugin](https://www.aguasto.com/inference-stack/kubernetes) - [Model weights are only the beginning of the memory budget](https://www.aguasto.com/learn/model-weights-and-runtime-memory) - [Why tokens per second is not enough to benchmark inference](https://www.aguasto.com/research/inference-benchmark-methodology) - [Prefill, decode and the cost of separating them](https://www.aguasto.com/learn/prefill-decode-and-disaggregated-serving) - [Choosing between vLLM, SGLang and TensorRT-LLM](https://www.aguasto.com/learn/choosing-an-inference-serving-framework) - [From Power to Inference—Building a Distributed AI Compute Network](https://www.aguasto.com/learn/from-power-to-inference) - [Dedicated compute and shared inference: compare the whole workload](https://www.aguasto.com/insights/dedicated-and-shared-inference-economics) - [Distributed inference reliability begins with the failure boundary](https://www.aguasto.com/learn/distributed-inference-reliability) - [Core Scientific: reading contracted, energized and billed capacity](https://www.aguasto.com/research/cases/core-scientific-capacity-disclosures) - [Why Modular AI Infrastructure Matters Now](https://www.aguasto.com/research/why-modular-ai-infrastructure-matters-now) - [Official LinkedIn](https://www.linkedin.com/company/aguasto) This is a generated representation of the public HTML page. Diagrams are conceptual. GPU and inference services are planned and are not currently available to book or call. The contact page describes the available inquiry flow.