Optimizes LLM inference with NVIDIA TensorRT for maximum throughput and lowest latency. Use for production deployment on NVIDIA GPUs (A100/H100), when you need 10-100x faster inference than PyTorch, o
Install
The skill file stays in the publisher's repository.
Paste the copied command in a terminal. It installs this SKILL.md into that agent.
npx skills add Orchestra-Research/AI-Research-SKILLs --skill tensorrt-llm -a cursor -y
Optimizes LLM inference with NVIDIA TensorRT for maximum throughput and lowest latency. Use for production deployment on NVIDIA GPUs (A100/H100), when you need 10-100x faster inference than PyTorch, or for serving models with quantization (FP8/INT4), in-flight batching, and multi-GPU scaling.
Questions
How do I install tensorrt-llm?
Download 12-inference-serving/tensorrt-llm/SKILL.md from the publisher, or copy the path and open the repository. PlugMCP lists the skill and does not host the file.
Where does tensorrt-llm live?
The publisher keeps it at https://github.com/Orchestra-Research/AI-Research-SKILLs.
Which agents can use tensorrt-llm?
Claude, Cursor, and Codex can read a SKILL.md you add from the publisher's repository.
Optimizes LLM inference with NVIDIA TensorRT for maximum throughput and lowest latency. Use for production deployment on NVIDIA GPUs (A100/H100), when you need 10-100x faster inference than PyTorch, or for serving models with quantization (FP8/INT4), in-flight batching, and multi-GPU scaling.
name: tensorrt-llm
description: Optimizes LLM inference with NVIDIA TensorRT for maximum throughput and lowest latency. Use for production deployment on NVIDIA GPUs (A100/H100), when you need 10-100x faster inference than PyTorch, or for serving models with quantization (FP8/INT4), in-flight batching, and multi-GPU scaling.
version: 1.0.0
author: Orchestra Research
license: MIT
tags: [Inference Serving, TensorRT-LLM, NVIDIA, Inference Optimization, High Throughput, Low Latency, Production, FP8, INT4, In-Flight Batching, Multi-GPU]
dependencies: [tensorrt-llm, torch]
TensorRT-LLM
NVIDIA's open-source library for optimizing LLM inference with state-of-the-art performance on NVIDIA GPUs.
When to use TensorRT-LLM
Use TensorRT-LLM when:
Deploying on NVIDIA GPUs (A100, H100, GB200)
Need maximum throughput (24,000+ tokens/sec on Llama 3)
In-flight batching: Dynamic batching during generation
Paged KV cache: Efficient memory management
Flash Attention: Optimized attention kernels
Quantization: FP8, INT4, FP4 for 2-4× faster inference
CUDA graphs: Reduced kernel launch overhead
Parallelism
Tensor parallelism (TP): Split model across GPUs
Pipeline parallelism (PP): Layer-wise distribution
Expert parallelism: For Mixture-of-Experts models
Multi-node: Scale beyond single machine
Advanced features
Speculative decoding: Faster generation with draft models
LoRA serving: Efficient multi-adapter deployment
Disaggregated serving: Separate prefill and generation
Common patterns
Quantized model (FP8)
from tensorrt_llm import LLM
# Load FP8 quantized model (2× faster, 50% memory)
llm = LLM(
model="meta-llama/Meta-Llama-3-70B",
dtype="fp8",
max_num_tokens=8192
)
# Inference same as before
outputs = llm.generate(["Summarize this article..."])
# Process 100 prompts efficiently
prompts = [f"Question {i}: ..." for i in range(100)]
outputs = llm.generate(
prompts,
sampling_params=SamplingParams(max_tokens=200)
)
# Automatic in-flight batching for maximum throughput
Download 12-inference-serving/tensorrt-llm/SKILL.md from the publisher, or copy the path and open the repository. PlugMCP lists the skill and does not host the file.
Where does tensorrt-llm live?
The publisher keeps it at https://github.com/Orchestra-Research/AI-Research-SKILLs.
Which agents can use tensorrt-llm?
Claude, Cursor, and Codex can read a SKILL.md you add from the publisher's repository.