tensorrt-llm

Q: How do I install tensorrt-llm?

Run npx skills add https://github.com/orchestra-research/ai-research-skills --skill tensorrt-llm in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/tensorrt-llm/ (personal) or .claude/skills/tensorrt-llm/ (project).

by orchestra-researchAI & Agents

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.

Installs: 1K
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Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/orchestra-research/ai-research-skills --skill tensorrt-llm

01
Install
Run the command in your project. The CLI asks which agents to install for.
02
Describe the task
Ask your agent for something this skill covers. It loads tensorrt-llm automatically when relevant.
03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

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)
Require low latency for real-time applications
Working with quantized models (FP8, INT4, FP4)
Scaling across multiple GPUs or nodes

Use vLLM instead when:

Need simpler setup and Python-first API
Want PagedAttention without TensorRT compilation
Working with AMD GPUs or non-NVIDIA hardware

Use llama.cpp instead when:

Deploying on CPU or Apple Silicon
Need edge deployment without NVIDIA GPUs
Want simpler GGUF quantization format

Quick start

Installation

# Docker (recommended)
docker pull nvidia/tensorrt_llm:latest

# pip install
pip install tensorrt_llm==1.2.0rc3

# Requires CUDA 13.0.0, TensorRT 10.13.2, Python 3.10-3.12

Basic inference

from tensorrt_llm import LLM, SamplingParams

# Initialize model
llm = LLM(model="meta-llama/Meta-Llama-3-8B")

# Configure sampling
sampling_params = SamplingParams(
    max_tokens=100,
    temperature=0.7,
    top_p=0.9
)

# Generate
prompts = ["Explain quantum computing"]
outputs = llm.generate(prompts, sampling_params)

for output in outputs:
    print(output.text)

Serving with trtllm-serve

# Start server (automatic model download and compilation)
trtllm-serve meta-llama/Meta-Llama-3-8B \
    --tp_size 4 \              # Tensor parallelism (4 GPUs)
    --max_batch_size 256 \
    --max_num_tokens 4096

# Client request
curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "meta-llama/Meta-Llama-3-8B",
    "messages": [{"role": "user", "content": "Hello!"}],
    "temperature": 0.7,
    "max_tokens": 100
  }'

Key features

Performance optimizations

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..."])

Multi-GPU deployment

# Tensor parallelism across 8 GPUs
llm = LLM(
    model="meta-llama/Meta-Llama-3-405B",
    tensor_parallel_size=8,
    dtype="fp8"
)

Batch inference

# 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

Performance benchmarks

Meta Llama 3-8B (H100 GPU):

Throughput: 24,000 tokens/sec
Latency: ~10ms per token
vs PyTorch: 100× faster

Llama 3-70B (8× A100 80GB):

FP8 quantization: 2× faster than FP16
Memory: 50% reduction with FP8

Supported models

LLaMA family: Llama 2, Llama 3, CodeLlama
GPT family: GPT-2, GPT-J, GPT-NeoX
Qwen: Qwen, Qwen2, QwQ
DeepSeek: DeepSeek-V2, DeepSeek-V3
Mixtral: Mixtral-8x7B, Mixtral-8x22B
Vision: LLaVA, Phi-3-vision
100+ models on HuggingFace

References

Optimization Guide - Quantization, batching, KV cache tuning
Multi-GPU Setup - Tensor/pipeline parallelism, multi-node
Serving Guide - Production deployment, monitoring, autoscaling

Resources

Docs: https://nvidia.github.io/TensorRT-LLM/
GitHub: https://github.com/NVIDIA/TensorRT-LLM
Models: https://huggingface.co/models?library=tensorrt_llm

FAQ

What does the tensorrt-llm skill do?

How do I install tensorrt-llm?

Run npx skills add https://github.com/orchestra-research/ai-research-skills --skill tensorrt-llm in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/tensorrt-llm/ (personal) or .claude/skills/tensorrt-llm/ (project).

Which AI agents can use tensorrt-llm?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is tensorrt-llm safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains tensorrt-llm?

It is published by orchestra-research in orchestra-research/ai-research-skills. It has 1,041 recorded installs.