tensorrt-llm

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

Agent operating procedure

1. Check the environment. Confirm hardware, framework and server versions, model format, and expected load.
2. Pin down the inputs. Confirm formats, identifiers and parameters from the data or the user. Ask rather than guess any value that changes the result.
3. Run a small version first. Serve or log a single request or run end to end before scaling.
4. Execute the full task using the instructions and references above.
5. Validate the result. Measure latency, throughput and output correctness against a reference; check resource usage and costs.
6. Report. State what was run (versions, commands, parameters), what was checked, and what is still uncertain.

If this happens	Do this
The server fails to start or OOMs	Check model size versus memory, quantization and parallelism settings.
A function, flag or endpoint in these instructions is missing in the installed version	Check the installed version's own documentation (help(), --help, official docs), adapt, and tell the user. Never invent an API.
A required input, identifier or parameter is ambiguous	Ask the user, or state the assumption explicitly before running.

Integrity rules

• Never fabricate results, parameters, identifiers, citations or statistics. If something cannot be run or verified, say so plainly.
• Do not expose services or credentials publicly; confirm cloud costs before provisioning.
• Treat version-specific details here as possibly outdated: confirm them against the official documentation for the installed version.
• Ask before actions that cost money, consume shared GPUs or cloud quota, touch personal or patient data, or cannot be undone.

Related skills

• serving-llms-vllm: Serves LLMs with high throughput using vLLM's PagedAttention and continuous batching.
• miles-rl-training: Provides guidance for enterprise-grade RL training using miles, a production-ready fork of slime.
• llama-cpp: Runs LLM inference on CPU, Apple Silicon, and consumer GPUs without NVIDIA hardware.