Tensorrt Llm — Optimizes LLM inference with NVIDIA TensorRT for maximum throughput and lowest latency

Tensorrt Llm

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.

Skill metadata

Source	Optional — install with hermes skills install official/mlops/tensorrt-llm
Path	optional-skills/mlops/tensorrt-llm
Version	1.0.0
Author	Orchestra Research
License	MIT
Dependencies	tensorrt-llm, torch
Tags	Inference Serving, TensorRT-LLM, NVIDIA, Inference Optimization, High Throughput, Low Latency, Production, FP8, INT4, In-Flight Batching, Multi-GPU

Reference: full SKILL.md

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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