tensorrt-llm

par firecrawl

Optimise l'inférence des LLM avec NVIDIA TensorRT pour un débit

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

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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