Mode d’emploi
- Copiez la commande et examinez le paquet avant de l’installer dans votre environnement agent.
- Consultez le document original pour les outils, identifiants et environnements requis.
- Décrivez les entrées et le livrable souhaité ; comparez la sortie aux sources.
npx skills add https://github.com/orchestra-research/ai-research-skills --skill tensorrt-llmAvant de commencer
- L’installation n’inclut ni abonnement au modèle, ni quota API, ni accès aux fichiers.
- Étoiles et installations reflètent l’activité, pas un test indépendant de qualité ou de sécurité.
Document Skill original
Rendu depuis le dépôt source en conservant titres, exemples, code, tableaux, liens et images.
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
bash
# 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 3.10-3.12Basic inference
python
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
bash
# 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)
python
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
python
# Tensor parallelism across 8 GPUs
llm = LLM(
model="meta-llama/Meta-Llama-3-405B",
tensor_parallel_size=8,
dtype="fp8"
)Batch inference
python
# 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 throughputPerformance 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](references/optimization.md) - Quantization, batching, KV cache tuning
- [Multi-GPU Setup](references/multi-gpu.md) - Tensor/pipeline parallelism, multi-node
- [Serving Guide](references/serving.md) - 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
