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.
npxskills add zechenzhangagi/ai-research-skills--skill tensorrt-llmLoading…
NVIDIA's open-source library for optimizing LLM inference with state-of-the-art performance on NVIDIA GPUs.
Use TensorRT-LLM when:
Use vLLM instead when:
Use llama.cpp instead when:
# 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
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)
# 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
}'
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..."])
# Tensor parallelism across 8 GPUs
llm = LLM(
model="meta-llama/Meta-Llama-3-405B",
tensor_parallel_size=8,
dtype="fp8"
)
# 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
Meta Llama 3-8B (H100 GPU):
Llama 3-70B (8× A100 80GB):
Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
Set up and use 1Password CLI (op). Use when installing the CLI, enabling desktop app integration, signing in (single or multi-account), or reading/injecting/running secrets via op.
CLI to manage emails via IMAP/SMTP. Use `himalaya` to list, read, write, reply, forward, search, and organize emails from the terminal. Supports multiple accounts and message composition with MML (MIME Meta Language).
Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
Set up and use 1Password CLI (op). Use when installing the CLI, enabling desktop app integration, signing in (single or multi-account), or reading/injecting/running secrets via op.
CLI to manage emails via IMAP/SMTP. Use `himalaya` to list, read, write, reply, forward, search, and organize emails from the terminal. Supports multiple accounts and message composition with MML (MIME Meta Language).