---
name: tensorrt-llm-7b914ffe6b4f
description: 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.
version: 1.0.0
author: Orchestra Research
license: MIT
dependencies: [tensorrt-llm, torch]
platforms: [linux, macos]
metadata:
  hermes:
    tags: [Inference Serving, TensorRT-LLM, NVIDIA, Inference Optimization, High Throughput, Low Latency, Production, FP8, INT4, In-Flight Batching, Multi-GPU]

---

# 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 3.10-3.12
```

### Basic 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 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](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




---
Source attribution: aniruddhaadak80
Original project: https://github.com/aniruddhaadak80/herclew/blob/080fb1e18bb9513027be097e3cb530e6c5c636cf/core-agent/optional-skills/mlops/tensorrt-llm/SKILL.md
License: MIT
License information: https://opensource.org/license/mit
Original upstream instructions; downloaded from Mega AI Hub.

## Upstream license notice

MIT License

Copyright (c) 2025 Nous Research

Permission is hereby granted, free of charge, to any person obtaining a copy
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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SOFTWARE.



# Mega AI Hub resource metadata

Creator / publisher: aniruddhaadak80
Original project: https://github.com/aniruddhaadak80/herclew/blob/080fb1e18bb9513027be097e3cb530e6c5c636cf/core-agent/optional-skills/mlops/tensorrt-llm/SKILL.md
Attribution basis: Source repository record

Fields of work: Data & Analytics, Development & Engineering
Roles: Backend Developer, Data Scientist, ML / AI Engineer
Specialties: API, Backend
Tools: Docker
Country applicability: Country not specified
Classification basis: Source catalog labels
Instruction SHA-256: cdf911b1538cea5d1c87c0b5a9e7dc3ccf1173bfb6468cb4f34aaa28524a9d1f