Introduction
TensorRT-LLM is NVIDIA’s open-source toolbox for optimizing large language model inference, designed for high-performance GPU serving and enterprise deployment. It supports mainstream models and advanced quantization techniques.
Key Features
- Custom attention kernels, batch inference, distributed parallelism, and multiple quantization methods (FP8/FP4/INT4/INT8)
- High-level Python API for single-GPU, multi-GPU, and multi-node deployment
- Seamless integration with Triton Inference Server, PyTorch, and other ecosystems
- Modular architecture, easy to extend and customize
Use Cases
- Enterprise-scale LLM inference and deployment
- Efficient GPU inference in cloud and on-premises
- Rapid prototyping for LLM applications
- Quantized model performance optimization
Technical Highlights
- C++/Python/CUDA multi-language collaboration, extreme performance optimization
- Built-in KV cache, inference scheduling, structured output, and other advanced features
- Supports mainstream LLMs and quantized models, easy integration of new models