TensorRT-LLM Architecture and Process

TensorRT-LLM is a framework designed for optimising and deploying large language models on NVIDIA GPUs.

It encompasses various components and stages, from model definition to efficient execution on hardware.

Purpose and Scope

Optimised Inference for LLMs

TensorRT-LLM is tailored for efficient inference of large language models, utilising NVIDIA's TensorRT for GPU optimisation.

Multi-GPU and Multi-Node Support

It caters to large-scale deployments, supporting both multi-GPU and multi-node configurations, crucial for handling the computational demands of large language models.

Model Definition and Training

Users can define their own models or choose from pre-defined architectures supported by TensorRT-LLM.

While TensorRT-LLM focuses on inference, the models themselves need to be trained using other frameworks like NVIDIA Nemo or PyTorch. Pre-trained model checkpoints can also be sourced from various providers, including HuggingFace.

Compilation with TensorRT

The framework provides a Python API to recreate models in a format that can be compiled by TensorRT into an efficient engine. This step involves translating the high-level model architecture into a representation optimised for GPU execution.

The model is then compiled into a TensorRT engine, which is an optimised version of the model specifically designed for fast inference on NVIDIA GPUs.

Runtime Execution

TensorRT-LLM includes components to create a runtime environment that can execute the optimised TensorRT engine.

Advanced functionalities like beam-search, top-K, and top-P sampling are available, which are important for tasks like text generation where different strategies for selecting the next word in a sequence are needed.

C++ Runtime: While there's Python support, the C++ runtime is recommended for performance reasons.

Integration with Triton Inference Server

Backends for Triton: The toolkit includes Python and C++ backends for integration with the NVIDIA Triton Inference Server, facilitating the deployment of LLMs as web-based services.

In-Flight Batching: Particularly in the C++ backend, TensorRT-LLM implements in-flight batching (grouping multiple inference requests together), improving throughput and efficiency.

Usage and Practical Implications

Deployment of LLMs: TensorRT-LLM streamlines the process of deploying large language models, particularly in web-based or cloud environments.

Optimisation for NVIDIA Hardware: The toolkit is specifically designed to leverage NVIDIA GPUs, making it suitable for environments where such hardware is available.

Flexibility and Advanced Features: It offers flexibility in terms of model choice and advanced features for runtime execution, catering to a range of use cases from simple language understanding to complex text generation.

Conclusion

TensorRT-LLM is a robust framework that bridges the gap between the development of large language models and their efficient deployment on NVIDIA GPUs.

It addresses the end-to-end workflow from model definition and GPU optimization to runtime execution and web service deployment, focusing on performance and scalability.