What are the key configuration parameters for squeezing more performance out of TensorRT-LLM for inference?

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To optimize the performance of TensorRT-LLM for inference, there are several key configuration parameters and techniques you can leverage:
  1. Model Level Optimizations: TensorRT-LLM employs sophisticated model-level optimizations. It supports a variety of large language models (LLMs) such as Llama 1 and 2, ChatGLM, Falcon, MPT, Baichuan, and Starcoder.
  2. In-flight Batching and Paged Attention: These techniques are used to manage the execution of multiple requests concurrently, which can significantly improve the throughput of the system.
  3. Multi-GPU Multi-Node (MGMN) Inference: TensorRT-LLM supports multi-GPU and multi-node configurations, which can be used to distribute the inference workload across multiple GPUs or nodes, thereby improving the overall performance.
  4. NVIDIA Hopper Transformer Engine with FP8: This engine is designed to accelerate the execution of transformer-based models, which are commonly used in LLMs.
  5. Tensor Parallelism (TP): This is a technique for distributing the computation of a single layer across multiple GPUs. It can be particularly effective for large models that would otherwise not fit within the memory of a single GPU.
  6. Batch Size and Input/Output Length: The batch size and the length of the input and output sequences can have a significant impact on the performance of the system. Larger batch sizes can improve throughput but may increase latency. Similarly, shorter input/output sequences can reduce latency but may decrease throughput.
  7. Optimized Kernels: TensorRT-LLM comes packed with optimized kernels that can significantly improve the performance of LLM inference on NVIDIA GPUs
  8. Pre- and Post-Processing Steps: These steps, which include tasks such as sampling tokens from the model output and managing the KV cache, are highly optimized in TensorRT-LLM.
Remember, the optimal configuration can vary depending on the specific requirements of your application, such as the trade-off between latency and throughput, the size of the model, and the available hardware resources. Therefore, it's recommended to experiment with different configurations and measure their impact on performance.
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