TensorRT-LLM Leads in GPU Throughput
A comparative analysis of LLM inference tools for NVIDIA hardware points to TensorRT-LLM as the current leader for raw throughput. However, the analysis notes that open-source alternatives like vLLM and TGI remain highly competitive, with performance varying based on specific batch sizes and prompt complexity.
- TensorRT-LLM achieves its performance through ahead-of-time compilation, fusing CUDA kernels and optimizing computation graphs for specific NVIDIA GPU architectures like Hopper and Blackwell. This contrasts with vLLM, which primarily relies on runtime optimizations like its PagedAttention algorithm. - In benchmarks with short inputs and outputs, TensorRT-LLM has demonstrated up to 1.34 times higher throughput than vLLM, a figure that can increase to 2.72 times with long inputs and outputs. However, for scenarios with very small batch sizes under tight latency constraints (e.g., 20ms), vLLM can outperform TensorRT-LLM. - The library offers robust support for various quantization techniques, which are critical for reducing memory footprint and accelerating inference. It has first-class support for FP8 precision on H100 and newer GPUs, as well as INT8 with SmoothQuant and INT4 with Activation-aware Weight Quantization (AWQ). - Key features contributing to its speed include in-flight batching, which is more advanced than vLLM's continuous batching, and speculative decoding. It also utilizes a paged key-value (KV) cache to efficiently manage GPU memory during inference. - Operationally, using TensorRT-LLM requires a model compilation step where factors like GPU architecture, maximum batch size, and sequence lengths must be defined beforehand. This results in a longer initial setup compared to vLLM but can yield lower latency for the compiled configuration. - TensorRT-LLM is a core component of the NVIDIA ecosystem, designed for tight integration with tools like the Triton Inference Server for production deployments. This makes it a natural choice for teams already invested in NVIDIA's enterprise stack. - It supports a wide range of popular open-source models, including families like Llama, Mistral, Mixtral, Gemma, and Phi, as well as multi-modal models like LLaVA.
