New Benchmarks for vLLM and TensorRT-LLM
A new video analysis benchmarks the performance of LLM inference frameworks vLLM and TensorRT-LLM on NVIDIA RTX 6000 GPUs. A separate guide offers practical advice for optimizing performance with vLLM, covering configuration, memory management, and resolving bottlenecks. These resources provide updated data on throughput and latency for enterprise-class hardware setups.
The core innovation in vLLM, developed at UC Berkeley, is PagedAttention, an algorithm inspired by virtual memory and paging in operating systems. This technique manages the memory-intensive key-value (KV) cache by breaking it into non-contiguous blocks, slashing memory waste by up to 96% and enabling much higher throughput compared to traditional methods. TensorRT-LLM is NVIDIA's open-source library for optimizing inference, built to extract maximum performance from its own hardware. It functions by compiling models into highly optimized CUDA kernels and using graph optimizations to fuse operations, which minimizes latency and is tightly integrated with the NVIDIA Triton Inference Server for production environments. The primary trade-off between the two frameworks often hinges on throughput versus latency. vLLM's continuous batching and efficient memory management typically give it an edge in high-concurrency scenarios, achieving greater overall throughput. In contrast, TensorRT-LLM is often superior for applications requiring the absolute lowest single-request latency, especially when workloads and hardware are stable and well-defined. For hardware-specific optimizations, TensorRT-LLM leverages the capabilities of newer architectures like Hopper and Ada Lovelace. This includes native support for FP8 precision, a feature of NVIDIA H100 GPUs that can double performance and halve memory usage compared to 16-bit precision with minimal impact on accuracy. While vLLM also supports FP8, TensorRT-LLM's integration is designed to exploit the underlying hardware directly. Both frameworks are architected differently to suit distinct operational needs. vLLM is often praised for its flexibility and ease of integration, especially with the Hugging Face ecosystem, making it ideal for rapid development and dynamic workloads. TensorRT-LLM, while potentially having a steeper setup curve due to its model compilation step, is tailored for mature, performance-critical enterprise environments deeply invested in the NVIDIA stack.
