vLLM vs. TensorRT-LLM: The Real-World Verdict
New benchmarks are challenging the conventional wisdom on inference frameworks. While TensorRT-LLM often wins on synthetic tests, vLLM's dynamic "continuous batching" frequently closes the gap on real-world, unpredictable enterprise workloads. One analyst noted that for spiky, mixed traffic typical in RAG systems, vLLM's adaptability can outweigh TensorRT's raw performance edge, potentially leading to lower overall costs when factoring in engineering overhead.
vLLM's core innovation, PagedAttention, was developed by researchers at UC Berkeley and is conceptually based on virtual memory and paging from traditional operating systems. This technique manages the memory-intensive key-value (KV) cache by breaking it into non-contiguous blocks, which can reduce memory waste by up to 96% and allow for more efficient memory sharing. By treating GPU memory like virtual memory, PagedAttention enables near-optimal memory utilization, with waste reported as low as 4%, compared to 60-80% in older systems. This efficiency gain allows for significantly larger batch sizes to be processed simultaneously, directly boosting GPU utilization and overall throughput. TensorRT-LLM, an open-source library from NVIDIA, implements its own version of this technique, calling it "in-flight batching," to continuously process new requests without waiting for the entire batch to finish. This is part of a broader suite of optimizations including custom attention kernels, graph rewriting, and aggressive quantization down to FP8, FP4, and INT4 to maximize performance specifically on NVIDIA hardware. While TensorRT-LLM often shows higher throughput in benchmarks with large batch sizes, vLLM can outperform it in scenarios with tight Time-Per-Output-Token (TPOT) constraints (e.g., under 20ms), which favor smaller batches. This makes vLLM a strong contender for latency-sensitive interactive applications. For ultra-low latency tasks with short inputs and outputs, however, TensorRT-LLM is often recommended if the model and hardware are supported. The choice between them often comes down to workload and ecosystem. TensorRT-LLM is deeply integrated with the NVIDIA stack, including Triton Inference Server, making it ideal for enterprises committed to that environment who need to extract maximum performance. In contrast, vLLM offers broader hardware support, including AMD GPUs, and easier integration with the Hugging Face ecosystem, appealing to those who prioritize flexibility and faster iteration.
