LLM Serving Frameworks: vLLM vs. TensorRT-LLM
A comparison of leading AI serving frameworks highlights the trade-offs between vLLM and TensorRT-LLM for production workloads. vLLM is noted for its ease of use and rapid integration with HuggingFace models, making it ideal for prototyping. In contrast, TensorRT-LLM is positioned for production, with kernel-level optimizations enabling sub-100ms latency for models like Llama 3 on A100 and H100 GPUs.
- The core innovation of vLLM, developed at UC Berkeley, is PagedAttention, which treats the KV cache like virtual memory in an operating system. This partitions the cache into blocks, reducing memory waste by up to 96% and allowing for more efficient memory sharing between requests. - TensorRT-LLM implements "in-flight batching," also known as continuous batching, which dynamically batches incoming requests. This avoids waiting for a full batch to complete, improving GPU utilization and reducing latency. vLLM's scheduler separates prefill and decode requests into different batches by default, while TensorRT-LLM can mix them. - For quantization, TensorRT-LLM supports FP8, FP4, and INT4 with Activation-aware Weight Quantization (AWQ), while vLLM offers flexibility with GPTQ, AWQ, and various integer quantization formats. On some workloads, INT4 weight-only quantization can double throughput for both frameworks compared to FP16. - While vLLM supports a broader range of hardware, including AMD and AWS accelerators, TensorRT-LLM is exclusively optimized for NVIDIA GPUs to extract maximum performance. TensorRT-LLM integrates with the NVIDIA Triton Inference Server for production deployments, providing features like dynamic batching and multi-model serving. - The vLLM project is now incubated by the LF AI & Data Foundation to ensure open and transparent governance, preventing control by any single entity. Its roadmap includes disaggregated serving—separating prompt processing and token generation onto different GPUs—and multi-node inference for extremely large models. - TensorRT-LLM allows for custom CUDA plugins to extend its functionality with user-defined kernels for specific optimizations not covered by the standard library. It is architected on PyTorch, providing a high-level Python API that integrates with tools like NVIDIA Dynamo.
