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Choosing Your Engine for LLM Inference: The Ultimate vLLM vs. TensorRT LLM Guide

This is the next blog in the series of blogs on LLMs and Generative AI. When deploying large language models (LLMs) for inference, it is critical to consider: efficiency, scalability, and performance. Users will likely be very familiar with two market leading options: vLLM and Nvidia's TensorRT LLM.

In this blog, we dive into their pros and cons, helping users select the most appropriate option for their use case.

vLLM vs TensorRT LLM

vLLM: Optimized for Flexibility and Scalability

vLLM is an open-source, high-performance inference engine designed to optimize large language model (LLM) serving with dynamic batching, efficient memory management, and seamless integration with popular frameworks. Let's discuss the Pros and Cons of vLLM.

Pros

#️⃣ 🌟 Feature 📝 Description
1 Dynamic Batching and Caching vLLM excels at dynamic batching, greatly reducing latency for real-time inference. Its smart caching boosts throughput for repeated requests.
2 Ease of Use and Integration Seamlessly integrates with popular frameworks like Hugging Face Transformers, enabling rapid development and deployment.
3 Cost Efficiency Optimizes GPU utilization and memory, helping organizations minimize infrastructure costs and making self-hosted inference more viable.
4 Horizontal Scalability Built-in support for horizontal scaling makes vLLM ideal for elastic, cloud-native deployments demanding high responsiveness.

Cons

#️⃣ 🚧 Limitation 📋 Description
1 Limited Optimization for Specific Hardware vLLM lacks hardware-specific deep optimizations compared to TensorRT LLM. This limits peak performance especially on specialized GPUs like NVIDIA's latest models.
2 Higher Latency for Large Inputs vLLM may experience latency spikes when processing very large inputs or during cold starts, potentially impacting real-time applications.

The popularity of vLLM and its simplicity is why Rafay uses vLLM as the engine in our Catalog. This offering from Rafay allows organizations to operationalize vLLM quickly and even provide their users with a self service experience.

Info

Watch a video of an end user launching a LLM Inference Endpoint via self service.

vLLM Template Catalog

TensorRT LLM: Maximized Performance Through Hardware Acceleration

TensorRT-LLM is an open-source library for optimizing Large Language Model (LLM) inference. It provides state-of-the-art optimizations, including custom attention kernels, inflight batching, paged KV caching, quantization, speculative decoding etc to ensure that inference performs efficiently on NVIDIA GPUs.

Pros

#️⃣ 🚀 Feature 🛠️ Description
1 Optimization TensorRT LLM delivers unparalleled performance on NVIDIA hardware through finely tuned optimizations, resulting in exceptionally low latency and high throughput for real-time inference.
2 Quantization TensorRT LLM supports advanced model quantization methods, significantly reducing memory consumption with minimal accuracy loss, allowing deployment of larger models on limited GPUs.
3 Performance Specifically designed for latency-critical environments, TensorRT LLM ensures consistently predictable, real-time performance vital for sectors like finance and interactive AI applications.

Cons

#️⃣ ⚠️ Limitation 🛠️ Description
1 Setup Achieving optimal performance with TensorRT LLM often requires extensive configuration, testing, and fine-tuning, adding overhead to development and deployment timelines.
2 Lock-in Deep integration with NVIDIA hardware limits flexibility and portability, increasing the risk of vendor lock-in across heterogeneous environments.
3 Batching TensorRT LLM performs best with static workloads but may struggle with highly dynamic scenarios where batch sizes vary significantly.

Conclusion

Choosing between vLLM and TensorRT LLM often depends on specific deployment requirements. Organizations must carefully weigh their performance needs, hardware ecosystem, and operational complexity when selecting the right inference tool.

  • vLLM provides flexibility, ease of integration, and scalability suitable for dynamic, evolving workloads.
  • TensorRT LLM is unmatched for hardware-specific acceleration, delivering superior latency and throughput for stable, high-performance demands.