What is vLLM, and How Does It Achieve Fast Inference?

Large language models are powerful tools for generating text closely resembling human writing. Running them for inference, especially at scale, can be challenging. Optimized versions of popular models like GPT-3 take up multiple gigabytes of memory for a single instance. If you want to run these models locally or even on a cloud instance, you may need to worry about memory bottlenecks, long loading times, and higher-than-expected costs. As this article will show, if you want to achieve ultra-fast, cost-efficient LLM inference engine at scale without memory bottlenecks or performance trade-offs, you must look beyond traditional inference engines to cutting-edge solutions like vLLM. O

ne such solution is AI inference APIs like those offered by Inference. These powerful tools can help you achieve the low-latency, cost-effective LLM inference you need to deploy your next-generation large language model successfully.

What is vLLM, and What Is the Core Idea Behind It?

vLLM, or virtualized large language models, is an open-source library designed to enhance the efficiency of large language model inference and serving. It introduces PagedAttention, an innovative attention algorithm that optimizes memory management by allowing non-contiguous storage of attention keys and values, significantly reducing memory waste. This optimization leads to state-of-the-art serving throughput, achieving up to 24 times higher than traditional methods.

vLLM supports continuous batching of incoming requests and seamless integration with popular Hugging Face models, making it a flexible and high-performance solution for deploying LLMs in various applications.

Key Features of vLLM

Let’s examine what distinguishes vLLM from the competition and why it is one of the most important tools in the broad landscape of modern machine learning.

Multi-GPU Support

It’s crucial for LLMs like ChatGPT, where high-performance requirements are a must, that multi-GPU setups work efficiently and provide decent parallel processing of model computations.

Thanks to the vLLM multi-GPU architecture, workloads are effectively distributed, reducing bottlenecks and increasing the system’s overall throughput. vLLM has native multi-GPU setup support, which helps with optimization.

Continuous Batching

Most traditional systems require fixed batch sizes. vLLM eliminates this archaic need and allows continuous batching. This enables dynamic task distribution, allowing for better resource management and efficiency.

Continuous batching is incredibly useful in environments with typical fluctuating workloads. As it continuously manages input streams, vLLM minimizes idle time, making it an almost necessary component for real-time AI applications.

Speculative Decoding for Speed

Chatbots and real-time text generators are incredibly latency-sensitive, and vLLM optimizes this by generating potential future tokens preemptively and validating them simultaneously. This reduces and optimizes the inference time of LLMs through its speculative decoding approach. It’s one of the many standout innovations of vLLM.

Optimized Memory Usage with Embedding Layers

Scaling LLMs need excellent efficiency when it comes to GPU memory. vLLM embedding layers are highly optimized for memory efficiency, ensuring that the LLM utilizes GPU memory in a balanced and effective manner. This solves the memory resource issue that most LLMs like ChatGPT have without sacrificing performance.

LLM Adapters

vLLM supports the integration of LLM adapters, allowing devs to fine-tune and customize LLMs without retraining whole systems. Modular approaches are gaining popularity in AI and ML, mainly because they save time and resources and offer an easier path to integrating LLMs into specialized applications.

Benefits of vLLM

vLLM offers several benefits for LLM applications, including:

Open-source

LLM is a freely available inference and serving engine that allows developers to access, modify, and contribute to its codebase.

High Performance

vLLM is one of the fastest inference servers, achieving up to 24 times higher throughput than traditional methods. So you can significantly reduce response times for your AI applications.

Broad Model Support

vLLM seamlessly integrates with a wide range of open-source models like generative Transformer models in HuggingFace Transformers, including:

• Llama 3.1
• Llama 3
• Mistral
• Mixtral-8x7B
• Qwen2
• And more

Easy Deployment

vLLM is a user-friendly tool. Its architecture minimizes setup complexity, allowing you to get your models up and running quickly without requiring deep hardware optimization or memory management expertise.

Related Reading

• SGlang
• Ollama

How Does vLLM Achieve Fast Inference?

One of the biggest challenges in LLM inference is the high memory consumption required to process large sequences of tokens. Traditional inference mechanisms struggle with scaling efficiently, often leading to excessive vLLM memory usage and reduced throughput. vLLM solves this problem with Paged Attention, an attention algorithm inspired by the classic idea of virtual memory and paging in operating systems. Instead of allocating all memory upfront, Paged Attention allocates memory in smaller chunks. This approach:

• Reduces GPU Memory Overhead: Paged Attention avoids unnecessary allocations by using only the memory needed at any given moment.
• Enables Larger Context Windows: Developers can work with longer sequences without worrying about memory constraints.
• Improves Scalability: Multiple models or larger batch sizes can run simultaneously on the same hardware.

Keep it Moving with Continuous Batching

Traditional batching methods in LLM inference often fail to utilize GPU resources fully. Static batching requires waiting until a batch is filled before processing, leading to underutilization during periods of low activity. vLLM introduces Continuous Batching, an innovative approach that dynamically merges incoming requests into ongoing batches. This system offers:

• Higher Throughput: By continuously feeding the GPU with data, vLLM minimizes idle time and utilization.
• Reduced Latency: Real-time applications benefit from faster response times, as requests no longer have to wait for a whole batch.
• Support for Diverse Workloads: Continuous Batching adapts seamlessly to varying request sizes and arrival patterns, making it ideal for multi-tenant environments.

Optimized CUDA Kernels for Next-Level Performance

Optimized CUDA kernels are tailored to perform low-level GPU operations with maximum efficiency. CUDA is a parallel computing platform by NVIDIA that accelerates AI workloads. vLLM takes this further by fine-tuning its kernels specifically for vLLM inference.

These optimizations include integrating FlashAttention and FlashInfer, resulting in faster end-to-end inference. The optimized kernels are designed to leverage the full potential of NVIDIA GPUs, such as the NVIDIA A100 and the NVIDIA H100, to ensure top-tier performance across hardware generations.

Related Reading

• Gemma LLM
• Llama.cpp

Why vLLM Is Becoming a Standard for Enhancing LLM Performance

Large language models can cost thousands of dollars to train and deploy, so it’s no surprise that organizations are looking for ways to reduce the costs and compute resources required to run them. This has led to the growing adoption of vLLM, an inference engine specifically designed to improve the performance of large language models. VLLM is changing the game for organizations looking to deploy large language models by optimizing scalability, reducing latency, and lowering inference costs.

The primary algorithm that came out of vLLM is PagedAttention. However, PagedAttention is not the only capability that vLLM provides. Additional performance optimizations that vLLM can offer include:

• PyTorch Compile/CUDA Graph: for optimizing GPU memory.
• Quantization: This is used to reduce memory space required to run models.
• Tensor parallelism: This breaks up the processing work among multiple GPUs.
• Speculative decoding: for speeding up text generation by using a smaller model to predict tokens and a larger model to validate that prediction.
• Flash Attention: This is used to improve the efficiency of transformer models.

Aside from its optimization abilities, vLLM's flexible nature has also helped it grow in popularity. vLLM works with small and large language models and integrates with popular models and frameworks. Finally, its open-source nature allows for code transparency, customization, and faster bug fixes.

LoRA and QLoRA: Resource-Efficient Fine-Tuning Techniques

LoRA and QLoRA are resource-efficient fine-tuning techniques that help users optimize costs and compute resources. Instead of updating all 70 billion parameters of a large language model when fine-tuning a specific task, LoRA reduces the memory and compute requirements by only training a small set of additional parameters (usually less than 100).

Once the model is fine-tuned, the original model can be discarded, and the additional parameters can be used to condition the pre-trained model for the task of interest. This approach optimizes performance while significantly reducing the costs of fine-tuning large models.

vLLM Outperforms Other Inference Engines Across Multiple Metrics

The official vLLM benchmark results (as reported by vLLM here) show vLLM v0.6.0 against several alternative serving engines, including TensorRT-LLM r24.07, SGLang v0.3.0, and lm-deploy v0.6.0a0. These benchmarks were conducted across various models and datasets to determine how well each engine performs under different conditions. Benchmark Setup

The following configurations were used for benchmarking vLLM:

• Models: vLLM was tested on performance on two popular models, Llama 3 8B and Llama 3 70B.
• Hardware: The official vLLM benchmarks were carried out using high-end GPUs like the NVIDIA A100 and NVIDIA H100.
• Datasets: The tests were performed using three datasets:
  • ShareGPT: Comprising 500 prompts randomly sampled from the ShareGPT dataset.
  • Prefill-heavy Dataset: A synthetic dataset using the sonnet dataset with an average of 462 input tokens and 16 output tokens.
  • Decode-heavy Dataset: Another synthetic dataset generated from the sonnet dataset, containing similar input tokens but with a significantly higher average of 256 output tokens.
• Metrics: The benchmarks evaluated the engines based on:
  • Throughput: The number of requests served per second (requests per second, QPS) under simultaneous request loads.

vLLM Performance Results

For throughput, vLLM showed the highest performance [See the results below] on the NVIDIA H100 GPUs for both Llama 3 8B and Llama 3 70B models compared to the other serving engines.

vLLM outperformed all alternatives in the ShareGPT and Decode-heavy datasets. While TensorRT-LLM is a strong player in this space, especially with its hardware-optimized vLLM inference pipeline, vLLM showed superior throughput and reduced token-generation times. The SGLang and lmdeploy engines also performed decently in some instances but vLLM surpassed them in both throughput and processing efficiency for more complex queries.

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A Quick Explanation of vLLM

vLLM is an open-source library that optimizes the inference of large language models (LLMs) on cloud environments. Developed by researchers at UC Berkeley, vLLM has been shown to deliver performance gains of up to 30% over existing inference libraries such as Hugging Face’s Optimum and Triton.

vLLM's speed and efficiency make it ideal for serving LLMs in production, particularly for applications that require low-latency responses to user queries, such as chatbots and RAG.