Introducing vLLM: A High-Speed Open-Source Machine Learning Library for LLM Inference and Serving

Introducing vLLM: Boosting Throughput and Redefining the State of the Art in Large Language Model Serving

Have you ever wondered how large language models are revolutionizing our daily lives? From programming assistants to universal chatbots, these models are changing the game. But there’s a catch – running these applications on GPUs can be extremely expensive, with costs up to ten times higher than traditional keyword searches. That’s why you need to read this blog post. We’re about to dive into the fascinating world of vLLM, a groundbreaking solution that boosts the throughput of large language model serving systems while minimizing costs.

Batching Requests and Managing Key-Value Cache: A Complex Challenge

To achieve high throughput serving of large language models, it is crucial to batch a sufficient number of requests concurrently. However, existing systems face a major hurdle – managing the key-value cache (KV cache) memory for each request. This memory can grow and shrink dynamically, making it challenging to handle efficiently. Inefficient management can lead to fragmentation and redundant duplication, significantly reducing the batch size and increasing costs.

Introducing PagedAttention: An Innovative Solution

Researchers have proposed PagedAttention as a solution to this complex problem. Inspired by traditional virtual memory and paging techniques in operating systems, PagedAttention revolutionizes attention algorithms. By dividing the KV cache into blocks, each containing keys and values for a specific number of tokens, PagedAttention allows for flexible and non-contiguous storage. This results in efficient memory utilization with minimal inefficiency, leading to greater GPU utilization.

Achieving Zero Waste with vLLM

But that’s not all – vLLM takes memory efficiency to the next level. By utilizing PagedAttention, vLLM ensures almost zero waste in KV cache memory and enables flexible sharing of the cache within and between requests. This breakthrough innovation delivers up to 24 times more throughput than HuggingFace Transformers without requiring any changes to the model architecture. With vLLM, the state of the art in large language model serving has been redefined.

Efficient Memory Sharing and Improved Techniques

PagedAttention also brings another key advantage – efficient memory sharing. By significantly reducing the additional memory required for sampling techniques like parallel sampling and beam search, vLLM with PagedAttention enables a speed gain of up to 2.2 times while reducing memory utilization by up to 55%. This enhancement makes these sampling techniques useful and effective for Large Language Model (LLM) services.

Supercharging Large Language Models: Accuracy and Performance

The researchers behind vLLM have not only focused on memory efficiency but also ensured accuracy and performance. Compared to cutting-edge systems like FasterTransformer and Orca, vLLM increases the throughput of well-known LLMs by 2-4, with a more noticeable improvement for larger models, intricate decoding algorithms, and longer sequences.

Experience the Power of vLLM

As the impact of large language models continues to grow, the need for efficient and cost-effective serving systems becomes paramount. vLLM equipped with PagedAttention offers a game-changing solution, redefining the state of the art in LLM serving. To learn more, check out the research paper, code on GitHub, and reference article, all linked in the sources below.

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Remember, the future of large language model serving is here – and vLLM is leading the way. Discover the power of efficient memory utilization and skyrocket your throughput. Exciting times await!

– Paper:
– Github:
– Reference Article:

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