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Simple, Quick, and Low cost LLM Serving with PagedAttention

Simple, Quick, and Low cost LLM Serving with PagedAttention

2023-06-20 14:17:32

By Woosuk Kwon*, Zhuohan Li*, Siyuan Zhuang, Ying Sheng, Lianmin Zheng, Cody Yu, Joey Gonzalez, Hao Zhang, and Ion Stoica (* Equal Contribution). June twentieth, 2023

GitHub | Documentation | Paper (Keep Tuned)


LLMs promise to basically change how we use AI throughout all industries. Nonetheless, truly serving these fashions is difficult and may be surprisingly sluggish even on costly {hardware}. As we speak we’re excited to introduce vLLM, an open-source library for quick LLM inference and serving. vLLM makes use of PagedAttention, our new consideration algorithm that successfully manages consideration keys and values. vLLM outfitted with PagedAttention redefines the brand new cutting-edge in LLM serving: it delivers as much as 24x greater throughput than HuggingFace Transformers, with out requiring any mannequin structure modifications.

vLLM has been developed at UC Berkeley and deployed at Chatbot Arena and Vicuna Demo for the previous two months. It’s the core expertise that makes LLM serving reasonably priced even for a small analysis staff like LMSYS with restricted compute assets. Check out vLLM now with a single command at our GitHub repository.

Past State-of-the-art Efficiency

We evaluate the throughput of vLLM with HuggingFace Transformers (HF), the most well-liked LLM library and HuggingFace Text Generation Inference (TGI), the earlier cutting-edge. We consider in two settings: LLaMA-7B on an NVIDIA A10G GPU and LLaMA-13B on an NVIDIA A100 GPU (40GB). We pattern the requests’ enter/output lengths from the ShareGPT dataset. In our experiments, vLLM achieves as much as 24x greater throughput in comparison with HF and as much as 3.5x greater throughput than TGI.

Serving throughput when every request asks for one output completion. vLLM achieves 14x – 24x greater throughput than HF and a pair of.2x – 2.5x greater throughput than TGI.


Serving throughput when every request asks for three parallel output completions. vLLM achieves 8.5x – 15x greater throughput than HF and three.3x – 3.5x greater throughput than TGI.

The Secret Sauce: PagedAttention

In vLLM, we establish that the efficiency of LLM serving is bottlenecked by reminiscence. Within the autoregressive decoding course of, all of the enter tokens to the LLM produce their consideration key and worth tensors, and these tensors are stored in GPU reminiscence to generate subsequent tokens. These cached key and worth tensors are sometimes called KV cache. The KV cache is

  • Massive: Takes as much as 1.7GB for a single sequence in LLaMA-13B.
  • Dynamic: Its dimension depends upon the sequence size, which is very variable and unpredictable.
    Consequently, effectively managing the KV cache presents a big problem. We discover that current programs waste 60% – 80% of reminiscence resulting from fragmentation and over-reservation.

To deal with this downside, we introduce PagedAttention, an consideration algorithm impressed by the traditional concept of digital reminiscence and paging in working programs. Not like the standard consideration algorithms, PagedAttention permits storing steady keys and values in non-contiguous reminiscence house. Particularly, PagedAttention partitions the KV cache of every sequence into blocks, every block containing the keys and values for a set variety of tokens. Through the consideration computation, the PagedAttention kernel identifies and fetches these blocks effectively.


PagedAttention: KV Cache are partitioned into blocks. Blocks don’t must be contiguous in reminiscence house.

As a result of the blocks don’t must be contiguous in reminiscence, we are able to handle the keys and values in a extra versatile method as in OS’s digital reminiscence: one can consider blocks as pages, tokens as bytes, and sequences as processes. The contiguous logical blocks of a sequence are mapped to non-contiguous bodily blocks by way of a block desk. The bodily blocks are allotted on demand as new tokens are generated.


Instance technology course of for a request with PagedAttention.

In PagedAttention, reminiscence waste solely occurs within the final block of a sequence. In observe, this leads to near-optimal reminiscence utilization, with a mere waste of underneath 4%. This enhance in reminiscence effectivity proves extremely useful: It permits the system to batch extra sequences collectively, improve GPU utilization, and thereby considerably improve the throughput as proven within the efficiency end result above.

PagedAttention has one other key benefit: environment friendly reminiscence sharing. For instance, in parallel sampling, a number of output sequences are generated from the identical immediate. On this case, the computation and reminiscence for the immediate may be shared between the output sequences.


Instance of parallel sampling.

PagedAttention naturally permits reminiscence sharing via its block desk. Just like how processes share bodily pages, totally different sequences in PagedAttention can share the blocks by mapping their logical blocks to the identical bodily block. To make sure secure sharing, PagedAttention retains monitor of the reference counts of the bodily blocks and implements the Copy-on-Write mechanism.


Instance technology course of for a request that samples a number of outputs.

PageAttention’s reminiscence sharing significantly reduces the reminiscence overhead of advanced sampling algorithms, corresponding to parallel sampling and beam search, reducing their reminiscence utilization by as much as 55%. This will translate into as much as 2.2x enchancment in throughput. This makes such sampling strategies sensible in LLM providers.

PagedAttention is the core expertise behind vLLM, our LLM inference and serving engine that helps a wide range of fashions with excessive efficiency and an easy-to-use interface. For extra technical particulars about vLLM and PagedAttention, try our GitHub repo and keep tuned for our paper.

The Silent Hero Behind LMSYS Vicuna and Chatbot Area

This April, LMSYS developed the favored Vicuna chatbot fashions and made them publicly out there. Since then, Vicuna has been served in Chatbot Arena for hundreds of thousands of customers. Initially, LMSYS FastChat adopted a HF Transformers based mostly serving backend to serve the chat demo. Because the demo grew to become extra common, the height site visitors ramped up a number of occasions, making the HF backend a big bottleneck. The LMSYS and vLLM staff have labored collectively and shortly developed the FastChat-vLLM integration to make use of vLLM as the new backend with a purpose to assist the rising calls for (as much as 5x extra site visitors). In an early internal micro-benchmark by LMSYS, the vLLM serving backend can obtain as much as 30x greater throughput than an preliminary HF backend.

See Also

Since mid-April, the most well-liked fashions corresponding to Vicuna, Koala, and LLaMA, have all been efficiently served utilizing the FastChat-vLLM integration – With FastChat because the multi-model chat serving frontend and vLLM because the inference backend, LMSYS is ready to harness a restricted variety of university-sponsored GPUs to serve Vicuna to hundreds of thousands of customers with excessive throughput and low latency. LMSYS is increasing using vLLM to a wider vary of fashions, together with Databricks Dolly, LAION’s OpenAsssiant, and Stability AI’s stableLM. The support for more models is being developed and forthcoming.


Requests served by FastChat-vLLM integration within the Chatbot Area between April to Could. Certainly, greater than half of the requests to Chatbot Area use vLLM because the inference backend.

This utilization of vLLM has additionally considerably lowered operational prices. With vLLM, LMSYS was capable of reduce the variety of GPUs used for serving the above site visitors by 50%. vLLM has been dealing with a median of 30K requests every day and a peak of 60K, which is a transparent demonstration of vLLM’s robustness.

Get began with vLLM

Set up vLLM with the next command (try our installation guide for extra):

vLLM can be utilized for each offline inference and on-line serving. To make use of vLLM for offline inference, you’ll be able to import vLLM and use the LLM class in your Python scripts:

from vllm import LLM

prompts = ["Hello, my name is", "The capital of France is"]  # Pattern prompts.
llm = LLM(mannequin="lmsys/vicuna-7b-v1.3")  # Create an LLM.
outputs = llm.generate(prompts)  # Generate texts from the prompts.

To make use of vLLM for on-line serving, you can begin an OpenAI API-compatible server by way of:

$ python -m vllm.entrypoints.openai.api_server --model lmsys/vicuna-7b-v1.3

You may question the server with the identical format as OpenAI API:

$ curl http://localhost:8000/v1/completions 
    -H "Content material-Sort: utility/json" 
    -d '{
        "mannequin": "lmsys/vicuna-7b-v1.3",
        "immediate": "San Francisco is a",
        "max_tokens": 7,
        "temperature": 0
    }'

For extra methods to make use of vLLM, please try the quickstart guide.


Weblog written by Woosuk Kwon and Zhuohan Li (UC Berkeley). Particular because of Hao Zhang for the combination of vLLM and FastChat and for writing the corresponding part. We thank all the staff — Siyuan Zhuang, Ying Sheng, Lianmin Zheng (UC Berkeley), Cody Yu (Impartial Researcher), Joey Gonzalez (UC Berkeley), Hao Zhang (UC Berkeley & UCSD), and Ion Stoica (UC Berkeley).

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