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@@ -16,16 +16,8 @@ Easy, fast, and cheap LLM serving for everyone
 
 ---
 
-**The First vLLM Bay Area Meetup (Oct 5th 6pm-8pm PT)**
-
-We are excited to invite you to the first vLLM meetup!
-The vLLM team will share recent updates and roadmap.
-We will also have vLLM users and contributors coming up to the stage to share their experiences.
-Please register [here](https://lu.ma/first-vllm-meetup) and join us!
-
----
-
 *Latest News* 🔥
+- [2023/10] We hosted [the first vLLM meetup](https://lu.ma/first-vllm-meetup) in SF! Please find the meetup slides [here](https://docs.google.com/presentation/d/1QL-XPFXiFpDBh86DbEegFXBXFXjix4v032GhShbKf3s/edit?usp=sharing).
 - [2023/09] We created our [Discord server](https://discord.gg/jz7wjKhh6g)! Join us to discuss vLLM and LLM serving! We will also post the latest announcements and updates there.
 - [2023/09] We released our [PagedAttention paper](https://arxiv.org/abs/2309.06180) on arXiv!
 - [2023/08] We would like to express our sincere gratitude to [Andreessen Horowitz](https://a16z.com/2023/08/30/supporting-the-open-source-ai-community/) (a16z) for providing a generous grant to support the open-source development and research of vLLM.
@@ -82,36 +74,6 @@ Visit our [documentation](https://vllm.readthedocs.io/en/latest/) to get started
 - [Quickstart](https://vllm.readthedocs.io/en/latest/getting_started/quickstart.html)
 - [Supported Models](https://vllm.readthedocs.io/en/latest/models/supported_models.html)
 
-## Performance
-
-vLLM outperforms Hugging Face Transformers (HF) by up to 24x and Text Generation Inference (TGI) by up to 3.5x, in terms of throughput.
-For details, check out our [blog post](https://vllm.ai).
-
-<p align="center">
-  <picture>
-  <source media="(prefers-color-scheme: dark)" srcset="https://raw.githubusercontent.com/vllm-project/vllm/main/docs/source/assets/figures/perf_a10g_n1_dark.png">
-  <img src="https://raw.githubusercontent.com/vllm-project/vllm/main/docs/source/assets/figures/perf_a10g_n1_light.png" width="45%">
-  </picture>
-  <picture>
-  <source media="(prefers-color-scheme: dark)" srcset="https://raw.githubusercontent.com/vllm-project/vllm/main/docs/source/assets/figures/perf_a100_n1_dark.png">
-  <img src="https://raw.githubusercontent.com/vllm-project/vllm/main/docs/source/assets/figures/perf_a100_n1_light.png" width="45%">
-  </picture>
-  <br>
-  <em> Serving throughput when each request asks for 1 output completion. </em>
-</p>
-
-<p align="center">
-  <picture>
-  <source media="(prefers-color-scheme: dark)" srcset="https://raw.githubusercontent.com/vllm-project/vllm/main/docs/source/assets/figures/perf_a10g_n3_dark.png">
-  <img src="https://raw.githubusercontent.com/vllm-project/vllm/main/docs/source/assets/figures/perf_a10g_n3_light.png" width="45%">
-  </picture>
-  <picture>
-  <source media="(prefers-color-scheme: dark)" srcset="https://raw.githubusercontent.com/vllm-project/vllm/main/docs/source/assets/figures/perf_a100_n3_dark.png">
-  <img src="https://raw.githubusercontent.com/vllm-project/vllm/main/docs/source/assets/figures/perf_a100_n3_light.png" width="45%">
-  </picture>  <br>
-  <em> Serving throughput when each request asks for 3 output completions. </em>
-</p>
-
 ## Contributing
 
 We welcome and value any contributions and collaborations.
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