Update readme (#3809)

README.md

@@ -1,4 +1,4 @@
-<div align="center"  id="sglangtop">
+<div align="center" id="sglangtop">
 <img src="https://raw.githubusercontent.com/sgl-project/sglang/main/assets/logo.png" alt="logo" width="400" margin="10px"></img>
 
 [![PyPI](https://img.shields.io/pypi/v/sglang)](https://pypi.org/project/sglang)
@@ -19,7 +19,7 @@
 | [**Slides**](https://github.com/sgl-project/sgl-learning-materials?tab=readme-ov-file#slides) |
 
 ## News
-- [2025/01] 🔥 SGLang provides day one support for DeepSeek V3/R1 models on NVIDIA and AMD GPUs with DeepSeek-specific optimizations. ([instructions](https://github.com/sgl-project/sglang/tree/main/benchmark/deepseek_v3), [AMD blog](https://www.amd.com/en/developer/resources/technical-articles/amd-instinct-gpus-power-deepseek-v3-revolutionizing-ai-development-with-sglang.html))
+- [2025/01] 🔥 SGLang provides day one support for DeepSeek V3/R1 models on NVIDIA and AMD GPUs with DeepSeek-specific optimizations. ([instructions](https://github.com/sgl-project/sglang/tree/main/benchmark/deepseek_v3), [AMD blog](https://www.amd.com/en/developer/resources/technical-articles/amd-instinct-gpus-power-deepseek-v3-revolutionizing-ai-development-with-sglang.html), [10+ other companies](https://x.com/lmsysorg/status/1887262321636221412))
 - [2024/12] 🔥 v0.4 Release: Zero-Overhead Batch Scheduler, Cache-Aware Load Balancer, Faster Structured Outputs ([blog](https://lmsys.org/blog/2024-12-04-sglang-v0-4/)).
 - [2024/09] v0.3 Release: 7x Faster DeepSeek MLA, 1.5x Faster torch.compile, Multi-Image/Video LLaVA-OneVision ([blog](https://lmsys.org/blog/2024-09-04-sglang-v0-3/)).
 - [2024/07] v0.2 Release: Faster Llama3 Serving with SGLang Runtime (vs. TensorRT-LLM, vLLM) ([blog](https://lmsys.org/blog/2024-07-25-sglang-llama3/)).
@@ -58,7 +58,10 @@ Learn more in the release blogs: [v0.2 blog](https://lmsys.org/blog/2024-07-25-s
 [Development Roadmap (2024 Q4)](https://github.com/sgl-project/sglang/issues/1487)
 
 ## Adoption and Sponsorship
-The project is supported by (alphabetically): AMD, Atlas Cloud, Baseten, Cursor, DataCrunch, Etched, Hyperbolic, Jam & Tea Studios, LinkedIn, LMSYS CORP, Meituan, Nebius, Novita AI, NVIDIA, RunPod, Stanford, UC Berkeley, UCLA, xAI, 01.AI.
+The project has been deployed to large-scale production, generating trillions of tokens every day.
+It is supported by the following institutions: AMD, Atlas Cloud, Baseten, Cursor, DataCrunch, Etched, Hyperbolic, Jam & Tea Studios, LinkedIn, LMSYS, Meituan, Nebius, Novita AI, NVIDIA, RunPod, Stanford, UC Berkeley, UCLA, xAI, and 01.AI.
+
+<img src="https://raw.githubusercontent.com/sgl-project/sgl-learning-materials/main/slides/adoption.png" alt="logo" width="800" margin="10px"></img>
 
 ## Contact Us