diff --git a/README.md b/README.md
index 6ffa5e473..b6af86a28 100644
--- a/README.md
+++ b/README.md
@@ -14,13 +14,14 @@ The core features include:
 - **Flexible Frontend Language**: Enables easy programming of LLM applications with chained generation calls, advanced prompting, control flow, multiple modalities, parallelism, and external interactions.
 
 ## News
-- [2024/04] 🔥 SGLang is used by the official **LLaVA-NeXT (video)** release ([blog](https://llava-vl.github.io/blog/2024-04-30-llava-next-video/)).
-- [2024/02] 🔥 SGLang enables **3x faster JSON decoding** with compressed finite state machine ([blog](https://lmsys.org/blog/2024-02-05-compressed-fsm/)).
-- [2024/01] SGLang provides up to **5x faster inference** with RadixAttention ([blog](https://lmsys.org/blog/2024-01-17-sglang/)).
+- [2024/07] 🔥 Faster Llama3 Serving with SGLang Runtime (vs. TensorRT-LLM, vLLM) ([blog](https://lmsys.org/blog/2024-07-25-sglang-llama3/)).
+- [2024/04] SGLang is used by the official **LLaVA-NeXT (video)** release ([blog](https://llava-vl.github.io/blog/2024-04-30-llava-next-video/)).
+- [2024/02] SGLang enables **3x faster JSON decoding** with compressed finite state machine ([blog](https://lmsys.org/blog/2024-02-05-compressed-fsm/)).
 
 <details>
 <summary>More</summary>
 
+- [2024/01] SGLang provides up to **5x faster inference** with RadixAttention ([blog](https://lmsys.org/blog/2024-01-17-sglang/)).
 - [2024/01] SGLang powers the serving of the official **LLaVA v1.6** release demo ([usage](https://github.com/haotian-liu/LLaVA?tab=readme-ov-file#demo)).
 
 </details>
@@ -58,6 +59,7 @@ pip install flashinfer -i https://flashinfer.ai/whl/cu121/torch2.3/
 
 ### Method 3: Using docker
 The docker images are available on Docker Hub as [lmsysorg/sglang](https://hub.docker.com/r/lmsysorg/sglang/tags), built from [Dockerfile](docker).
+Repalce `<secret>` below with your huggingface hub [token](https://huggingface.co/docs/hub/en/security-tokens).
 
 ```bash
 docker run --gpus all \
@@ -411,15 +413,12 @@ for out in state.text_iter():
 - The `choices` argument in `sgl.gen` is implemented by computing the [token-length normalized log probabilities](https://blog.eleuther.ai/multiple-choice-normalization/) of all choices and selecting the one with the highest probability.
 - The `regex` argument in `sgl.gen` is implemented through autoregressive decoding with logit bias masking, according to the constraints set by the regex. It is compatible with `temperature=0` and `temperature != 0`.
 
+
 ## Benchmark And Performance
-- Llama-7B on NVIDIA A10G, FP16, Tensor Parallelism=1
-![llama_7b](assets/llama_7b.jpg)
+![8b_throughput](https://lmsys.org/images/blog/sglang_llama3/8b_throughput.svg)
+![70b_fp8_throughput](https://lmsys.org/images/blog/sglang_llama3/70b_fp8_throughput.svg)
 
-- Mixtral-8x7B on NVIDIA A10G, FP16, Tensor Parallelism=8
-![mixtral_8x7b](assets/mixtral_8x7b.jpg)
-
-- Learn more about the above [results](docs/benchmark_results.md).
-- Synthetic latency and throughput benchmark [scripts](https://github.com/sgl-project/sglang/tree/main/benchmark/latency_throughput).
+Learn more at this [blog](https://lmsys.org/blog/2024-07-25-sglang-llama3/).
 
 ## Roadmap
 [Development Roadmap (2024 Q3)](https://github.com/sgl-project/sglang/issues/634)