diff --git a/README.md b/README.md
index f11d12c52..7a1bc8e8b 100644
--- a/README.md
+++ b/README.md
@@ -44,12 +44,8 @@ pip install -e "python[all]"
 ```
 
 ### Notes
-- If you are using older GPUs (NVIDIA V100, T4), please pick the correct triton compiler version to avoid some known bugs.
-  - For NVIDIA T4, please use `pip install "triton>=2.2.0"`.
-  - For NVIDIA V100, please install the [nightly](https://triton-lang.org/main/getting-started/installation.html) version.
 - If you only need to use the OpenAI backend, you can avoid installing other dependencies by using `pip install "sglang[openai]"`
 
-
 ## Quick Start
 The example below shows how to use sglang to answer a mulit-turn question.
 
@@ -367,7 +363,8 @@ python -m sglang.launch_server --model-path meta-llama/Llama-2-7b-chat-hf --port
 ```
 python -m sglang.launch_server --model-path meta-llama/Llama-2-7b-chat-hf --port 30000 --mem-fraction-static 0.7
 ```
-- You can turn on [flashinfer](docs/flashinfer.md) to accelerate the inference by using highly optimized CUDA kernels.
+- See [flashinfer.md](docs/flashinfer.md) on accelerating inference using highly optimized CUDA kernels.
+- See [hyperparameter_tuning.md](docs/hyperparameter_tuning.md) on tuning hyperparameters for better performance.
 
 ### Supported Models
 - Llama
diff --git a/docs/hyperparameter_tuning.md b/docs/hyperparameter_tuning.md
index ce2ecf946..0263d557a 100644
--- a/docs/hyperparameter_tuning.md
+++ b/docs/hyperparameter_tuning.md
@@ -5,6 +5,7 @@
 Achieving a large batch size is the most important thing for attaining high throughput.
 
 When the server is running at full load, look for the following in the log:
+
 ```[gpu_id=0] #running-req: 233, #token: 370959, token usage: 0.82, gen throughput (token/s): 4594.01, #queue-req: 417```
 
 ### Tune Your Request Submission Speed
@@ -22,10 +23,10 @@ On the other hand, if you see `token usage` very high and you frequently see war
 ### Tune `--dp-size` and `--tp-size`
 Data parallelism is better for throughput. When there is enough GPU memory, always favor data parallelism for throughput.
 
-### (Minor) Tune `--max-prefill-tokens`, `--mem-fraction-static`, `--max-running-requests`.
-If you see out of memory (OOM) errors, you can decrease these parameters.
-If OOM happens during prefill, try to decrease `--max-prefill-tokens`.
-If OOM happens during decoding, try to decrease `--max-running-requests`.
+### (Minor) Tune `--max-prefill-tokens`, `--mem-fraction-static`, `--max-running-requests`
+If you see out of memory (OOM) errors, you can decrease these parameters.  
+If OOM happens during prefill, try to decrease `--max-prefill-tokens`.  
+If OOM happens during decoding, try to decrease `--max-running-requests`.  
 You can also try to decrease `--mem-fraction-static`, which reduces the memory usage of the KV cache memory pool and helps both prefill and decoding.
 
 ### (Minor) Tune `--schedule-heuristic`