[Docs] Update start/install.md

[windsonsea]

A few minor changes to improve the text and format.

Checklist

• Format your code according to the Code Formatting with Pre-Commit.
• Add unit tests as outlined in the Running Unit Tests.
• Update documentation / docstrings / example tutorials as needed, according to Writing Documentation.
• Provide throughput / latency benchmark results and accuracy evaluation results as needed, according to Benchmark and Profiling and Accuracy Results.
• For reviewers: If you haven't made any contributions to this PR and are only assisting with merging the main branch, please remove yourself as a co-author when merging the PR.
• Please feel free to join our Slack channel at https://slack.sglang.ai to discuss your PR.

[zhaochenyang20]

@simveit could you help to take a look?

[simveit]

@zhaochenyang20 @windsonsea left a comment

[zhaochenyang20]

@simveit is it ready on your side？

[simveit]

@zhaochenyang20 i would say it's ok, but not sure if need this PR. It just inserts a few newlines?

[zhaochenyang20]

Not necessary.

[windsonsea]

This newline is required indeed 🥲

[zhaochenyang20]

why is required 🤔？

[windsonsea]

See website: https://docs.sglang.ai/start/install.html#method-5-using-kubernetes

Options 1 & 2 are similar to two small headings.

Execute... and Modify... are its description.

Check the difference here.

Before:

1. Option 1: For single node serving (typically when the model size fits into GPUs on one node) Execute command kubectl apply -f docker/k8s-sglang-service.yaml, to create k8s deployment and service, with llama-31-8b as example.

2. Option 2: For multi-node serving (usually when a large model requires more than one GPU node, such as DeepSeek-R1) Modify the LLM model path and arguments as necessary, then execute command kubectl apply -f docker/k8s-sglang-distributed-sts.yaml, to create two nodes k8s statefulset and serving service.

After modified (an unordered list might be more suitable):

• Option 1: For single node serving (typically when the model size fits into GPUs on one node)

  Execute command kubectl apply -f docker/k8s-sglang-service.yaml, to create k8s deployment and service, with llama-31-8b as example.

• Option 2: For multi-node serving (usually when a large model requires more than one GPU node, such as DeepSeek-R1)

  Modify the LLM model path and arguments as necessary, then execute command kubectl apply -f docker/k8s-sglang-distributed-sts.yaml, to create two nodes k8s statefulset and serving service.

[zhaochenyang20]

good, I see!

[windsonsea]

One more concern is to make each command easy to copy/paste, then we can improve it further like:

• Option 1: For single node serving (typically when the model size fits into GPUs on one node)

  Execute the following command to create k8s deployment and service, with llama-31-8b as example.

  kubectl apply -f docker/k8s-sglang-service.yaml

• Option 2: For multi-node serving (usually when a large model requires more than one GPU node, such as DeepSeek-R1)

  Modify the LLM model path and arguments as necessary, then execute the following command to create two nodes k8s statefulset and serving service.

  kubectl apply -f docker/k8s-sglang-distributed-sts.yaml

[windsonsea]

Same issue as above