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[Docs] Enhance Anyscale documentation, add quickstart links for vLLM (#21018)

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Signed-off-by: Ricardo Decal <rdecal@anyscale.com>
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docs/deployment/frameworks/anyscale.md
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[](){ #deployment-anyscale }
[Anyscale](https://www.anyscale.com) is a managed, multi-cloud platform developed by the creators of Ray.
It hosts Ray clusters inside your own AWS, GCP, or Azure account, delivering the flexibility of open-source Ray
without the operational overhead of maintaining Kubernetes control planes, configuring autoscalers, or managing observability stacks.
Anyscale automates the entire lifecycle of Ray clusters in your AWS, GCP, or Azure account, delivering the flexibility of open-source Ray
without the operational overhead of maintaining Kubernetes control planes, configuring autoscalers, managing observability stacks, or manually managing head and worker nodes with helper scripts like <gh-file:examples/online_serving/run_cluster.sh>.
When serving large language models with vLLM, Anyscale can rapidly provision [production-ready HTTPS endpoints](https://docs.anyscale.com/examples/deploy-ray-serve-llms) or [fault-tolerant batch inference jobs](https://docs.anyscale.com/examples/ray-data-llm).
## Production-ready vLLM on Anyscale quickstarts
- [Offline batch inference](https://console.anyscale.com/template-preview/llm_batch_inference?utm_source=vllm_docs)
- [Deploy vLLM services](https://console.anyscale.com/template-preview/llm_serving?utm_source=vllm_docs)
- [Curate a dataset](https://console.anyscale.com/template-preview/audio-dataset-curation-llm-judge?utm_source=vllm_docs)
- [Finetune an LLM](https://console.anyscale.com/template-preview/entity-recognition-with-llms?utm_source=vllm_docs)