---
title: "How to Deploy a Production-Grade vLLM Stack on T Cloud Public CCE"
url: https://daily.dev/posts/how-to-deploy-a-production-grade-vllm-stack-on-t-cloud-public-cce-bl76gbiny
source_url: https://itnext.io/how-to-deploy-a-production-grade-vllm-stack-on-t-cloud-public-cce-b79894043f87
type: article
source: "ITNEXT"
published: 2026-07-03T07:42:07.481Z
updated: 2026-07-03T07:42:33.482Z
tags: ["kubernetes", "gpu", "ai-inference", "vllm"]
reading_time: 27
upvotes: 0
comments: 0
language: en
---

> ## Documentation Index
> Fetch the complete documentation index at: https://daily.dev/llms.txt
> Use this file to discover all available pages before exploring further.

# How to Deploy a Production-Grade vLLM Stack on T Cloud Public CCE

**[ITNEXT](https://daily.dev/sources/itnext)** · 27 min read · 0 upvotes · 0 comments

## Summary

A comprehensive guide to deploying the vLLM Production Stack on T Cloud Public CCE (Kubernetes). Covers four deployment patterns: single-GPU standalone serving, single-node tensor parallelism on NVIDIA T4 and V100 GPUs, multi-node distributed serving with Ray/KubeRay, and disaggregated prefill/decode serving using LMCache and NIXL for high-throughput workloads. Each scenario includes full Helm chart configurations, GPU memory considerations, quantization strategies (AWQ, GPTQ INT4), and validation steps. Models covered include GPT-OSS 20B, Qwen2.5-32B-Instruct-AWQ, Llama-3.1-70B-Instruct, and Qwen2.5-14B-Instruct-AWQ.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://itnext.io/how-to-deploy-a-production-grade-vllm-stack-on-t-cloud-public-cce-b79894043f87>

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Tags: [#kubernetes](https://daily.dev/tags/kubernetes), [#gpu](https://daily.dev/tags/gpu), [#ai-inference](https://daily.dev/tags/ai-inference), [#vllm](https://daily.dev/tags/vllm)

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