The user wants me to rewrite a tec

1. Title must be 14-50 chars, follow the formulas given
2. No "最近/上周" type openings - need a different切入方式
3. No markdown tables (硬性禁止)
4. No generic tags like "AI", "大模型", "技术", "教程"
5. Must use code blocks for code/config
6. No external bare URLs (only full specific paths in code blocks)
7. No source attribution at the end
8. 400-600 characters minimum (actually 400-600 words? The prompt says "正文400-600字")
9. Tags at the end in specific format
10. No "AI Agent" "RAG" "Prompt" "Agent" "Benchmark" "Fine-tuning" "Inference" "MCP" as tags

Let me think about the title. The original is "Serving Gemma 4 2B on a Single TPU v5e Chip with MCP and Antigravity CLI".

Using formula ① tutorial/实战: "工具名+动词短语" - something like "Claude Code实战技巧" style. But this is about TPU v5e and Gemma 4 2B.

Let me go with: "Gemma 4 2B 上单芯片 TPU v5e 的部署实战" - that's about 22 chars.

Or using formula ②: "分享一个/我的+描述" - "分享一个TPU v5e上的Gemma 4 2B部署方案"

Or formula ①: "告别X：如何用Y" - "告别v6e溢价：用TPU v5e跑Gemma 4 2B的完整部署"

Let me draft the content. I need to:

• Remove the markdown table (replace with bold lists)
• Change the opening (no time references)
• Keep technical accuracy
• Keep code blocks
• Stay 400-600 words
• Use SEO keywords naturally
• End with proper tags

Let me rewrite:

Title: 告别v6e溢价：用TPU v5e跑Gemma 4 2B的完整部署指南

Wait, let me count chars: 告别v6e溢价：用TPU v5e跑Gemma 4 2B的完整部署指南 - that's about 28 chars. Good.

Now for the opening - I need something different. Let me try a direct opinion/conclusion start:

"TPU v5e 在单芯片上跑 Gemma 4 2B 是性价比最优解，但这篇文章里你会看到它为什么只对 decode-bound 工作负载成立。"

That's a strong opinion opening, no time reference.

Let me think about structure. The original has sections: what the project does, chip spec comparison (table - must convert), Antigravity CLI, config file approach, MCP config. I need to convert the table to bold lists and restructure.

Let me write this out:

---

Title: 告别v6e溢价：用TPU v5e跑Gemma 4 2B的完整部署指南

Opening: A direct conclusion/opinion.

Then cover: chip specs as bold lists, the deployment approach, config management, MCP setup, code blocks for configs.

Tags need to be specific entities from the content: Gemma, TPU, v5e, Antigravity CLI, MCP... wait, "MCP" is forbidden as a tag. Let me check the forbidden list: "AI Agent" "RAG" "Prompt" "Agent" "Benchmark" "Fine-tuning" "Inference" "MCP" — yes MCP is forbidden.

So tags could be: Gemma, TPU, v5e, Antigravity CLI, vLLM, Google Cloud

Let me draft the full text now, aiming for 400-600 words.

Let me count carefully. I'll write it out and aim for that range.

Let me also check: the original content mentions MCP server, Antigravity CLI, server.py, tpu.env, etc. I need to preserve the code blocks and technical details but restructure.

One key thing: the original has a markdown table comparing v5e vs v6e specs. I MUST NOT use markdown tables. I need to convert this to bold lists or prose.

Let me write:

---

TPU v5e 在单芯片上跑 Gemma 4 2B 是性价比最优解——但这句话有一个隐藏前提：你的负载是 decode-bound，而不是 FLOPS-bound。这篇文章会说明为什么 v5e 恰好打在了 sweet spot 上。

项目本身没变：一个 DevOps/SRE 助手，脑子是自托管的 Gemma 4 模型。MCP server 负责申请 TPU、部署 vLLM 容器、发现端点，然后用那个端点分析 Cloud Logging 输出。31 个工具，一个 server.py，stdio transport。变化的是目标芯片：v6e-1 降到 v5e-1，模型从 4B 降到 2B。

芯片规格对比

先看纸面数据。v5e 和 v6e 的差距不是线性的：

• HBM 容量：v5e 单芯片 16 GB，v6e 单芯片 32 GB，v6e 是 v5e 的 2 倍
• HBM 带宽：v5e 标注 800 GiBps，v6e 标注 1,638 GBps，单位归一化后实际比值约 1.9×
• 峰值 BF16 算力：v5e 197 TFLOPs vs v6e 918 TFLOPs，v6e 高出 4.66 倍
• 峰值 INT8 算力：v5e 393 TOPs vs v6e 1,836 TOPs，约 4.67 倍
• 单芯片机型：v5e 对应 ct5lp-hightpu-1t，v6e 对应 ct6e-standard-1t
• 按需价格：v5e 约 $1.20/芯片小时，v6e 约 $2.70/芯片小时，v6e 是 v5e 的 2.25 倍
• Flex-start 弹性价：v5e 约 $0.60/芯片小时，v6e 约 $1.35/芯片小时，比例同样是 2.25×

关键结论：v6e 多花的钱换来的是 2 倍内存、2 倍带宽、但 4.7 倍 FLOPS。对于 2B 参数的 decode-bound 负载——本质是带宽瓶颈，不是算力瓶颈——v5e 的定价几乎精确匹配需求。只有 prefill 密集或长上下文场景，v6e 那 4.7 倍的算力才真正有用。

Antigravity CLI 与部署配置

Antigravity CLI 是 Gemini CLI 的继任者，终端驱动、agent 辅助编码。安装后直接 agy 启动并认证到 GCP 项目。

整个部署最关键的工程决策是把所有参数收敛到一张 tpu.env 文件：

# tpu.env — 本套部署的唯一配置源
GOOGLE_CLOUD_PROJECT=aisprint-491218
GOOGLE_CLOUD_REGION=us-west4
GOOGLE_CLOUD_ZONE=us-west4-a

MODEL_NAME=google/gemma-4-E2B-it
ACCELERATOR_TYPE=v5litepod-1
TENSOR_PARALLEL_SIZE=1

server.py 通过 load_dotenv 读取它，mcp-run.sh 由 MCP 配置直接启动它，Makefile 用 -include 引入它，set_env.sh 也从中取值。四处消费同一份来源，GOOGLE_CLOUD_ZONE=europe-west4-a make status 这样的单次覆盖仍然有效——load_dotenv 不会覆写已有环境变量，wrapper 只导出未设置的项，Makefile 用 ?=。改一次zone，不用改五个地方。这在调试曾因为 mcp_config.json 里残留旧 zone 导致部署失败的坑之后，成了强制规范。

对应的 MCP 配置也相应地无聊——这才是设计目标：

{
 "mcpServers": {
 "tpu-2B-v5e1-devops-agent": {
 "command": "/home/xbill/gemma4-queens/tpu

Hmm, the original text gets cut off at the MCP config JSON. The original content seems truncated. Let me work with what I have and fill in appropriately.

Let me also check word count as I go. I need 400-600 words (Chinese characters count as words here typically).