Add blog post: HiCache L3 for Hybrid Attention Models

[stmatengss]

Summary

• New blog post covering the full HiCache L3 optimization stack for hybrid attention architectures (MLA + SWA)
• Topics: periodic SWA checkpoints, window-aware LRU refresh, eviction correctness fixes, C4/C128 compress-state offload, NSA indexer serialization frontier, draft KV pool registration, and Mooncake group semantics
• Includes benchmark data: 18 GB → 4 GB per million tokens, agent cache hit 8% → 94%, TTFT 163s → 5.68s
• Both English (index.md) and Chinese (index.zh.md) versions included

Test plan

• Verify Hugo builds cleanly with the new post (hugo --minify)
• Check frontmatter renders correctly on blog index
• Confirm both EN and ZH versions display properly
• Review PR links in post body resolve correctly

🤖 Generated with [Qoder][https://qoder.com]

[gemini-code-assist]

Code Review

This pull request adds a new blog post in both English and Chinese detailing the HiCache L3 optimization stack for hybrid attention models like DeepSeek V4, covering SWA checkpoints, window-aware LRU refresh, and Mooncake group semantics. The review feedback correctly identifies minor capitalization inconsistencies where "Mooncake Store" was written as "MoonCake Store" in both language versions, providing suggestions to fix them.

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[gemini-code-assist]

Inconsistent capitalization of "Mooncake". It should be "Mooncake Store" instead of "MoonCake Store" to match the rest of the post.

Suggested change

Early work on L3 offloading for NSA models ([#18637](https://github.com/sgl-project/sglang/pull/18637)) added MoonCake Store integration for DeepSeek V3.2, including indexer key serialization. However, a safety concern emerged ([#20880](https://github.com/sgl-project/sglang/pull/20880)): the L3 storage layer only handles MHA K/V and MLA latent-K formats but does not generically serialize the NSA indexer cache. Without proper handling, L3 prefetch could cause silent data corruption and shape mismatch crashes.

Early work on L3 offloading for NSA models ([#18637](https://github.com/sgl-project/sglang/pull/18637)) added Mooncake Store integration for DeepSeek V3.2, including indexer key serialization. However, a safety concern emerged ([#20880](https://github.com/sgl-project/sglang/pull/20880)): the L3 storage layer only handles MHA K/V and MLA latent-K formats but does not generically serialize the NSA indexer cache. Without proper handling, L3 prefetch could cause silent data corruption and shape mismatch crashes.

[gemini-code-assist]

Inconsistent capitalization of "Mooncake". It should be "Mooncake Store" instead of "MoonCake Store" to match the rest of the post.

Suggested change

	早期对 NSA 模型的 L3 卸载工作（[#18637](https://github.com/sgl-project/sglang/pull/18637)）为 DeepSeek V3.2 添加了 MoonCake Store 集成，包括索引器 key 的序列化。但出现了安全顾虑（[#20880](https://github.com/sgl-project/sglang/pull/20880)）：L3 存储层仅处理 MHA K/V 和 MLA latent-K 格式，不通用地序列化 NSA 索引器缓存。不正确处理时，L3 预取可能导致静默数据损坏和形状不匹配崩溃。
	早期对 NSA 模型的 L3 卸载工作（[#18637](https://github.com/sgl-project/sglang/pull/18637)）为 DeepSeek V3.2 添加了 Mooncake Store 集成，包括索引器 key 的序列化。但出现了安全顾虑（[#20880](https://github.com/sgl-project/sglang/pull/20880)）：L3 存储层仅处理 MHA K/V 和 MLA latent-K 格式，不通用地序列化 NSA 索引器缓存。不正确处理时，L3 预取可能导致静默数据损坏和形状不匹配崩溃。