diff --git a/docs_new/cookbook/autoregressive/GLM/GLM-5.1.mdx b/docs_new/cookbook/autoregressive/GLM/GLM-5.1.mdx
index e1a624461..14afc85c9 100644
--- a/docs_new/cookbook/autoregressive/GLM/GLM-5.1.mdx
+++ b/docs_new/cookbook/autoregressive/GLM/GLM-5.1.mdx
@@ -2,7 +2,6 @@
title: GLM-5.1
metatags:
description: "Deploy GLM-5.1 with SGLang on NVIDIA H100/H200/B300/GB300 and AMD MI300X/MI325X/MI355X."
-tag: NEW
---
## 1. Model Introduction
diff --git a/docs_new/cookbook/autoregressive/GLM/GLM-5.2.mdx b/docs_new/cookbook/autoregressive/GLM/GLM-5.2.mdx
new file mode 100644
index 000000000..0ef366ce8
--- /dev/null
+++ b/docs_new/cookbook/autoregressive/GLM/GLM-5.2.mdx
@@ -0,0 +1,195 @@
+---
+title: GLM-5.2
+description: "Deploy GLM-5.2 with SGLang — Z.ai's DeepSeek-Sparse-Attention (DSA) Mixture-of-Experts model with MTP speculative decoding and 1M context, on H200, B200, and GB300."
+tag: NEW
+---
+
+## Deployment
+
+
+
+
+
+For all methods and hardware platforms, see the [official SGLang installation guide](../../../docs/get-started/install). The two paths below match the **Python / Docker** toggle in the command panel.
+
+
+
+
+
+```bash Command
+pip install --upgrade pip
+pip install uv
+uv pip install sglang
+```
+
+Then run the **Python** output of the command panel below in that environment.
+
+
+
+
+
+```bash Command
+docker pull lmsysorg/sglang:latest
+```
+
+For how to launch the image, see [Install → Method 3: Using Docker](../../../docs/get-started/install#method-3-using-docker). Substitute the inner `sglang serve ...` with what the command generator below produces.
+
+
+
+
+
+
+
+Pick your hardware + recipe to generate the launch command. The three serving strategies cover the common operating points:
+
+- **Low-Latency** — fastest reply for a single user. Pick for chat.
+- **Balanced** — good speed with several users at once. Use for typical multi-user serving.
+- **High-Throughput** — most tokens per second across many users. Best for batch jobs.
+
+import { Deployment } from "/src/snippets/_deployment.jsx";
+import { config } from "/src/snippets/configs/zai-org/glm-5.2.jsx";
+import { benchmarks } from "/src/snippets/configs/zai-org/glm-5.2-benchmarks.jsx";
+
+
+
+## Playground
+
+The Playground is where you experiment with **SGLang features beyond the verified matrix**. The Deploy panel above only emits combinations the SGLang team has signed off on; the Playground lets you turn on additional knobs on top of whichever cell the Deploy panel is currently showing.
+
+import { Playground } from "/src/snippets/_playground.jsx";
+
+
+
+## 1. Model Introduction
+
+**GLM-5.2** is Z.ai's flagship Mixture-of-Experts model built on **DeepSeek Sparse Attention (DSA)**: a lightning indexer selects a sparse set of key tokens per query (top-2048), so attention cost stays near-constant as context grows. It ships in two precisions — **FP8** (`zai-org/GLM-5.2-FP8`) and full **BF16** (`zai-org/GLM-5.2`) — both with **78 transformer layers**, **256 routed experts** (8 active per token), a **1M-token context window**, and a single **MTP (Multi-Token Prediction)** layer for built-in EAGLE-style speculative decoding. FP8 is the recommended deployment; BF16 (~1.5 TB) needs an 8×B300 node or a multi-node setup.
+
+