[None][doc] Update GLM-5 docs for GLM-5.3 and disaggregated serving

docs/source/deployment-guide/deployment-guide-for-glm-5-on-trtllm.md

@@ -4,29 +4,44 @@
 
 This deployment guide provides step-by-step instructions for running the GLM-5 model using TensorRT LLM with FP8 and NVFP4 quantization, optimized for NVIDIA Blackwell GPUs. It covers the complete setup required; from accessing model weights and preparing the software environment to configuring TensorRT LLM parameters, launching the server, and validating inference output.
 
-GLM-5 uses Multi-Latent Attention (MLA) with DeepSeek Sparse Attention (DSA). It shares the same architecture as DeepSeek V3.2 and reuses the `DeepseekV32ForCausalLM` code path in TensorRT LLM. GLM-5 natively supports Multi-Token Prediction (MTP) for speculative decoding.
+GLM-5 uses Multi-Latent Attention (MLA) with DeepSeek Sparse Attention (DSA). It shares the same architecture as DeepSeek V3.2 (with minor changes) and is served through the `GlmMoeDsaForCausalLM` model, which reuses the DeepSeek V3.2 code path in TensorRT LLM. GLM-5 natively supports Multi-Token Prediction (MTP) for speculative decoding.
+
+This guide applies to the GLM-5 family, including GLM-5.2 and GLM-5.3. GLM-5.3 is a weight update over GLM-5.2 with the same architecture and code path, so the server configurations and deployment steps below are identical across versions. Note that GLM-5.3 ships a revised chat template with different reasoning controls. Unlike GLM-5.2, there is no `enable_thinking` kwarg and thinking cannot be turned off; generation always begins with `<think>`. Reasoning depth is set with `reasoning_effort`, which accepts `low`, `high`, or `max` (default `max`). The separate `clear_thinking` kwarg controls whether reasoning from earlier turns is retained in multi-turn conversations; it does not disable thinking. Clients that controlled reasoning behavior via `chat_template_kwargs` on GLM-5.2 should update accordingly.
 
 The guide is intended for developers and practitioners seeking high-throughput or low-latency inference using NVIDIA's accelerated stack.
 
+### Validated Features
+
+The following features have been tested with GLM-5 on TensorRT LLM:
+
+* CUDA Graph
+* Multi-Token Prediction (MTP)
+* Disaggregated serving
+* Disaggregated serving with MTP
+
+Because GLM-5 reuses the DeepSeek V3.2 code path, other features supported by `DeepseekV32ForCausalLM` are expected to work but have not been separately validated. See the [Model-Feature Support Matrix](../models/supported-models.md#model-feature-support-matrix-key-models) for the current per-feature status.
+
 ## Prerequisites
 
 * GPU: 8x NVIDIA B200 (SM100)
 * OS: Linux
 * Drivers: CUDA Driver 575 or later
 * Docker with NVIDIA Container Toolkit installed
-* Minimum TensorRT LLM version: 1.3.0rc8
+* Minimum TensorRT LLM version: 1.3.0rc25
 
 ## Models
 
 The following checkpoints are available:
 
-* FP8 model: [zai-org/GLM-5-FP8](https://huggingface.co/zai-org/GLM-5-FP8) — Official FP8 checkpoint
-* BF16 model: [zai-org/GLM-5](https://huggingface.co/zai-org/GLM-5) — Official BF16 checkpoint
-* NVFP4 model: [warnold-nv/GLM-5-nvfp4-v1](https://huggingface.co/warnold-nv/GLM-5-nvfp4-v1) — Unofficial NVFP4 checkpoint for experimentation only. *Quantized with ModelOpt by Will Arnold.*
+* FP8 model: [zai-org/GLM-5.3](https://huggingface.co/zai-org/GLM-5.3) — Official FP8 checkpoint
+* BF16 model: [zai-org/GLM-5.3-BF16](https://huggingface.co/zai-org/GLM-5.3-BF16) — Official BF16 checkpoint
+* NVFP4 model: [warnold-nv/GLM-5-nvfp4-v1](https://huggingface.co/warnold-nv/GLM-5-nvfp4-v1) — Unofficial NVFP4 checkpoint of GLM-5 for experimentation only. *Quantized with ModelOpt by Will Arnold.*
+
+> **Note on checkpoint naming:** For GLM-5.3 the base repository (`zai-org/GLM-5.3`) contains the FP8 weights and the BF16 weights live in the `-BF16` repository. This is the reverse of GLM-5 and GLM-5.2, where the base repositories ([zai-org/GLM-5](https://huggingface.co/zai-org/GLM-5), [zai-org/GLM-5.2](https://huggingface.co/zai-org/GLM-5.2)) are BF16 and the FP8 weights live in the `-FP8` repositories ([zai-org/GLM-5-FP8](https://huggingface.co/zai-org/GLM-5-FP8), [zai-org/GLM-5.2-FP8](https://huggingface.co/zai-org/GLM-5.2-FP8)). Check the `quantization_config` in the checkpoint's `config.json` if in doubt.
 
 ```bash
 git lfs install
-git clone https://huggingface.co/zai-org/GLM-5-FP8 /models/GLM-5-FP8
+git clone https://huggingface.co/zai-org/GLM-5.3 /models/GLM-5.3
 ```
 
 ## MoE Backend Support Matrix
@@ -53,7 +68,7 @@ docker run --rm -it \
     -p 8000:8000 \
     -v /path/to/your/models:/models \
     --name tensorrt_llm \
-    nvcr.io/nvidia/tensorrt-llm/release:1.3.0rc8 \
+    nvcr.io/nvidia/tensorrt-llm/release:1.3.0rc25 \
     /bin/bash
 ```
 
@@ -133,7 +148,8 @@ Below is an example command to launch the TensorRT LLM server with GLM-5 from wi
 
 ```bash
 trtllm-serve \
-  /models/GLM-5-FP8 \
+  /models/GLM-5.3 \
+  --served_model_name zai-org/GLM-5.3 \
   --host 0.0.0.0 \
   --port 8000 \
   --max_batch_size 128 \
@@ -150,6 +166,40 @@ trtllm-serve \
 > - Reduce `--max_num_tokens` to `3072` for max-throughput configs.
 > - Reduce `--max_batch_size` and `cuda_graph_config.max_batch_size` to `32` or `16` for min-latency configs.
 
+### Disaggregated Serving
+
+GLM-5 supports disaggregated serving, which separates the prefill (context) and decode (generation) phases onto different workers so each can be scaled and tuned independently. This can be combined with MTP.
+
+Launch one or more context (prefill) servers and one or more generation (decode) servers with `trtllm-serve`, then start a `trtllm-serve disaggregated` orchestrator that routes client requests between them. Both context and generation workers must carry the same `cache_transceiver_config` to exchange the KV cache. Add the following to the config file used by each worker:
+
+```yaml
+cache_transceiver_config:
+  backend: NIXL
+```
+
+Reuse the FP8 config (or the FP8-with-MTP config for Disagg + MTP) from [Recommended Performance Settings](#recommended-performance-settings) for the workers, adding the `cache_transceiver_config` block above. The context workers should also set `disable_overlap_scheduler: true`.
+
+The orchestrator is launched with a disaggregated config that lists the context and generation worker URLs:
+
+```bash
+trtllm-serve disaggregated -c disagg_config.yaml
+```
+
+```yaml
+hostname: localhost
+port: 8000
+context_servers:
+  num_instances: 1
+  urls:
+    - "localhost:8001"
+generation_servers:
+  num_instances: 1
+  urls:
+    - "localhost:8002"
+```
+
+Clients then send OpenAI-compatible requests to the orchestrator (`localhost:8000`). For the full walkthrough, per-worker GPU placement, and multi-node/SLURM launch, see the [Disaggregated Serving guide](../features/disagg-serving.md).
+
 ### LLM API Options (YAML Configuration)
 
 These options provide control over TensorRT LLM's behavior and are set within the YAML file passed to the `trtllm-serve` command via the `--config` argument.
@@ -209,7 +259,7 @@ After the TensorRT LLM server is set up and shows *Application startup complete*
 curl http://localhost:8000/v1/completions \
   -H "Content-Type: application/json" \
   -d '{
-      "model": "zai-org/GLM-5-FP8",
+      "model": "zai-org/GLM-5.3",
       "prompt": "What is the capital of France?",
       "max_tokens": 16,
       "temperature": 0
@@ -222,7 +272,7 @@ Example response:
 {
   "id": "cmpl-...",
   "object": "text_completion",
-  "model": "zai-org/GLM-5-FP8",
+  "model": "zai-org/GLM-5.3",
   "choices": [
     {
       "index": 0,
@@ -264,7 +314,7 @@ result_dir=/tmp/glm5_output
 for concurrency in ${concurrency_list}; do
     num_prompts=$((concurrency * multi_round))
     python -m tensorrt_llm.serve.scripts.benchmark_serving \
-        --model zai-org/GLM-5-FP8 \
+        --model zai-org/GLM-5.3 \
         --backend openai \
         --dataset-name "random" \
         --random-input-len ${isl} \

docs/source/models/supported-models.md

@@ -24,7 +24,7 @@ The following is a table of supported models for the PyTorch backend:
 | `Gemma4AssistantForCausalLM`         | Gemma 4 MTP assistant              | `google/gemma-4-E2B-it-assistant`, `google/gemma-4-E4B-it-assistant`, `google/gemma-4-26B-A4B-it-assistant`, `google/gemma-4-31B-it-assistant` |
 | `Glm4MoeForCausalLM`                 | GLM-4.5, GLM-4.6, GLM-4.7          | `THUDM/GLM-4-100B-A10B`                      |
 | `Glm4MoeLiteForCausalLM` [^5]        | GLM-4.7-Flash                      | `zai-org/GLM-4.7-Flash`                      |
-| `GlmMoeDsaForCausalLM`               | GLM-5                              | `zai-org/GLM-5`                              |
+| `GlmMoeDsaForCausalLM`               | GLM-5, GLM-5.2, GLM-5.3            | `zai-org/GLM-5.3`                            |
 | `GraniteForCausalLM` [^5]            | Granite 3, Granite Guardian 3      | `ibm-granite/granite-3.1-8b-instruct`, `ibm-granite/granite-3.3-8b-instruct`, `ibm-granite/granite-guardian-3.2-5b` |
 | `GraniteMoeHybridForCausalLM` [^5]   | Granite 4.0 Hybrid MoE             | `ibm-granite/granite-4.0-h-small`            |
 | `GptOssForCausalLM`                  | GPT-OSS                            | `openai/gpt-oss-20b`, `openai/gpt-oss-120b`  |
@@ -72,6 +72,7 @@ Note: Support for other models may vary. Features marked "N/A" are not applicabl
 | -------------------------------- | ----------------- | ---------- | -------------------------- | --------------------- | --------------- | --- | ---------------- | ----------------- | ------ | ------------- | ---------------- | -------------- | ------------------------ | --------------------- | --------------- |
 | `DeepseekV3ForCausalLM`          | Yes               | Yes        | Yes                        | Yes                   | Yes [^1]        | Yes | No               | No                | No     | Yes           | Yes              | Yes [^2]       | N/A                      | Yes                   | Yes             |
 | `DeepseekV32ForCausalLM`         | Yes               | Yes        | Yes                        | Yes                   | Yes             | Yes | No               | No                | No     | Yes           | Yes              | Yes            | N/A                      | Yes                   | Yes             |
+| `GlmMoeDsaForCausalLM`           | Yes               | Yes        | Untested                   | Yes                   | Yes             | Yes | No               | No                | No     | Yes           | Untested         | Untested       | N/A                      | Untested              | Untested        |
 | `DeepseekV4ForCausalLM` [^11]    | Yes               | Yes        | Yes                        | Untested              | Yes             | Yes | No               | No                | No     | Yes           | Yes              | Untested       | Yes                      | Untested              | Untested        |
 | `Glm4MoeForCausalLM`             | Yes               | Yes        | Yes                        | Untested              | Yes             | Yes | No               | No                | No     | Yes           | Yes              | Untested       | N/A                      | Yes                   | Yes             |
 | `Qwen3MoeForCausalLM`            | Yes               | Yes        | Yes                        | Yes                   | Yes             | No  | Yes              | Yes               | No     | Yes           | Yes              | Yes            | N/A                      | Yes                   | Yes             |