sglang/docs/cookbook/autoregressive/GLM/GLM-5.3.mdx

---
title: GLM-5.3
description: "Deploy GLM-5.3 with SGLang — Z.ai's DeepSeek-Sparse-Attention (DSA) Mixture-of-Experts model with MTP speculative decoding and 1M context, on H200, B200, B300, GB300, and AMD MI300X/MI325X/MI355X."
tag: NEW
---

## Deployment

<a id="install" />

<Accordion title="Install SGLang">

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.

<Tabs>

<Tab title="Python (pip / uv)">

```bash Command
pip install --upgrade pip
pip install uv
uv pip install --prerelease=allow sglang
```

Then run the **Python** output of the command panel below in that environment.

</Tab>

<Tab title="Docker">

```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.

</Tab>

</Tabs>

</Accordion>

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-5.3.jsx";
import { benchmarks } from "/src/snippets/configs/zai-org/glm-5.3-benchmarks.jsx";

<Deployment config={config} benchmarks={benchmarks} />

<Warning>
  All recipes here run the DSA indexer top-k on the default `--dsa-topk-backend sgl-kernel`. Other top-k backend choices have not been fully validated on GLM-5.3.
</Warning>

## Playground

The Playground is where you experiment with **SGLang features beyond the deployment matrix**. It lets you turn on additional knobs on top of whichever cell the Deploy panel is currently showing.

import { Playground } from "/src/snippets/_playground.jsx";

<Playground config={config} />

## 1. Model Introduction

**GLM-5.3** 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 keeps the same base architecture while updating post-training for complex coding and long-horizon tasks. It ships in two precisions — **FP8** (`zai-org/GLM-5.3`) and full **BF16** (`zai-org/GLM-5.3-BF16`) — 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. For Blackwell, RadixArk publishes an **experimental NVFP4** build (`RadixArk/GLM-5.3-NVFP4`, Model Optimizer) that quantizes only the routed experts' linear weights and activations to 4-bit (attention, shared experts, dense layers, MTP, embeddings, and the LM head stay unquantized), cutting the weight footprint to ~0.45 TB so a 4-GPU GB300 node can serve it with TP4. **The NVFP4 cells are experimental** — benchmark data (accuracy / throughput) is still pending; treat them as provisional until the benchmark cards fill in.

<table style={{width: "100%", borderCollapse: "collapse", tableLayout: "fixed"}}>
  <thead>
    <tr style={{borderBottom: "2px solid #d55816"}}>
      <th style={{textAlign: "left",  padding: "10px 12px", fontWeight: 700}}>Model</th>
      <th style={{textAlign: "left",  padding: "10px 12px", fontWeight: 700}}>Architecture</th>
      <th style={{textAlign: "left",  padding: "10px 12px", fontWeight: 700}}>Context</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td style={{padding: "9px 12px"}}><strong><a href="https://huggingface.co/zai-org/GLM-5.3">GLM-5.3</a></strong></td>
      <td style={{padding: "9px 12px"}}>MoE · DSA · 256 experts (top-8) · MTP · FP8</td>
      <td style={{padding: "9px 12px", textAlign: "right"}}>1,048,576</td>
    </tr>
    <tr>
      <td style={{padding: "9px 12px"}}><strong><a href="https://huggingface.co/zai-org/GLM-5.3-BF16">GLM-5.3-BF16</a></strong></td>
      <td style={{padding: "9px 12px"}}>MoE · DSA · 256 experts (top-8) · MTP · BF16</td>
      <td style={{padding: "9px 12px", textAlign: "right"}}>1,048,576</td>
    </tr>
    <tr>
      <td style={{padding: "9px 12px"}}><strong><a href="https://huggingface.co/RadixArk/GLM-5.3-NVFP4">GLM-5.3-NVFP4</a></strong></td>
      <td style={{padding: "9px 12px"}}>MoE · DSA · 256 experts (top-8) · MTP · NVFP4</td>
      <td style={{padding: "9px 12px", textAlign: "right"}}>1,048,576</td>
    </tr>
  </tbody>
</table>

**Recommended generation:** `temperature=1.0`, `top_p=0.95` (the checkpoint's `generation_config.json` defaults; informational — do not hardcode in client code).

**Resources:** [GLM-5.3](https://huggingface.co/zai-org/GLM-5.3) · [GLM-5.3-BF16](https://huggingface.co/zai-org/GLM-5.3-BF16) · [GLM-5.3-NVFP4](https://huggingface.co/RadixArk/GLM-5.3-NVFP4).

## 2. Configuration Tips

- **DeepSeek Sparse Attention (DSA).** GLM-5.3 uses the `glm_moe_dsa` architecture; SGLang auto-selects the DSA attention backends (`flashmla_sparse` prefill, `fa3` decode, `sgl-kernel` indexer topk). No attention-backend flag is needed on the supported hardware. SGLang also auto-selects the KV-cache dtype for DSA models — `fp8_e4m3` on Blackwell (B200/GB300/B300, which then routes DSA through the TensorRT-LLM backend) and `bf16` on Hopper (H200) — so no `--kv-cache-dtype` flag is required. On Hopper, pairing `--kv-cache-dtype fp8_e4m3` with `--dsa-prefill-backend flashmla_sparse_q8 --dsa-decode-backend flashmla_kv` selects the native FP8 sparse prefill kernel (computes directly on the fp8 KV cache with no fp8→bf16 dequantization round-trip; GLM-5.3's 64 query heads match the kernel's native tile) — see the [DeepSeek-V3.2 page](../DeepSeek/DeepSeek-V3_2) for kernel details; the optional `SGLANG_ENABLE_DSA_Q8KV8_*` performance env vars are documented in `python/sglang/srt/environ.py`.
- **MTP / speculative decoding.** The checkpoint ships one nextn layer. Enable EAGLE MTP for lower latency (`--speculative-algorithm EAGLE --speculative-num-steps 5 --speculative-eagle-topk 1 --speculative-num-draft-tokens 6` for low-latency; `1-1-2` for balanced). The config's `index_share_for_mtp_iteration` reuses the DSA indexer's topk across draft steps (effective only at `--speculative-eagle-topk 1`). Watch the server's reported **accept length** and adjust `--speculative-num-steps` / `--speculative-num-draft-tokens`: lower the draft length when rejected draft tokens create excess verification work.
- **DFlash2 (block-diffusion draft).** The **Speculative** card in the [Playground above](#playground) also offers **DFlash2**, which replaces the in-checkpoint MTP layer with the separately trained block-diffusion drafter [`incoai/GLM-5.3-DFlash2`](https://huggingface.co/incoai/GLM-5.3-DFlash2). It proposes a whole block per step and the target verifies the block in one forward pass, so output quality stays the target's. The block size — 8, i.e. 7 draft tokens per verification step — comes from the draft checkpoint's own `dflash_config`, so no `--speculative-num-draft-tokens` is passed; the draft is a small dense model and runs on `fa4` instead of the target's DSA backends. Note that DFLASH runs on **CUDA/NPU only** and rejects **DP-Attention**, so turn DP-Attention off in the **Attention** card before selecting it on a high-throughput base. The draft repository is public but licensed CC BY-NC-ND 4.0 for research and evaluation.
- **Memory.** The FP8 weights are large (MoE total, not active params). Start around `--mem-fraction-static 0.8` on H200 (TP8) and tune up; raise it for the 4-GPU GB300 single-node layout (TP4).
- **DP-Attention + DeepEP** for the balanced/high-throughput strategies spreads attention across data-parallel ranks and routes MoE through DeepEP.
- **BF16 weights need more GPUs.** The full-precision build (`zai-org/GLM-5.3-BF16`, ~1.5 TB) does not fit a single 8×H200 / 8×B200 / 4×GB300 node. It fits single-node on **8×B300** (TP8, ~2.1 TB HBM); on the smaller GPUs it needs a **multi-node** layout (e.g. 2×8×H200 or 2×8×B200 at TP16, 2×4×GB300 at TP8). FP8 is the recommended deployment. Use the same DSA / MTP / chunked-prefill guidance as FP8.
- **PD Disaggregation (prefill/decode).** GLM-5.3 is a DSA model and runs under prefill/decode disaggregation — toggle the **PD Disagg** card in the [Playground above](#playground) (pick a Prefill/Decode role + transfer backend, then front the roles with `sglang_router.launch_router --pd-disaggregation`). The Mooncake backend **auto-detects the InfiniBand HCA**, so no device flag is needed by default; only add `--disaggregation-ib-device mlx5_0` (your NIC) if auto-detection picks the wrong device or KV transfer fails to connect. On H200 Docker, expose the IB HCAs to the container (`--privileged --ulimit memlock=-1`, or `--device /dev/infiniband:/dev/infiniband --cap-add IPC_LOCK`) — without IB exposure Mooncake silently falls back to TCP.
- **Chunked-prefill size is regime-dependent.** For long-input balanced workloads, start with `--chunked-prefill-size 32768` and tune it together with `--max-running-requests` for your input length and KV capacity. Keep the default chunked-prefill size for the high-throughput recipe unless profiling shows that prefill is the bottleneck.

- **AMD GPUs (MI300X / MI325X / MI355X).** FP8 (`zai-org/GLM-5.3`) runs single-node at `tp=8` on all three. BF16 (`zai-org/GLM-5.3-BF16`, ~1.51 TB) only fits single-node on **MI325X** (2 TB HBM) and **MI355X** (2.3 TB); **MI300X** (1.5 TB) cannot hold the BF16 weights plus KV cache on one node, so use FP8 there. Use the DSA tilelang backend (`--dsa-prefill-backend tilelang --dsa-decode-backend tilelang`) and add `--chunked-prefill-size 131072` plus `--watchdog-timeout 1200` (20 min for weight loading). GLM-5.3 and DeepSeek-V3.2 share the same model structure; for other DSA / HiSparse tips see the [DeepSeek-V3.2 cookbook](../DeepSeek/DeepSeek-V3_2).
- **MTP / EAGLE speculative decoding** is disabled for AMD in the Deploy panel because the gfx950 spec-decode draft kernel is not yet validated on this hardware (and at `--speculative-num-steps > 3` hits a separate build issue). Until MTP is validated on gfx950, omit the `--speculative-*` flags and serve without MTP.

## 3. Advanced Usage

### 3.1 Reasoning

GLM-5.3 is a reasoning model, and generated commands enable `--reasoning-parser auto` (which resolves to `glm45` for GLM-5.3) by default so thinking is separated from the final answer — thinking lands in `message.reasoning_content`, the answer in `message.content`. Without the parser the server returns the thinking and the answer as one `content` string with a stray `</think>` between them, because the chat template opens `<think>` in the generation prompt. You can disable **Reasoning Parser** in the **Parsers** card of the [Playground above](#playground) when an integration needs that raw format. The chat template defaults `clear_thinking` to `false`; for multi-turn chat, pass `chat_template_kwargs: {"clear_thinking": True}` so previous reasoning is cleared before the next response.

**Reasoning effort.** Pass `chat_template_kwargs: {"reasoning_effort": ...}` to select `low`, `high`, or `max`. If you omit it or pass another value, the template uses `max`.

| `reasoning_effort` | Injected system line | Effect |
|---|---|---|
| *(not passed / unset)*, `"max"` | `Reasoning Effort: Max` | default — highest reasoning effort |
| `"high"` | `Reasoning Effort: High` | high reasoning effort |
| `"low"` | `Reasoning Effort: Low` | low reasoning effort |
| any other value | `Reasoning Effort: Max` | falls back to the default |

<Accordion title="Reasoning Example (Python)">

```python Example
from openai import OpenAI

client = OpenAI(base_url="http://localhost:30000/v1", api_key="EMPTY")
resp = client.chat.completions.create(
    model="zai-org/GLM-5.3",
    messages=[{"role": "user", "content": "What is 15% of 240?"}],
    extra_body={"chat_template_kwargs": {"clear_thinking": True, "reasoning_effort": "high"}},
)
msg = resp.choices[0].message
print("Reasoning:", getattr(msg, "reasoning_content", None))
print("Answer:", msg.content)
```

</Accordion>

<Accordion title="Example Output">

```text Output
Reasoning: 1.  **Identify the core question:** The user wants to find 15% of 240.
2.  **Convert the percentage to a decimal:** 15% = 0.15
3.  **Multiply by the total:** 0.15 * 240 = 36
    (Quick mental math: 10% of 240 = 24; 5% = 12; 24 + 12 = 36.)

Answer: 15% of 240 is **36**.

Here is how you can calculate it:
0.15 × 240 = 36
```

</Accordion>

### 3.2 Tool Calling

Generated commands enable `--tool-call-parser auto` by default, so structured calls are returned in `message.tool_calls` with `finish_reason: "tool_calls"`. `auto` resolves to **`glm47`** for GLM-5.3: the model emits the newer `<tool_call>…<arg_key>…<arg_value>…` format, which the older `glm45` parser does not parse (the call would be left as raw text in `content`). Running with no tool-call parser fails the same way, and `finish_reason` stays `"stop"`, so an agent loop never sees the call. You can disable **Tool Call Parser** in the **Parsers** card of the [Playground above](#playground) when tool calling is not needed. On thinking mode the turn also fills `reasoning_content`, so print both fields.

<Accordion title="Tool Calling Example (Python)">

```python Example
from openai import OpenAI

client = OpenAI(base_url="http://localhost:30000/v1", api_key="EMPTY")
tools = [{
    "type": "function",
    "function": {
        "name": "get_weather",
        "description": "Get the current weather for a city",
        "parameters": {
            "type": "object",
            "properties": {"city": {"type": "string"}},
            "required": ["city"],
        },
    },
}]
resp = client.chat.completions.create(
    model="zai-org/GLM-5.3",
    messages=[{"role": "user", "content": "What's the weather in Paris?"}],
    tools=tools,
)
msg = resp.choices[0].message
print("Reasoning:", getattr(msg, "reasoning_content", None))
print("Tool calls:", msg.tool_calls)
```

</Accordion>

<Accordion title="Example Output">

```text Output
Reasoning: The user wants to know the weather in Paris. I'll call the get_weather function with "Paris" as the city.

Tool calls: [
  {
    "id": "call_13fcd52146934b7781d06d4a",
    "type": "function",
    "function": {"name": "get_weather", "arguments": "{\"city\": \"Paris\"}"}
  }
]
```

</Accordion>

### 3.3 HiCache (Hierarchical KV Caching)

For long-context, prefix-heavy workloads, enable hierarchical KV caching to spill cold KV blocks to host memory (toggle the **Hierarchical KV Cache** card in the [Playground above](#playground)). Useful given GLM-5.3's 1M-token window; pair `--hicache-ratio` with a write policy that matches your reuse pattern.

### 3.4 Claude Code Integration

GLM-5.3's strong reasoning + tool-calling makes it a good backend for [Claude Code](https://code.claude.com/docs/en/overview), Anthropic's agentic CLI. SGLang exposes the Anthropic-compatible `/v1/messages` endpoint on every server, so Claude Code can talk to a GLM-5.3 server with only environment variables — no code change. Launch the server with `--reasoning-parser auto --tool-call-parser auto` (any recipe from the Deployment panel above works), then:

```bash Command
export ANTHROPIC_BASE_URL="http://127.0.0.1:30000"
export ANTHROPIC_AUTH_TOKEN="dummy"
export API_TIMEOUT_MS="3000000"
export CLAUDE_CODE_AUTO_COMPACT_WINDOW="1000000"
export CLAUDE_CODE_DISABLE_NONESSENTIAL_TRAFFIC=1
export CLAUDE_CODE_ATTRIBUTION_HEADER=0
export ANTHROPIC_DEFAULT_HAIKU_MODEL="glm-5.3[1m]"
export ANTHROPIC_DEFAULT_SONNET_MODEL="glm-5.3[1m]"
export ANTHROPIC_DEFAULT_OPUS_MODEL="glm-5.3[1m]"
claude
```

Two of these matter specifically for GLM-5.3:

- **`CLAUDE_CODE_ATTRIBUTION_HEADER=0`** — Claude Code prepends a per-request attribution block to the system prompt. GLM-5.3's chat template renders `tools` **before** `system`, so that per-request hash is the first token to diverge between turns and the radix prefix cache re-prefills the whole system + history every turn. This env removes the block and restores prefix-cache reuse.
- **`glm-5.3[1m]`** as the model name — the `[1m]` suffix is the client-side hint that enables Claude Code's 1M-context beta, matching GLM-5.3's 1,048,576-token window. Without it, context is capped well below 1M. SGLang does not validate the `model` field, so any name is accepted server-side.

For the full setup (streaming, tool-use, count_tokens, persisting env in `~/.claude/settings.json`, troubleshooting), see [Anthropic-Compatible API](../../../docs/basic_usage/anthropic_api).

### 3.5 Context Parallelism

<Warning>
Zigzag prefill CP (`--cp-strategy zigzag`) is temporarily unavailable for GLM-5.3. For prefill CP on CUDA, use `interleave` with `--dp 1` as shown below.
</Warning>

Prefill context parallelism can help with reduction of TTFT under long context. To enable prefill context parallelism for GLM 5.3, please append the following arguments:
```bash
--attn-cp-size 8 \
--enable-prefill-cp \
--cp-strategy interleave \
```
which splits the sequence equally across `--attn-cp-size` ranks during attention forward. The trade off for prefill CP is that it will introduce extra all-gather operation before indexer-topk and attention kernels, so it will increase latency for decode (in unified deployment) or short prefill.

When deploying with PD Disaggregation, a **prefill worker using the Mooncake transfer backend** can enable the [LayerSplit](https://z.ai/blog/scaling-pain) technique with
```bash
--enable-dsa-cache-layer-split \
--enable-prefill-cp \
--attn-cp-size 8 \
--cp-strategy interleave \
```
With LayerSplit, the kv cache on each rank can be sharded over the CP attention group, and prefetched when necessary. This can reduce kv cache memory by up to 75%, thus increasing the throughput on prefill side.