How to Deploy GLM-5.2 Using vLLM for Production Inference

Deploy GLM-5.2 with vLLM by installing version ≥0.23.0, downloading the model from Hugging Face or ModelScope, and launching the OpenAI-compatible server with --max-model-len 1048576 and --enable-thinking to leverage the 1M-token context and optimized sparse attention kernels.

The zai-org/GLM-5 repository provides the GLM-5.2 model, a 1M-context language model optimized for long-horizon agentic tasks. Deploying GLM-5.2 using vLLM for production inference requires specific configuration of the DeepSeek Sparse Attention (DSA) kernels and reasoning effort parameters. This guide covers the complete setup process, from installation to serving the model via a REST API.

Architecture and Model Specifications

DeepSeek Sparse Attention and MoE Design

GLM-5.2 utilizes the DeepSeek Sparse Attention (DSA) architecture built on a Mixture-of-Experts (MoE) design. According to the repository's README.md at lines 25-27, the model implements IndexShare technology that reuses a single indexer across every four sparse-attention layers. This design reduces per-token FLOPs by approximately 2.9× when processing the full 1,048,576 token context.

Speculative Decoding Enhancements

The model features an enhanced Multi-Token Prediction (MTP) layer for speculative decoding. As documented in the source, this extends the acceptance length by up to 20% during generation, reducing latency for production workloads.

Prerequisites and Environment Setup

Before deploying GLM-5.2, ensure you have vLLM version 0.23.0 or higher installed. Download the model weights from Hugging Face or ModelScope (exact URLs are listed in README.md lines 61-68).

pip install vllm>=0.23.0

Deploying GLM-5.2 with vLLM

You can deploy GLM-5.2 using either the Python API or the OpenAI-compatible server endpoint.

Python API Implementation

For programmatic access, instantiate the LLM class with the appropriate configuration for GLM-5.2's 1M-token context:

import os
from vllm import LLM, SamplingParams

# Set the model checkpoint directory (downloaded from Hugging Face)

model_path = "/data/GLM-5.2"          # Adjust to your location

# Create an LLM instance – vLLM automatically loads the DSA/DSA-MoE kernels

llm = LLM(
    model=model_path,
    tensor_parallel_size=1,           # Increase for multi-GPU deployment

    max_seq_len=1_048_576,           # 1M token context supported by GLM-5.2

    enable_thinking=True,            # Keep default "max" effort

)

# Define sampling parameters

sampling_params = SamplingParams(
    temperature=0.7,
    top_p=0.9,
    max_tokens=256,
    # To request high-effort reasoning, add:

    # reasoning_effort="high"

)

# Generate a response

prompt = "Explain the benefits of using vLLM for long-context LLM inference."
outputs = llm.generate([prompt], sampling_params)

print(outputs[0].text)

OpenAI-Compatible REST Server

For production services, launch vLLM as a REST server using the OpenAI-compatible API:

python -m vllm.entrypoints.openai.api_server \
    --model /data/GLM-5.2 \
    --port 8000 \
    --max-model-len 1048576 \
    --enable-thinking \
    --reasoning-effort max   # or "high" for higher quality

Clients can then send requests to /v1/chat/completions using standard OpenAI client libraries.

Configuring Reasoning Effort and Thinking Budget

GLM-5.2 exposes a reasoning_effort parameter (max or high) and an enable_thinking flag. As specified in README.md at lines 80-81, setting reasoning_effort="high" activates a more intensive reasoning path, while enable_thinking=false disables the internal "thinking" phase entirely. These parameters control the trade-off between latency and reasoning depth.

Ascend NPU Deployment

For users targeting Huawei Ascend hardware, vLLM-Ascend, xLLM, and SGLang are supported alternatives. The repository contains specific guidance in example/ascend.md (lines 13-23) detailing the configuration steps for NPU-based inference.

Summary

• Model architecture: GLM-5.2 uses DeepSeek Sparse Attention (DSA) with MoE and IndexShare optimization, enabling 1M-token contexts with 2.9× FLOP reduction.
• vLLM requirements: Version ≥0.23.0 with CUDA kernels optimized for DSA and MoE operations.
• Key parameters: Configure max_seq_len to 1,048,576, set enable_thinking based on reasoning requirements, and adjust reasoning_effort between "max" and "high".
• Deployment modes: Use the Python API for embedded applications or the OpenAI-compatible server (vllm.entrypoints.openai.api_server) for production REST services.
• Hardware alternatives: Ascend NPU deployments are supported via vLLM-Ascend as documented in example/ascend.md.

Frequently Asked Questions

What is the maximum context length for GLM-5.2 in vLLM?

GLM-5.2 supports a context length of 1,048,576 tokens (1M tokens). When deploying with vLLM, set the --max-model-len parameter to 1048576 to utilize the full context window. The IndexShare architecture in README.md lines 25-27 enables efficient processing of these long sequences by reusing indexers across sparse-attention layers.

How do I disable the thinking phase in GLM-5.2?

To disable the internal reasoning process, set the enable_thinking parameter to false. According to README.md lines 80-81, this completely bypasses the model's "thinking" stage, reducing latency for use cases that do not require chain-of-thought reasoning. By default, enable_thinking is set to true with reasoning_effort set to "max".

Can I deploy GLM-5.2 on Ascend NPU hardware?

Yes, GLM-5.2 supports Ascend NPU deployment through vLLM-Ascend, xLLM, and SGLang. The repository provides a dedicated guide in example/ascend.md covering the specific configuration requirements for Ascend hardware. This allows organizations to run inference on Huawei AI processors using the same model weights.

What version of vLLM is required for GLM-5.2?

You must install vLLM version 0.23.0 or higher to deploy GLM-5.2. This version includes the optimized CUDA kernel stack for MoE and DeepSeek Sparse Attention (DSA) required by the model's architecture. Earlier versions lack the necessary kernel optimizations for the 1M-token context and IndexShare efficiency features.

{
  "mcpServers": {
    "instagit": {
      "command": "npx",
      "args": ["-y", "instagit@latest"]
    }
  }
}