Deploying GLM-5 for Production Inference Using vLLM

Deploy GLM-5 (GLM-S) for production inference using vLLM's optimized CUDA kernels for DeepSeek Sparse Attention and Mixture-of-Experts, supporting 1M token contexts with configurable reasoning budgets.

The GLM-5 series (also referred to as GLM-S) in the zai-org/GLM-5 repository represents a family of large language models optimized for long-horizon, agentic tasks. Deploying GLM-5 for production inference using vLLM provides access to highly optimized CUDA kernels, speculative decoding, and efficient memory management specifically designed for the model's architecture. This guide covers the technical implementation based on the official source code and documentation.

GLM-5.2 Architecture Overview

GLM-5.2 utilizes the DeepSeek Sparse Attention (DSA) architecture combined with a Mixture-of-Experts (MoE) design. According to the repository's README.md (lines 25-27), the implementation features IndexShare technology that reuses a single indexer across every four sparse-attention layers, reducing per-token FLOPs by approximately 2.9× at 1M context length.

The architecture also includes an enhanced MTP (Multi-Token Prediction) layer for speculative decoding, extending acceptance length by up to 20%.

Prerequisites and Model Preparation

Before deploying GLM-5 for production inference using vLLM, ensure you have vLLM 0.23.0 or higher installed. Download the GLM-5 model checkpoints from Hugging Face or ModelScope—the README.md (lines 61-68) contains the exact repository URLs for the official model weights.

Production Deployment Methods

vLLM supports two primary deployment patterns for GLM-5: direct Python API integration and OpenAI-compatible server mode.

Python API Implementation

For programmatic access, instantiate the LLM class with specific parameters to enable GLM-5's thinking capabilities and 1M token context:

from vllm import LLM, SamplingParams

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

model_path = "/data/GLM-5.2"

# Initialize vLLM with GLM-5.2 specific settings

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

)

# Configure 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 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 Server Mode

For production services, launch vLLM as a REST endpoint:

python -m vllm.entrypoints.openai.api_server \
    --model /data/GLM-5.2 \
    --port 8000 \
    --max-model-len 1048576 \
    --enable-thinking \
    --reasoning-effort max

This exposes the standard /v1/chat/completions endpoint for client integration.

Configuring Reasoning Effort and Thinking Budgets

GLM-5 exposes granular control over its reasoning process through the reasoning_effort and enable_thinking parameters. As documented in README.md (lines 80-81), the reasoning_effort parameter accepts "max" (default) or "high" to trade latency for deeper reasoning, while enable_thinking=false disables the internal thinking phase entirely.

Ascend NPU Deployment

For deployments targeting Huawei Ascend hardware, the repository provides specific guidance in example/ascend.md (lines 13-23). Supported frameworks include vLLM-Ascend, xLLM, and SGLang, each offering optimized kernels for the Ascend NPU architecture.

Summary

• GLM-5 (GLM-S) features DeepSeek Sparse Attention with IndexShare optimization, achieving 2.9× FLOP reduction at 1M context length according to README.md (lines 25-27).
• vLLM 0.23.0+ provides optimized CUDA kernels for the MoE and DSA architecture.
• Deploy via Python API or OpenAI-compatible server mode with --max-model-len 1048576 for full context support.
• Control reasoning depth using reasoning_effort ("max" or "high") and enable_thinking parameters as documented in README.md (lines 80-81).
• Ascend NPU users should consult example/ascend.md for specialized deployment instructions.

Frequently Asked Questions

What is the maximum context length supported when deploying GLM-5 with vLLM?

GLM-5.2 supports a 1 million token context length (1,048,576 tokens) when deployed with vLLM. Configure this via the max_seq_len parameter in Python or --max-model-len 1048576 in server mode to utilize the full context window.

How does the reasoning_effort parameter affect GLM-5 inference?

The reasoning_effort parameter controls the depth of the model's internal reasoning. Setting it to "max" provides the default reasoning level, while "high" activates a more intensive reasoning path that increases latency but improves output quality. Set enable_thinking=false to disable the internal reasoning phase entirely.

Can GLM-5 run on non-NVIDIA hardware?

Yes. While the primary vLLM deployment uses CUDA kernels, the zai-org/GLM-5 repository supports Ascend NPU hardware through vLLM-Ascend, xLLM, and SGLang, as detailed in example/ascend.md (lines 13-23).

What version of vLLM is required for GLM-5 deployment?

You must use vLLM 0.23.0 or higher to properly support the DeepSeek Sparse Attention kernels and Mixture-of-Experts routing required for GLM-5's architecture.

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