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Introduction

GLM-5.1 is a Mixture-of-Experts (MoE) large language model developed by Z.ai, featuring 744B total parameters with 40B active parameters. It uses 256 routed experts (top-8) plus one shared expert, with Multi-head Latent Attention (MLA) and DeepSeek Sparse Attention (DSA), and a built-in multi-token prediction (MTP) head for speculative decoding. The model features built-in bilingual (Chinese-English) capabilities with a unified pre-training framework, excelling at reasoning, math, code, and tool calling tasks. GLM-5.1 supports both Thinking mode (step-by-step reasoning) and Instruct mode (direct response), with a native context window of approximately 200K tokens. This document demonstrates the deployment of GLM-5.1 on Ascend NPUs using SGLang, including single-node and multi-node deployment, feature configuration, and performance optimization. This document is validated and written based on SGLang v0.5.13. The current model (GLM-5.1) is fully supported in this version. To use the latest features (e.g., speculative decoding, multi-node deployment), it is recommended to use v0.5.13 or a later version.

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Supported features

FeatureExample usage
Tensor Parallelism--tp-size 16
Data Parallelism--dp-size 16
Expert Parallelism--ep-size 16 \
--moe-a2a-backend deepep \
--deepep-mode auto
Context Parallelism--enable-nsa-prefill-context-parallel \
--nsa-prefill-cp-mode in-seq-split \
--attn-cp-size 4
PD Disaggregation--disaggregation-mode prefill \
--disaggregation-transfer-backend ascend
Quantization--quantization modelslim
Chunked Prefillauto based on device memory, or set explicit value;
disable with --chunked-prefill-size -1; e.g. --chunked-prefill-size 16384
NPU Graphenabled by default; disable with --disable-cuda-graph;
control range via --cuda-graph-bs or --cuda-graph-max-bs; e.g. --cuda-graph-bs 1 2 3 4 5 6
Speculative Decoding--speculative-algorithm NEXTN \
--speculative-num-steps 3 \
--speculative-eagle-topk 1 \
--speculative-num-draft-tokens 4 \
--speculative-draft-model-quantization unquant
Overlap Scheduleexport SGLANG_ENABLE_OVERLAP_PLAN_STREAM=1
DP LM Head--enable-dp-lm-head
The values in the Example usage column are for illustration only. Adjust them according to your hardware, deployment mode, and workload. For parameter details, see Feature descriptions; for recommended configurations for each deployment scenario, see Best practices.
For feature compatibility and conflict information between features, see Feature Compatibility.

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Prerequisites

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Model weights

If you need to download model weights, check the model size before downloading to reserve enough space.
Ensure the available device memory exceeds the model weight size before deployment. For optimal throughput and latency, refer to the best practice configurations which may require additional nodes or cards. It is recommended to download the model weights to a shared directory across multiple nodes.

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Installation

Ensure sufficient disk space before pulling images. The Docker image requires at least 30 GB of free space.
The dependencies required for the NPU runtime environment have been integrated into a Docker image and uploaded to the online platform. You can directly pull it. Both stable releases and daily builds are available. The following command is based on the stable release tag. For details, see Docker image versions.
Command
docker pull quay.io/ascend/sglang:v0.5.13.post1-cann9.0.0-a3

docker run -itd --shm-size=16g --name ${NAME} \
--privileged=true --net=host \
-v /var/queue_schedule:/var/queue_schedule \
-v /etc/ascend_install.info:/etc/ascend_install.info \
-v /usr/local/sbin:/usr/local/sbin \
-v /usr/local/Ascend/driver:/usr/local/Ascend/driver \
-v /usr/local/Ascend/firmware:/usr/local/Ascend/firmware \
--device=/dev/davinci0:/dev/davinci0  \
--device=/dev/davinci1:/dev/davinci1  \
--device=/dev/davinci2:/dev/davinci2  \
--device=/dev/davinci3:/dev/davinci3  \
--device=/dev/davinci4:/dev/davinci4  \
--device=/dev/davinci5:/dev/davinci5  \
--device=/dev/davinci6:/dev/davinci6  \
--device=/dev/davinci7:/dev/davinci7  \
--device=/dev/davinci8:/dev/davinci8  \
--device=/dev/davinci9:/dev/davinci9  \
--device=/dev/davinci10:/dev/davinci10  \
--device=/dev/davinci11:/dev/davinci11  \
--device=/dev/davinci12:/dev/davinci12  \
--device=/dev/davinci13:/dev/davinci13  \
--device=/dev/davinci14:/dev/davinci14  \
--device=/dev/davinci15:/dev/davinci15  \
--device=/dev/davinci_manager:/dev/davinci_manager \
--device=/dev/hisi_hdc:/dev/hisi_hdc \
--entrypoint=bash \
quay.io/ascend/sglang:v0.5.13.post1-cann9.0.0-a3
  • If the model weights have already been downloaded to a shared directory, use -v to mount the model path into the container, for example: -v /path/to/models:/models.
  • Replace ${NAME} with your own container name or remove --name to use default name.

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Online service deployment

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Multi-node PD mixed deployment

Multi-node deployment distributes the model across multiple Atlas 800I A3 nodes using tensor parallelism while keeping prefill and decode on the same nodes (PD mixed mode), suitable for scenarios that need more device memory than a single node can provide. This scenario is already covered in the best practice. For the complete, optimized deployment commands and benchmark data, see GLM-5.1 Best Practice — Multi-node PD Mixed On A3.

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Multi-node PD disaggregation deployment

PD disaggregation splits the prefill and decode stages onto separate nodes, reducing interference and improving throughput for high-concurrency scenarios. This scenario is already covered in the best practice. For the complete, optimized deployment commands and benchmark data, see GLM-5.1 Best Practice — PD Disaggregation On A3.

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Functional verification

After the service is started, you can invoke the model by sending a prompt: 
# ============================================================
# Before running, update the following variables:
#   HOST: the server host address (e.g., localhost)
#   PORT: the server port number (e.g., 6689)
# ============================================================

curl http://${HOST}:${PORT}/generate \
    -H "Content-Type: application/json" \
    -d '{
        "text": "What is the capital of France?",
        "sampling_params": {
            "max_new_tokens": 64,
            "temperature": 0
        }
    }'
Expected result: an HTTP 200 response with the generated text containing “Paris”. Once the server prints The server is fired up and ready to roll! in the logs, it is ready to accept requests. For more testing examples (Health Check, Generate, Chat Completions, and port usage guidance), see Testing the Service.

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Accuracy evaluation

For accuracy evaluation methods and datasets, see Accuracy Evaluation on Ascend NPU.

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Performance

For performance data and benchmark commands, see Performance Testing on Ascend NPU.

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Best practices

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Best practice configuration reference

For complete optimal configurations with deployment scripts and benchmark commands, see the GLM-5.1 Best Practice page.

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Performance tuning

For the full list of supported features, see Supported features. For detailed optimization guidance, see Optimization on Ascend NPU.

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FAQ

For common environment, installation, and general parameter issues, please refer to the Ascend NPU FAQ. This section only covers model-specific issues.