> ## Documentation Index
> Fetch the complete documentation index at: https://docs.sglang.io/llms.txt
> Use this file to discover all available pages before exploring further.

# Qwen3.8-Max

## Introduction

Qwen3.8-Max (published as Qwen3.8-2.4T-A95B) is a Mixture-of-Experts (MoE) model with 2.4 trillion total
parameters and 95B activated per token. It uses a 92-layer hybrid architecture that combines Gated Delta
Network (GDN) linear-attention with full-attention layers. The model has a native context length of 262,144
tokens (extensible to over 1 million) and built-in multi-token prediction (MTP) weights for speculative
decoding.

This document demonstrates the deployment of Qwen3.8-Max on Ascend NPUs using SGLang, including multi-node PD
mixed mode, feature configuration, and performance optimization.

Qwen3.8-Max is newly released. This document is validated and written based on the **SGLang main branch (daily
build)**. It is recommended to use the latest daily build Docker image, or build SGLang from the main branch
source.

## Supported features

| Feature            | Example usage                                                                                                                                                 |
| ------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| Tensor Parallelism | `--tp-size 64`                                                                                                                                                |
| Data Parallelism   | `--enable-dp-attention \`<br />`--dp-size 4`                                                                                                                  |
| Expert Parallelism | `--moe-a2a-backend deepep \`<br />`--deepep-mode auto`                                                                                                        |
| Quantization       | `--quantization modelslim`                                                                                                                                    |
| Chunked Prefill    | auto based on device memory, or set explicit value;<br />disable with `--chunked-prefill-size -1`; e.g., `--chunked-prefill-size 8192`                        |
| NPU Graph          | enabled by default; disable with `--disable-cuda-graph`;<br />control range via `--cuda-graph-bs` or `--cuda-graph-max-bs-decode`; e.g., `--cuda-graph-bs 16` |
| Overlap Schedule   | `export SGLANG_ENABLE_OVERLAP_PLAN_STREAM=1`                                                                                                                  |
| DP LM Head         | `--enable-dp-lm-head`                                                                                                                                         |

<Note>
  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](/docs/hardware-platforms/ascend-npus/optimization/parameter_tuning#feature-descriptions); for
  recommended configurations for each deployment scenario, see [Best practices](#best-practices).
</Note>

For feature compatibility and conflict information between features,
see [Feature Compatibility](/docs/hardware-platforms/ascend-npus/optimization/parameter_tuning#feature-compatibility).

## Prerequisites

### Environment

Before following this tutorial, complete the environment setup in the documents below:

* [Ascend NPU Quickstart](/docs/hardware-platforms/ascend-npus/getting-started/quick_start) — the fastest way to get started.
  It walks you through launching the official container image, starting the SGLang server, and sending a test request.
  Recommended if you are new to SGLang on Ascend.
* [SGLang Installation with NPU Support](/docs/hardware-platforms/ascend-npus/getting-started/installation) — the full installation guide.
  It covers the component version mapping (CANN, TorchNPU, Triton, kernels, etc.), building from source or from a
  Dockerfile, and recommended system settings (CPU power scheme, NUMA, swap). Use it when you need to install or customize
  the environment instead of using the official image.

### Model weights

Before downloading model weights, check the model size to reserve enough disk space.
For multi-node deployment, download the weights to a shared directory accessible to all nodes.

* [Qwen/Qwen3.8-2.4T-A95B](https://www.modelscope.cn/models/Qwen/Qwen3.8-2.4T-A95B) (BF16, approximately 4.8TB)
* You can use [msmodelslim](https://gitcode.com/Ascend/msmodelslim) to quantize a W4A8 variant from the BF16 checkpoint.

<Info>
  We recommend deploying the W4A8 variant for reduced resource usage and higher throughput.
  The validated configuration in this tutorial deploys the W4A8 variant on 4 Atlas 800I A3 nodes (`--tp-size 64`, 64 dies
  in total). The BF16 checkpoint alone weighs approximately 4.8TB and requires additional nodes.
</Info>

For the hardware specifications (memory per die, dies per card),
see [Ascend NPU Reference — Hardware](/docs/hardware-platforms/ascend-npus/reference/glossary#hardware).

## Installation

<Warning>
  Ensure sufficient disk space before pulling images. The Docker image requires at least **30GB** of free space.
</Warning>

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.

The following command is based on the daily build tag, which contains the latest SGLang main branch changes.
For details, see [Docker image versions](/docs/hardware-platforms/ascend-npus/faq#8-docker-image-versions-stable-release-vs-daily-build).

<Tabs>
  <Tab title="Atlas 800I A3">
    ```bash Command theme={null}
    docker pull quay.io/ascend/sglang:main-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:main-cann9.0.0-a3
    ```
  </Tab>
</Tabs>

<Tip>
  * 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.
</Tip>

## Online service deployment

### Multi-node online 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. The validated configuration deploys the W4A8 quantized checkpoint on 4 nodes with `--tp-size 64`
(16 dies per node), DP attention (`--dp-size 4`), and DeepEP in `auto` mode.

Modify the IP addresses of the four nodes, then run the same script on all four nodes. Each node determines its own
rank by matching the local IP address against `IPS`.

```shell theme={null}
# ============================================================
# Before running, update the following variables:
#   IPS: IP addresses of each node in the cluster
#   MODEL_PATH: path to the model weights directory
#   HCCL_SOCKET_IFNAME: network interface name for HCCL
#   GLOO_SOCKET_IFNAME: network interface name for Gloo
# ============================================================

# high performance cpu
echo performance | tee /sys/devices/system/cpu/cpu*/cpufreq/scaling_governor
sysctl -w vm.swappiness=10
sysctl -w kernel.numa_balancing=0
sysctl -w kernel.sched_migration_cost_ns=50000
# bind cpu
export SGLANG_SET_CPU_AFFINITY=1
export SGLANG_ONE_VISIBLE_DEVICE_PER_PROCESS=1

unset https_proxy
unset http_proxy
unset HTTPS_PROXY
unset HTTP_PROXY
unset ASCEND_LAUNCH_BLOCKING
# cann
source /usr/local/Ascend/ascend-toolkit/set_env.sh
source /usr/local/Ascend/nnal/atb/set_env.sh

export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
export STREAMS_PER_DEVICE=32

# deepep
export DEEP_NORMAL_MODE_USE_INT8_QUANT=1
export SGLANG_DEEPEP_NUM_MAX_DISPATCH_TOKENS_PER_RANK=128
export HCCL_BUFFSIZE=2300
export DEEPEP_NORMAL_LONG_SEQ_ROUND=64
export DEEPEP_NORMAL_LONG_SEQ_PER_ROUND_TOKENS=512
export HCCL_OP_EXPANSION_MODE=AIV

# Run command ifconfig on each node, find out which inet addr has same IP with your node IP. That is your public interface, which should be added here
export HCCL_SOCKET_IFNAME=<network-interface>
export GLOO_SOCKET_IFNAME=<network-interface>

IPS=('<your node1 ip>' '<your node2 ip>' '<your node3 ip>' '<your node4 ip>')
IP_MASTER="${IPS[0]}:5000"

MODEL_PATH=/path/to/model-weights

LOCAL_HOST1=`hostname -I|awk -F " " '{print$1}'`
LOCAL_HOST2=`hostname -I|awk -F " " '{print$2}'`
for i in "${!IPS[@]}";
do
    if [[ "$LOCAL_HOST1" == "${IPS[$i]}" || "$LOCAL_HOST2" == "${IPS[$i]}" ]];
    then
        echo "${IPS[$i]}"

        # overlap schedule
        export SGLANG_ENABLE_OVERLAP_PLAN_STREAM=1
        export SGLANG_ENABLE_SPEC_V2=1
        export SGLANG_RAGGED_VERIFY_MODE=static

        sglang serve \
            --model-loader-extra-config '{"enable_multithread_load": true}' \
            --dist-init-addr $IP_MASTER --nnodes 4 --node-rank $i \
            --model-path $MODEL_PATH \
            --tokenizer-path $MODEL_PATH \
            --trust-remote-code \
            --attention-backend ascend \
            --device npu \
            --quantization modelslim \
            --dtype bfloat16 \
            --tp-size 64 \
            --enable-dp-attention --dp-size 4 --enable-dp-lm-head \
            --mem-fraction-static 0.8 \
            --chunked-prefill-size 8192 \
            --cuda-graph-bs 16 \
            --disable-radix-cache \
            --max-running-requests 64 \
            --host 0.0.0.0 \
            --port 30000 \
            --moe-a2a-backend deepep \
            --deepep-mode auto \
            --watchdog-timeout 9000
        break
    fi
done
```

## Functional verification

After the service is started, you can invoke the model by sending a prompt:

```shell theme={null}
# ============================================================
# Before running, update the following variables:
#   HOST: the server host address (e.g., localhost)
#   PORT: the server port number (e.g., 30000)
# ============================================================

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](/docs/hardware-platforms/ascend-npus/getting-started/installation#testing-the-service).

## Accuracy evaluation

For accuracy evaluation methods and datasets, see [Accuracy Evaluation on Ascend NPU](/docs/hardware-platforms/ascend-npus/evaluation/accuracy_evaluation).

## Performance

For performance data and benchmark commands, see [Performance Testing on Ascend NPU](/docs/hardware-platforms/ascend-npus/evaluation/performance_testing).

## Best practices

### Best practice configuration reference

Qwen3.8-Max has no standalone best practice page yet, as tuning parameters are not finalized. Instead, the
[Online service deployment](#online-service-deployment) section above provides a ready-to-use script for multi-node
deployment, which embeds the recommended feature combinations and tuning parameters (e.g., DP attention, DeepEP,
overlap schedule).

For the full catalog of optimization features and their parameter and compatibility details,
see [Optimization on Ascend NPU](/docs/hardware-platforms/ascend-npus/optimization/parameter_tuning).

## Performance tuning

For the full list of supported features, see [Supported features](#supported-features). For detailed optimization
guidance, see [Optimization on Ascend NPU](/docs/hardware-platforms/ascend-npus/optimization/parameter_tuning).

## FAQ

For common environment, installation, and general parameter issues, please refer to the [Ascend NPU FAQ](/docs/hardware-platforms/ascend-npus/faq).