> ## 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. # DeepSeek-V3.2 ## Introduction DeepSeek-V3.2 is a Mixture-of-Experts (MoE) large language model developed by DeepSeek, featuring 685B total parameters with 37B active parameters. It employs Multi-head Latent Attention (MLA) and DeepSeekMoE architecture, with built-in multi-token prediction (MTP) for speculative decoding. DeepSeek-V3.2 introduces the DeepSeek Sparse Attention (DSA) mechanism, a fine-grained sparse attention mechanism powered by a lightning indexer, enabling significant efficiency improvements in long-context scenarios. This document demonstrates the deployment of DeepSeek-V3.2 on Ascend NPUs using SGLang, including multi-node PD disaggregation mode, feature configuration, and performance optimization. This document is validated and written based on **SGLang v0.5.13**. The current model (DeepSeek-V3.2) is fully supported in this version. To use the latest features (e.g., PD disaggregation, speculative decoding, DSA context parallel), it is recommended to use v0.5.13 or a later version. ## Supported features | Feature | Example usage | | -------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | Tensor Parallelism | `--tp-size 32` | | Data Parallelism | `--dp-size 8` | | Expert Parallelism | `--ep-size 32 \`
`--moe-a2a-backend deepep \`
`--deepep-mode low_latency` | | Context Parallelism | `--enable-nsa-prefill-context-parallel \`
`--nsa-prefill-cp-mode in-seq-split \`
`--attn-cp-size 32` | | PD Disaggregation | `--disaggregation-mode prefill \`
`--disaggregation-transfer-backend ascend` | | Quantization | `--quantization modelslim` | | Speculative Decoding | `--speculative-algorithm NEXTN \`
`--speculative-num-steps 3 \`
`--speculative-eagle-topk 1 \`
`--speculative-num-draft-tokens 4` | | NPU Graph | enabled by default; disable with `--disable-cuda-graph`;
control range via `--cuda-graph-bs` or `--cuda-graph-max-bs-decode`; e.g., `--cuda-graph-bs 1 2 3 4 5 6 7 8` | | Overlap Schedule | `export 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](/docs/hardware-platforms/ascend-npus/ascend_npu_optimization#feature-descriptions); for recommended configurations for each deployment scenario, see [Best practices](#best-practices). For feature compatibility and conflict information between features, see [Feature Compatibility](/docs/hardware-platforms/ascend-npus/ascend_npu_optimization#feature-compatibility). ## Prerequisites ### Environment Before following this tutorial, complete the environment setup in the documents below: * [Ascend NPU Quickstart](/docs/hardware-platforms/ascend-npus/ascend_npu_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/ascend_npu) — 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. * [DeepSeek-V3.2-W8A8](https://www.modelscope.cn/models/sgl-npu/DeepSeek-V3.2-W8A8) (Quantized version, 694.47GB) The W8A8 variant (694.47GB) can be deployed on 16 × 64GB of device memory (`--tp-size 16`), which corresponds to one full A3 node (8 cards, 16 dies) or two A2 nodes. This is the minimum recommended configuration. For optimized configurations, see [Best practices](#best-practices), which may require additional cards or nodes. For the hardware specifications (memory per die, dies per card, and the difference between A2 and A3), see [Ascend NPU Reference — Hardware](/docs/hardware-platforms/ascend-npus/ascend_npu_reference#hardware). ## Installation Ensure sufficient disk space before pulling images. The Docker image requires at least **30GB** 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](/docs/hardware-platforms/ascend-npus/ascend_npu_faq#8-docker-image-versions-stable-release-vs-daily-build). ```bash Command theme={null} 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 ``` ```bash Command theme={null} docker pull quay.io/ascend/sglang:v0.5.13.post1-cann9.0.0-910b docker run -itd --shm-size=16g --name ${NAME} \ --privileged=true --net=host \ -v /var/queue_schedule:/var/queue_schedule \ -v /etc/ascend_install.info:/etc/ascend_install.info \ -v /usr/local/sbin:/usr/local/sbin \ -v /usr/local/Ascend/driver:/usr/local/Ascend/driver \ -v /usr/local/Ascend/firmware:/usr/local/Ascend/firmware \ --device=/dev/davinci0:/dev/davinci0 \ --device=/dev/davinci1:/dev/davinci1 \ --device=/dev/davinci2:/dev/davinci2 \ --device=/dev/davinci3:/dev/davinci3 \ --device=/dev/davinci4:/dev/davinci4 \ --device=/dev/davinci5:/dev/davinci5 \ --device=/dev/davinci6:/dev/davinci6 \ --device=/dev/davinci7:/dev/davinci7 \ --device=/dev/davinci_manager:/dev/davinci_manager \ --device=/dev/hisi_hdc:/dev/hisi_hdc \ --entrypoint=bash \ quay.io/ascend/sglang:v0.5.13.post1-cann9.0.0-910b ``` * If the model weights have already been downloaded to a shared directory, use `-v` to mount the model path into the container, for example: `-v /path/to/models:/models`. * Replace `${NAME}` with your own container name or remove `--name` to use default name. ## Online service deployment ### 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 [DeepSeek-V3.2 Best Practice — PD Disaggregation On A3](/docs/hardware-platforms/ascend-npus/best_practice/deepseek_v3_2#pd-disaggregation). ## 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., 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](/docs/hardware-platforms/ascend-npus/ascend_npu#testing-the-service). ## Accuracy evaluation For accuracy evaluation methods and datasets, see [Accuracy Evaluation on Ascend NPU](/docs/hardware-platforms/ascend-npus/ascend_npu_accuracy_evaluation). ## Performance For performance data and benchmark commands, see [Performance Testing on Ascend NPU](/docs/hardware-platforms/ascend-npus/ascend_npu_performance_testing). ## Best practices ### Best practice configuration reference For complete optimal configurations with deployment scripts and benchmark commands, see the [DeepSeek-V3.2 Best Practice](/docs/hardware-platforms/ascend-npus/best_practice/deepseek_v3_2) page. ## 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/ascend_npu_optimization). ## FAQ For common environment, installation, and general parameter issues, please refer to the [Ascend NPU FAQ](/docs/hardware-platforms/ascend-npus/ascend_npu_faq).