DeepSeek V3.2 Usage

DeepSeek-V3.2 model family equips DeepSeek-V3.1-Terminus with DeepSeek Sparse Attention (DSA) through continued training. With DSA, a fine-grained sparse attention mechanism powered by a lightning indexer, DeepSeek-V3.2 achieves efficiency improvements in long-context scenarios. For reporting issues or tracking upcoming features, please refer to this Roadmap. Note: This document is originally written for the usage of DeepSeek-V3.2-Exp model. The usage of DeepSeek-V3.2 or DeepSeek-V3.2-Speciale is the same as DeepSeek-V3.2-Exp except for the tool call parser.

Installation

Docker

# H200/B200
docker pull lmsysorg/sglang:latest

# MI350/MI355
docker pull lmsysorg/sglang:v0.5.8-rocm700-mi35x

# MI300
# v0.5.8-rocm700-mi30x does not include PR #17504. Prefer the newest MI30x ROCm
# image tag from Docker Hub when available, or build from source (below).
docker pull lmsysorg/sglang:v0.5.8-rocm700-mi30x


# NPUs
docker pull lmsysorg/sglang:dsv32-a2
docker pull lmsysorg/sglang:dsv32-a3

Build From Source

# Install SGLang
git clone https://github.com/sgl-project/sglang
cd sglang
pip3 install pip --upgrade
pip3 install -e "python"

Launch DeepSeek V3.2 with SGLang

To serve DeepSeek-V3.2-Exp on 8xH200/B200 GPUs:

# Launch with TP + DP (Recommended)
python -m sglang.launch_server --model deepseek-ai/DeepSeek-V3.2-Exp --tp 8 --dp 8 --enable-dp-attention

# Launch with EP + DP
python -m sglang.launch_server --model deepseek-ai/DeepSeek-V3.2-Exp --tp 8 --ep 8 --dp 8 --enable-dp-attention

# Launch with Pure TP
python -m sglang.launch_server --model deepseek-ai/DeepSeek-V3.2-Exp --tp 8

# Launch with TP on MI30x/MI35x
python3 -m sglang.launch_server --model deepseek-ai/DeepSeek-V3.2-Exp --tp 8 --nsa-prefill-backend tilelang --nsa-decode-backend tilelang

Configuration Tips

DP Attention (Recommended): For DeepSeek V3.2 model, the kernels are customized for the use case of dp_size=8, so DP attention (--dp 8 --enable-dp-attention) is the recommended configuration for better stability and performance. All test cases use this configuration by default.
Pure TP Mode: Launching with pure TP (without --dp and --enable-dp-attention) is also supported. Note that this mode has not been fully validated in PD disaggregation scenarios.
Short-sequence MHA prefill (adaptive): For short prefill sequences (default threshold: 2048 tokens), the NSA backend uses standard MHA automatically (no extra flags). On H200 (SM90) this path uses the FlashAttention variable-length kernel; on B200 (SM100) it uses TRT-LLM ragged MHA. MHA uses MHA_ONE_SHOT for best performance. MHA_ONE_SHOT computes multi-head attention over all tokens (both cached prefix and newly extended tokens) in a single kernel invocation, avoiding the overhead of chunked KV cache processing. This achieves optimal throughput for short sequences where total sequence length fits within the chunk capacity limit.
Choices of Attention Kernels: The attention backend is automatically set to nsa attention backend for DeepSeek V3.2 model. In this backend, different kernels for sparse prefilling/decoding are implemented, which can be specified by --nsa-prefill-backend and --nsa-decode-backend server arguments. The choices of nsa prefill/decode attention kernels include:
- flashmla_sparse: flash_mla_sparse_fwd kernel from flash_mla library. Can run on both Hopper and Blackwell GPUs. It requires bf16 q, kv inputs.
- flashmla_kv: flash_mla_with_kvcache kernel from flash_mla library. Can run on both Hopper and Blackwell GPUs. It requires bf16 q, fp8 k_cache inputs.
- fa3: flash_attn_with_kvcache kernel from flash_attn library. Can only run on Hopper GPUs. It requires bf16 q, kv inputs.
- tilelang: tilelang implementation that can run on GPU, HPU and NPU.
- aiter: Aiter kernel on AMD HPUs. Can only be used as decode kernel.
On the basis of performance benchmarks, the default configuration on H200 and B200 are set as follows :
- H200: flashmla_sparse prefill attention (short-seq prefill uses MHA via FlashAttention varlen), fa3 decode attention, bf16 kv cache dtype.
- B200: flashmla_auto prefill attention (short-seq prefill uses MHA via TRT-LLM ragged), flashmla_kv decode attention, fp8_e4m3 kv cache dtype. flashmla_auto enables automatic selection of either flashmla_sparse or flashmla_kv kernel for prefill based on KV cache dtype, hardware, and heuristics. When FP8 KV cache is enabled and total_kv_tokens < total_q_tokens * 512, it uses the flashmla_sparse kernel; otherwise, it falls back to the flashmla_kv kernel. The heuristics may need to be tuned if the performance of either the flashmla_sparse or flashmla_kv kernel changes significantly.

Multi-token Prediction

SGLang implements Multi-Token Prediction (MTP) for DeepSeek V3.2 based on EAGLE speculative decoding. With this optimization, the decoding speed can be improved significantly on small batch sizes. Please look at this PR for more information. Example usage with DP Attention:

python -m sglang.launch_server --model deepseek-ai/DeepSeek-V3.2-Exp --tp 8 --dp 8 --enable-dp-attention --speculative-algorithm EAGLE --speculative-num-steps 3 --speculative-eagle-topk 1 --speculative-num-draft-tokens 4

Example usage with Pure TP:

python -m sglang.launch_server --model deepseek-ai/DeepSeek-V3.2-Exp --tp 8 --speculative-algorithm EAGLE --speculative-num-steps 3 --speculative-eagle-topk 1 --speculative-num-draft-tokens 4

The best configuration for --speculative-num-steps, --speculative-eagle-topk and --speculative-num-draft-tokens can be searched with bench_speculative.py script for given batch size. The minimum configuration is --speculative-num-steps 1 --speculative-eagle-topk 1 --speculative-num-draft-tokens 2, which can achieve speedup for larger batch sizes.
The default value of --max-running-requests is set to 48 for MTP. For larger batch sizes, this value should be increased beyond the default value.

To enable the experimental overlap scheduler for EAGLE speculative decoding, set the environment variable SGLANG_ENABLE_SPEC_V2=1. This can improve performance by enabling overlap scheduling between draft and verification stages.

Function Calling and Reasoning Parser

The usage of function calling and reasoning parser is the same as DeepSeek V3.1. Please refer to Reasoning Parser and Tool Parser documents. To launch DeepSeek-V3.2-Exp with function calling and reasoning parser:

It is recommended to specify the chat-template, ensuring that you are within the sglang’s root directory.

python3 -m sglang.launch_server \
  --model-path deepseek-ai/DeepSeek-V3.2-Exp \
  --trust-remote-code \
  --tp-size 8 --dp-size 8 --enable-dp-attention \
  --tool-call-parser deepseekv31 \
  --reasoning-parser deepseek-v3 \
  --chat-template ./examples/chat_template/tool_chat_template_deepseekv32.jinja

To launch DeepSeek-V3.2 with function calling and reasoning parser:

python3 -m sglang.launch_server \
  --model-path deepseek-ai/DeepSeek-V3.2 \
  --trust-remote-code \
  --tp-size 8 --dp-size 8 --enable-dp-attention \
  --tool-call-parser deepseekv32 \
  --reasoning-parser deepseek-v3

DeepSeek-V3.2-Speciale doesn’t support tool calling, so can only be launched with reasoning parser:

python3 -m sglang.launch_server \
  --model-path deepseek-ai/DeepSeek-V3.2-Speciale \
  --trust-remote-code \
  --tp-size 8 --dp-size 8 --enable-dp-attention \
  --reasoning-parser deepseek-v3

PD Disaggregation

Prefill Command:

python -m sglang.launch_server \
        --model-path deepseek-ai/DeepSeek-V3.2-Exp \
        --disaggregation-mode prefill \
        --host $LOCAL_IP \
        --port $PORT \
        --tp 8 \
        --dp 8 \
        --enable-dp-attention \
        --dist-init-addr ${HOST}:${DIST_PORT} \
        --trust-remote-code \
        --disaggregation-bootstrap-port 8998 \
        --mem-fraction-static 0.9 \

Decode command:

python -m sglang.launch_server \
        --model-path deepseek-ai/DeepSeek-V3.2-Exp \
        --disaggregation-mode decode \
        --host $LOCAL_IP \
        --port $PORT \
        --tp 8 \
        --dp 8 \
        --enable-dp-attention \
        --dist-init-addr ${HOST}:${DIST_PORT} \
        --trust-remote-code \
        --mem-fraction-static 0.9 \

Router command:

python -m sglang_router.launch_router --pd-disaggregation \
  --prefill $PREFILL_ADDR 8998 \
  --decode $DECODE_ADDR \
  --host 127.0.0.1 \
  --port 8000 \

If you need more advanced deployment methods or production-ready deployment methods, such as RBG or LWS-based deployment, please refer to references/multi_node_deployment/rbg_pd/deepseekv32_pd. Additionally, you can also find startup commands for DeepEP-based EP parallelism in the aforementioned documentation.

Benchmarking Results

Accuracy Test with `gsm8k`

A simple accuracy benchmark can be tested with gsm8k dataset:

python3 benchmark/gsm8k/bench_sglang.py --num-shots 8 --num-questions 1319 --parallel 1319

The result is 0.956, which matches our expectation:

Accuracy: 0.956
Invalid: 0.000
Latency: 25.109 s
Output throughput: 5226.235 token/s

To test long-context accuracy, run gsm8k with --num-shots 20. The results are very close to the 8 shots results:

Accuracy: 0.956
Invalid: 0.000
Latency: 29.545 s
Output throughput: 4418.617 token/s

Accuracy Test with `gpqa-diamond`

Accuracy benchmark on long context can be tested on GPQA-diamond dataset with long output tokens and thinking enabled:

python3 -m sglang.test.run_eval --port 30000 --eval-name gpqa --num-examples 198 --max-tokens 128000 --repeat 8 --thinking-mode deepseek-v3

The mean accuracy over 8 runs shows 0.797, which matches the number 0.799 in official tech report.

Repeat: 8, mean: 0.797
Scores: ['0.808', '0.798', '0.808', '0.798', '0.783', '0.788', '0.803', '0.793']

For Deepseek V3.2, Deepseek recommends setting the sampling parameters to temperature = 1.0, top_p = 0.95:

python3 -m sglang.test.run_eval --port 30000 --eval-name gpqa --num-examples 198 --max-tokens 128000 --repeat 8 --top-p 0.95 --temperature 1.0 --thinking-mode deepseek-v3

Repeat: 8, mean: 0.840
Scores: ['0.848', '0.808', '0.848', '0.838', '0.879', '0.813', '0.838', '0.848']

which matches the official score, 0.824, as reported in the Deepseek-V3.2 technical report.

Accuracy Test with `aime 2025`

Prepare the environment by installing NeMo-Skills in the docker or your own virtual environment:

pip install git+https://github.com/NVIDIA/NeMo-Skills.git --ignore-installed blinker

Then launch the SGLang server:

python -m sglang.launch_server --model deepseek-ai/DeepSeek-V3.2-Exp --tp 8 --dp 8 --enable-dp-attention

For DeepSeek-V3.2 and DeepSeek-V3.2-Speciale:

python3 -m sglang.launch_server   --model-path deepseek-ai/DeepSeek-V3.2   --trust-remote-code   --tp-size 8 --dp-size 8 --enable-dp-attention   --tool-call-parser deepseekv32   --reasoning-parser deepseek-v3

Run the following script to evaluate AIME 2025:

#! /bin/bash
export NEMO_SKILLS_DISABLE_UNCOMMITTED_CHANGES_CHECK=1

ns prepare_data aime25

PORT=30000
BACKEND=sglang
MODEL="deepseek-ai/DeepSeek-V3.2-Exp" # Should be changed to the model name
MODEL_NAME="dsv32-fp8"

echo "Starting AIME25 evaluation with model $MODEL on port $PORT using backend $BACKEND..."
ns eval \
  --benchmarks=aime25:4 \
  --server_type=$BACKEND \
  --model=$MODEL \
  --server_address=http://localhost:${PORT}/v1 \
  --output_dir=nemo_skills_aime25_${MODEL_NAME}_output_${BACKEND}_$(date +%Y%m%d_%H%M%S) \
  ++chat_template_kwargs.thinking=true \
  ++inference.temperature=1.0 \
  ++inference.top_p=0.95 \
  ++inference.tokens_to_generate=64000
  # ++inference.tokens_to_generate=120000 for Speciale model

Test results (8*B200): DeepSeek-V3.2-Exp：

evaluation_mode	num_entries	avg_tokens	gen_seconds	symbolic_correct	no_answer
pass@1[avg-of-4]	30	15040	1673	87.50% ± 1.67%	0.00%
majority@4	30	15040	1673	90.00%	0.00%
pass@4	30	15040	1673	90.00%	0.00%

DeepSeek-V3.2:

evaluation_mode	num_entries	avg_tokens	gen_seconds	symbolic_correct	no_answer
pass@1[avg-of-4]	30	13550	1632	92.50% ± 1.67%	0.00%
majority@4	30	13550	1632	94.71%	0.00%
pass@4	30	13550	1632	96.67%	0.00%

DeepSeek-V3.2-Speciale:

evaluation_mode	num_entries	avg_tokens	gen_seconds	symbolic_correct	no_answer
pass@1[avg-of-4]	30	24155	3583	95.00% ± 1.92%	0.00%
majority@4	30	24155	3583	95.83%	0.00%
pass@4	30	24155	3583	100.00%	0.00%

DSA long sequence context parallel optimization(experimental)

Note: This feature is only verified on Hopper machines For context parallel in DeepSeek V3.2 model, we provide two different modes of splitting tokens, which can be controlled with argument --nsa-prefill-cp-mode.

In sequence splitting (default setting)

The first mode can be enabled by --nsa-prefill-cp-mode in-seq-split. This mode implements context parallel for DSA by splitting the sequence uniformly between context parallel ranks. At attention stage, each cp rank computes the indexer results of sharded sequence, and collects the whole kv cache through all gather operator. The communication group for context parallel reuses the one for attention tp, thus cp_size equals atten_tp_size = tp_size / dp_size. Note that in sequence splitting mode has the following restrictions:

The batch size is restricted to 1 for prefill batches
Multi-node/PD disaggregation is still not supported
moe_dense_tp_size=1, kv_cache_dtype = "bf16", moe_a2a_backend = "deepep"
To ensure cp_size > 1, the passed in tp_size must be larger than dp_size

For more details, please refer to PR https://github.com/sgl-project/sglang/pull/12065. Example:

# In-seq splitting mode launched with EP + DP
python -m sglang.launch_server --model deepseek-ai/DeepSeek-V3.2-Exp  --tp 8 --ep 8 --dp 2 --enable-dp-attention --enable-nsa-prefill-context-parallel --nsa-prefill-cp-mode in-seq-split --max-running-requests 32

Round robin splitting

This mode can be enabled by specifying the parameter --nsa-prefill-cp-mode round-robin-split, which distributes tokens across ranks based on token_idx % cp_size. In this scenario, compared with the aforementioned method, it additionally supports the fused MoE backend (the fused MoE backend may deliver better performance than DeepEP in single-machine scenarios), FP8 KV-cache, and multi-batch prefill inference. But it cannot be enabled with dp attention together. For more details, please refer to PR https://github.com/sgl-project/sglang/pull/13959. Example usage:

# Launch with FusedMoe + CP8
python -m sglang.launch_server --model deepseek-ai/DeepSeek-V3.2-Exp  --tp 8 --enable-nsa-prefill-context-parallel --nsa-prefill-cp-mode round-robin-split --max-running-requests 32

Pipeline Parallel + Context Parallel (PP + CP)

This mode combines Pipeline Parallelism (PP) and Context Parallelism (CP) to scale across multiple nodes, which can achieve better throughput and Time To First Token (TTFT). Note that this method has only been tested on H20 96G.

Standard Usage

To launch with PP=2 and CP (via round-robin-split mode) on 2 nodes. This configuration uses the fused MoE kernel by default, which generally provides better performance. For related development details, please refer to:

Fused MoE + CP support: PR #13959
PP + CP support: Issue #15358 and PR #16380

Node 0:

export SGLANG_PP_LAYER_PARTITION=30,31
python3 -m sglang.launch_server \
  --model-path deepseek-ai/DeepSeek-V3.2-Exp \
  --nnodes 2 --node-rank 0 \
  --dist-init-addr <HEAD_NODE_IP>:62001 \
  --tp 8 --pp-size 2 \
  --dp-size 1 --moe-dense-tp-size 1 \
  --enable-nsa-prefill-context-parallel \
  --nsa-prefill-cp-mode round-robin-split \
  --trust-remote-code \
  --disable-radix-cache \
  --mem-fraction-static 0.8 \
  --max-running-requests 128 \
  --chunked-prefill-size 16384 \
  --cuda-graph-max-bs 8 \
  --page-size 64 \
  --watchdog-timeout 3600 \
  --host 0.0.0.0 --port 8000 \
  --tool-call-parser deepseekv32

Node 1:

export SGLANG_PP_LAYER_PARTITION=30,31
python3 -m sglang.launch_server \
  --model-path deepseek-ai/DeepSeek-V3.2-Exp \
  --nnodes 2 --node-rank 1 \
  --dist-init-addr <HEAD_NODE_IP>:62001 \
  --tp 8 --pp-size 2 \
  --dp-size 1 --moe-dense-tp-size 1 \
  --enable-nsa-prefill-context-parallel \
  --nsa-prefill-cp-mode round-robin-split \
  --trust-remote-code \
  --disable-radix-cache \
  --mem-fraction-static 0.8 \
  --max-running-requests 128 \
  --chunked-prefill-size 16384 \
  --cuda-graph-max-bs 8 \
  --page-size 64 \
  --watchdog-timeout 3600 \
  --host 0.0.0.0 --port 8000 \
  --tool-call-parser deepseekv32

PD Disaggregation with PP + CP

If using PD (Prefill-Decode) Disaggregation, the Prefill nodes can be configured with PP + CP as follows. Prefill Node 0:

python -m sglang.launch_server \
  --model-path deepseek-ai/DeepSeek-V3.2-Exp \
  --served-model-name deepseek-v32 \
  --nnodes 2 --node-rank 0 \
  --dist-init-addr <PREFILL_HEAD_IP>:20102 \
  --tp 8 --pp-size 2 \
  --dp-size 1 --moe-dense-tp-size 1 \
  --enable-nsa-prefill-context-parallel \
  --nsa-prefill-cp-mode round-robin-split  \
  --disaggregation-ib-device mlx5_bond_0,mlx5_bond_1,mlx5_bond_2,mlx5_bond_3 \
  --trust-remote-code \
  --disable-radix-cache \
  --max-running-requests 512 \
  --chunked-prefill-size 4096 \
  --context-length 131072 \
  --mem-fraction-static 0.9 \
  --page-size 64 \
  --enable-metrics \
  --collect-tokens-histogram \
  --tokenizer-worker-num 8 \
  --host 0.0.0.0 --port 30000

Prefill Node 1:

python -m sglang.launch_server \
  --model-path deepseek-ai/DeepSeek-V3.2-Exp \
  --served-model-name deepseek-v32-prefill \
  --nnodes 2 --node-rank 1 \
  --dist-init-addr <PREFILL_HEAD_IP>:20102 \
  --tp 8 --pp-size 2 \
  --dp-size 1 --moe-dense-tp-size 1 \
  --enable-nsa-prefill-context-parallel \
  --nsa-prefill-cp-mode round-robin-split  \
  --disaggregation-ib-device mlx5_bond_0,mlx5_bond_1,mlx5_bond_2,mlx5_bond_3 \
  --trust-remote-code \
  --disable-radix-cache \
  --max-running-requests 512 \
  --chunked-prefill-size 4096 \
  --context-length 131072 \
  --mem-fraction-static 0.9 \
  --page-size 64 \
  --enable-metrics \
  --collect-tokens-histogram \
  --tokenizer-worker-num 8 \
  --host 0.0.0.0 --port 30000

For the Decode nodes, it is recommended to use the EP mode.

Getting Started

Basic Usage

Advanced Features

Supported Models

Hardware Platforms

Developer Guide

References

Installation

Docker

Build From Source

Launch DeepSeek V3.2 with SGLang

Configuration Tips

Multi-token Prediction

Function Calling and Reasoning Parser

PD Disaggregation

Benchmarking Results

Accuracy Test with `gsm8k`

Accuracy Test with `gpqa-diamond`

Accuracy Test with `aime 2025`

DSA long sequence context parallel optimization(experimental)

In sequence splitting (default setting)

Round robin splitting

Pipeline Parallel + Context Parallel (PP + CP)

Standard Usage

PD Disaggregation with PP + CP

Getting Started

Basic Usage

Advanced Features

Supported Models

Hardware Platforms

Developer Guide

References

​Installation

​Docker

​Build From Source

​Launch DeepSeek V3.2 with SGLang

​Configuration Tips

​Multi-token Prediction

​Function Calling and Reasoning Parser

​PD Disaggregation

​Benchmarking Results

​Accuracy Test with gsm8k

​Accuracy Test with gpqa-diamond

​Accuracy Test with aime 2025

​DSA long sequence context parallel optimization(experimental)

​In sequence splitting (default setting)

​Round robin splitting

​Pipeline Parallel + Context Parallel (PP + CP)

​Standard Usage

​PD Disaggregation with PP + CP

Installation

Docker

Build From Source

Launch DeepSeek V3.2 with SGLang

Configuration Tips

Multi-token Prediction

Function Calling and Reasoning Parser

PD Disaggregation

Benchmarking Results

Accuracy Test with `gsm8k`

Accuracy Test with `gpqa-diamond`

Accuracy Test with `aime 2025`

DSA long sequence context parallel optimization(experimental)

In sequence splitting (default setting)

Round robin splitting

Pipeline Parallel + Context Parallel (PP + CP)

Standard Usage

PD Disaggregation with PP + CP