SGLang¶

This example shows how to deploy Qwen/Qwen3.6-27B using SGLang and dstack.

For a DeepSeek-V4-Pro deployment on B200:8, see the DeepSeek V4 model page.

Apply a configuration¶

Here's an example of a service that deploys Qwen/Qwen3.6-27B using SGLang.

NVIDIAAMD

type: service
name: qwen36

image: lmsysorg/sglang:v0.5.10.post1

commands:
  - |
    sglang serve \
      --model-path Qwen/Qwen3.6-27B \
      --host 0.0.0.0 \
      --port 30000 \
      --tp $DSTACK_GPUS_NUM \
      --reasoning-parser qwen3 \
      --mem-fraction-static 0.8 \
      --context-length 262144

port: 30000
model: Qwen/Qwen3.6-27B

volumes:
  - instance_path: /root/.cache
    path: /root/.cache
    optional: true

resources:
  shm_size: 16GB
  gpu: H100:4

type: service
name: qwen36

image: lmsysorg/sglang:v0.5.10-rocm720-mi30x

commands:
  - |
    sglang serve \
      --model-path Qwen/Qwen3.6-27B \
      --host 0.0.0.0 \
      --port 30000 \
      --tp $DSTACK_GPUS_NUM \
      --reasoning-parser qwen3 \
      --mem-fraction-static 0.8 \
      --context-length 262144

port: 30000
model: Qwen/Qwen3.6-27B

volumes:
  - instance_path: /root/.cache
    path: /root/.cache
    optional: true

resources:
  cpu: 52..
  memory: 896GB..
  shm_size: 16GB
  disk: 450GB..
  gpu: MI300X:4

The AMD example keeps the deployment close to the upstream Qwen and SGLang guidance: a pinned ROCm image, tensor parallelism across all four GPUs, and the standard qwen3 reasoning parser without extra ROCm-specific tuning flags. The first startup on MI300X can take longer while SGLang compiles ROCm kernels.

Save one of the configurations above as qwen36.dstack.yml, then use the dstack apply command.

$ dstack apply -f qwen36.dstack.yml

If no gateway is created, the service endpoint will be available at <dstack server URL>/proxy/services/<project name>/<run name>/.

curl http://127.0.0.1:3000/proxy/services/main/qwen36/v1/chat/completions \
    -X POST \
    -H 'Authorization: Bearer &lt;dstack token&gt;' \
    -H 'Content-Type: application/json' \
    -d '{
      "model": "Qwen/Qwen3.6-27B",
      "messages": [
        {
          "role": "user",
          "content": "A bat and a ball cost $1.10 total. The bat costs $1.00 more than the ball. How much does the ball cost? Answer with just the dollar amount."
        }
      ],
      "separate_reasoning": true,
      "max_tokens": 1024
    }'

Qwen3.6 uses thinking mode by default. To disable thinking, pass "chat_template_kwargs": {"enable_thinking": false} in the request body. To enable tool calling, add --tool-call-parser qwen3_coder to the serve command.

If a gateway is configured (e.g. to enable auto-scaling, HTTPS, rate limits, etc.), the service endpoint will be available at https://qwen36.<gateway domain>/.

Configuration options¶

PD disaggregation¶

To run SGLang with PD disaggregation, use replicas groups: one for a router (for example, SGLang Model Gateway), one for prefill workers, and one for decode workers.

NVIDIA

type: service
name: prefill-decode
image: lmsysorg/sglang:v0.5.10.post1

env:
  - HF_TOKEN
  - MODEL_ID=zai-org/GLM-4.5-Air-FP8

replicas:
  - count: 1
    # For now replica group with router must have count: 1
    commands:
      - pip install sglang_router
      - |
        python -m sglang_router.launch_router \
          --host 0.0.0.0 \
          --port 8000 \
          --pd-disaggregation \
          --prefill-policy cache_aware
    router:
      type: sglang
    resources:
      cpu: 4

  - count: 1..4
    scaling:
      metric: rps
      target: 3
    commands:
      - |
        python -m sglang.launch_server \
          --model-path $MODEL_ID \
          --disaggregation-mode prefill \
          --disaggregation-transfer-backend nixl \
          --host 0.0.0.0 \
          --port 8000 \
          --disaggregation-bootstrap-port 8998
    resources:
      gpu: H200

  - count: 1..8
    scaling:
      metric: rps
      target: 2
    commands:
      - |
        python -m sglang.launch_server \
          --model-path $MODEL_ID \
          --disaggregation-mode decode \
          --disaggregation-transfer-backend nixl \
          --host 0.0.0.0 \
          --port 8000
    resources:
      gpu: H200

port: 8000
model: zai-org/GLM-4.5-Air-FP8

# Custom probe is required for PD disaggregation.
probes:
  - type: http
    url: /health
    interval: 15s

Currently, auto-scaling only supports rps as the metric. TTFT and ITL metrics are coming soon.

Cluster

PD disaggregation requires the service to run in a fleet with placement set to cluster, because the replicas require an interconnect between instances.

While the prefill and decode replicas run on GPUs, the router replica requires a CPU instance in the same cluster.

What's next?¶

Read about services and gateways
Browse the Qwen 3.6 SGLang cookbook and the SGLang server arguments reference