[Blog] Model inference with Prefill-Decode disaggregation

docs/blog/posts/pd-disaggregation.md

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+---
+title: "Model inference with Prefill-Decode disaggregation"
+date: 2026-02-19
+description: "TBA"
+slug: pd-disaggregation
+image: https://dstack.ai/static-assets/static-assets/images/dstack-pd-disaggregation.png
+categories:
+  - Changelog
+links:
+  - SGLang router integration: https://dstack.ai/blog/sglang-router/
+---
+
+# Model inference with Prefill-Decode disaggregation
+
+While `dstack` started as a GPU-native orchestrator for development and training, over the last year it has increasingly brought inference to the forefront — making serving a first-class citizen.
+
+<img src="https://dstack.ai/static-assets/static-assets/images/dstack-pd-disaggregation.png" width="630"/>
+
+At the end of last year, we introduced [SGLang router](../posts/sglang-router.md) integration — bringing cache-aware routing to [services](../../docs/concepts/services.md). Today, building on that integration, we’re adding native Prefill–Decode (PD) disaggregation.
+
+<!-- more -->
+
+Unlike many PD disaggregation setups tied to Kubernetes as the control plane, dstack does not depend on Kubernetes. It’s an open-source, GPU-native orchestrator that can provision GPUs directly in your cloud accounts or on bare-metal infrastructure — while also running on top of existing Kubernetes clusters if needed.
+
+For inference, `dstack` provides a [services](../../docs/concepts/services.md) abstraction. While remaining framework-agnostic, we integrate more deeply with leading open-source frameworks — [SGLang](https://github.com/sgl-project/sglang) being one of them for model inference.
+
+> If you’re new to Prefill–Decode disaggregation, see the official [SGLang docs](https://docs.sglang.io/advanced_features/pd_disaggregation.html).
+
+## Services
+
+With `dstack` `0.20.10`, you can define a service with separate replica groups for Prefill and Decode workers and enable PD disaggregation directly in the `router` configuration.
+
+<div editor-title="glm45air.dstack.yml">
+
+```yaml
+type: service
+name: glm45air
+
+env:
+  - HF_TOKEN
+  - MODEL_ID=zai-org/GLM-4.5-Air-FP8
+
+image: lmsysorg/sglang:latest
+
+replicas:
+  - count: 1..4
+    scaling:
+      metric: rps
+      target: 3
+    commands:
+      - |
+          python -m sglang.launch_server \
+            --model-path $MODEL_ID \
+            --disaggregation-mode prefill \
+            --disaggregation-transfer-backend mooncake \
+            --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 $MODEL_ID \
+            --disaggregation-mode decode \
+            --disaggregation-transfer-backend mooncake \
+            --host 0.0.0.0 \
+            --port 8000
+    resources:
+      gpu: H200
+
+port: 8000
+model: zai-org/GLM-4.5-Air-FP8
+
+probes:
+  - type: http
+    url: /health_generate
+    interval: 15s
+
+router:
+  type: sglang
+  pd_disaggregation: true
+```
+
+</div>
+
+Deploy it as usual:
+
+<div class="termy">
+
+```shell
+$ HF_TOKEN=...
+$ dstack apply -f glm45air.dstack.yml
+```
+
+</div>
+
+### Gateway
+
+Just like `dstack` relies on the SGLang router for cache-aware routing, Prefill–Decode disaggregation also requires a [gateway](https://chatgpt.com/docs/concepts/gateways.md#sglang) configured with the SGLang router.
+
+<div editor-title="gateway-sglang.dstack.yml">
+
+```yaml
+type: gateway
+name: inference-gateway
+
+backends: [kubernetes]
+region: any
+
+domain: example.com
+
+router:
+  type: sglang
+  policy: cache_aware
+```
+
+</div>
+
+## Limitations
+
+* Because the SGLang router requires all workers to be on the same network, and `dstack` currently runs the router inside the gateway, the gateway and the service must be running in the same cluster.
+* Prefill–Decode disaggregation is currently available with the SGLang backend (vLLM support is coming).
+* Autoscaling supports RPS as the metric for now; TTFT and end-to-end latency metrics are planned next.
+
+With native support for inference and now Prefill–Decode disaggregation, `dstack` makes it easier to run high-throughput, low-latency model serving across GPU clouds, and Kubernetes or bare-metal clusters.
+
+## What's next?
+
+We’re working on PD disaggregation benchmarks and tuning guidance — coming soon.
+
+In the meantime:
+
+1. Read about [services](../../docs/concepts/services.md), [gateways](../../docs/concepts/gateways.md), and [fleets](../../docs/concepts/fleets.md)
+2. Check out [Quickstart](../../docs/quickstart.md)
+3. Join [Discord](https://discord.gg/u8SmfwPpMd)