feat(serve): Enhance multi-node deployment and worker configuration

deploy/dynamo/sdk/src/dynamo/sdk/cli/serve_dynamo.py

@@ -26,6 +26,7 @@
 from typing import Any
 
 import click
+import uvloop
 
 from dynamo.runtime import DistributedRuntime, dynamo_endpoint, dynamo_worker
 from dynamo.sdk import dynamo_context
@@ -186,6 +187,7 @@ async def worker(runtime: DistributedRuntime):
                 logger.error(f"[{run_id}] Error in Dynamo component setup: {str(e)}")
                 raise
 
+        uvloop.install()
         asyncio.run(worker())
 
 

deploy/dynamo/sdk/src/dynamo/sdk/cli/serving.py

@@ -438,6 +438,28 @@ def serve_http(
                 "--worker-id",
                 "$(CIRCUS.WID)",
             ]
+            # Allows us to run a single dynamo service with multiple GPU workers
+            if worker_envs:
+                args.extend(["--worker-env", json.dumps(worker_envs)])
+            worker_env = env.copy() if env else {}
+
+            # Pass through the main service config
+            if "DYNAMO_SERVICE_CONFIG" in os.environ:
+                worker_env["DYNAMO_SERVICE_CONFIG"] = os.environ[
+                    "DYNAMO_SERVICE_CONFIG"
+                ]
+
+            # Get service-specific environment variables from DYNAMO_SERVICE_ENVS
+            if "DYNAMO_SERVICE_ENVS" in os.environ:
+                try:
+                    service_envs = json.loads(os.environ["DYNAMO_SERVICE_ENVS"])
+                    if svc.name in service_envs:
+                        service_args = service_envs[svc.name].get("ServiceArgs", {})
+                        if "envs" in service_args:
+                            worker_env.update(service_args["envs"])
+                except json.JSONDecodeError as e:
+                    logger.warning(f"Failed to parse DYNAMO_SERVICE_ENVS: {e}")
+
             watcher = create_watcher(
                 name=f"dynamo_service_{svc.name}",
                 args=args,

examples/llm/README.md

@@ -150,6 +150,51 @@ curl localhost:8000/v1/chat/completions   -H "Content-Type: application/json"
 
 ```
 
+### Multinode Examples
+
+#### Single node sized models
+You can deploy our example architectures on multiple nodes via NATS/ETCD based discovery and communication. Here's an example of deploying disaggregated serving on 2 nodes
+
+##### Disaggregated Deployment with KV Routing
+Node 1: Frontend, Processor, Router, 8 Decode
+Node 2: 8 Prefill
+
+**Step 1**: Start NATS/ETCD on your head node. Ensure you have the correct firewall rules to allow communication between the nodes as you will need the NATS/ETCD endpoints to be accessible by node 2.
+```bash
+# node 1
+cd deploy
+docker compose -d
+```
+
+**Step 2**: Create the inference graph for this deployment. The easiest way to do this is to remove the `.link(PrefillWorker)` from the `disagg_router.py` file.
+
+```python
+# graphs/disag_router.py
+# imports...
+Frontend.link(Processor).link(Router).link(VllmWorker)
+```
+
+**Step 3**: Start the frontend, processor, router, and 8 VllmWorkers on node 1.
+```bash
+# node 1
+cd /workspace/examples/llm
+dynamo serve graphs.disagg_router:Frontend -f ./configs/disagg_router.yaml --VllmWorker.ServiceArgs.workers=8
+```
+
+**Step 4**: Start 8 PrefillWorkers on node 2.
+Since we only want to start the `PrefillWorker` on node 2, you can simply run just the PrefillWorker component directly.
+
+```bash
+# node 2
+export NATS_SERVER = '<your-nats-server-address>'
+export ETCD_ENDPOINTS = '<your-etcd-endpoints-address>'
+
+cd /workspace/examples/llm
+dynamo serve components.prefill_worker:PrefillWorker -f ./configs/disagg_router.yaml --PrefillWorker.ServiceArgs.workers=8
+```
+
+You can now use the same curl request from above to interact with your deployment!
+
 ### Close deployment
 
 Kill all dynamo processes managed by circusd.

examples/llm/configs/disagg_router.yaml

@@ -48,12 +48,12 @@ VllmWorker:
 
 # TODO - set all of these but model as default
 PrefillWorker:
-  model: deepseek-ai/DeepSeek-R1-Distill-Llama-8B
-  kv-transfer-config: '{"kv_connector":"DynamoNixlConnector"}'
-  block-size: 64
-  max-model-len: 16384
-  max-num-batched-tokens: 16384
-  ServiceArgs:
-    workers: 1
-    resources:
-      gpu: 1
+ model: deepseek-ai/DeepSeek-R1-Distill-Llama-8B
+ kv-transfer-config: '{"kv_connector":"DynamoNixlConnector"}'
+ block-size: 64
+ max-model-len: 16384
+ max-num-batched-tokens: 16384
+ ServiceArgs:
+   workers: 1
+   resources:
+     gpu: 1