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> **NOTE**: This example is based on an internal NVIDIA library that will soon be publicly released. The example won't work until the official release.

## Build docker

```
./container/build.sh --framework VLLM_NIXL --target dev --build-context nixl=<path to downloaded nixl repo @ c53bb19a6a114e9093071bd1f2904f996ae1839b>
```

## Run container

```
./container/run.sh --framework VLLM_NIXL --target dev -it
```

All of the commands below are run inside the same container.

## Run deployment

Add model to dynamo and start http server.

In terminal 0:
```
llmctl http add chat-models deepseek-ai/DeepSeek-R1-Distill-Llama-8B test-nixl.vllm.generate
TRT_LOG=DEBUG http --port 8181
```



### Monolithic deployment

In terminal 1:

```
cd /workspace/examples/python_rs/llm/vllm_nixl
CUDA_VISIBLE_DEVICES=0 python3 worker.py \
    --model deepseek-ai/DeepSeek-R1-Distill-Llama-8B \
    --enforce-eager
```


### Disaggregated deployment

In terminal 1:

```
cd /workspace/examples/python_rs/llm/vllm_nixl
CUDA_VISIBLE_DEVICES=0 python prefill_worker.py \
    --model deepseek-ai/DeepSeek-R1-Distill-Llama-8B \
    --enforce-eager \
    --kv-transfer-config \
    '{"kv_connector":"DynamoNixlConnector"}'
```

In terminal 2:

```
cd /workspace/examples/python_rs/llm/vllm_nixl
CUDA_VISIBLE_DEVICES=1,2 python3 worker.py \
    --remote-prefill \
    --model deepseek-ai/DeepSeek-R1-Distill-Llama-8B \
    --enforce-eager \
    --tensor-parallel-size 2 \
    --kv-transfer-config \
    '{"kv_connector":"DynamoNixlConnector"}'
```


## Client

In another terminal:
```
curl localhost:8181/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "deepseek-ai/DeepSeek-R1-Distill-Llama-8B",
    "messages": [
      {"role": "user", "content": "What is the capital of France?"}
    ],
    "max_tokens": 10
  }'
```

## Run genai-perf

`genai-perf` is a tool for profiling and benchmarking LLM servers. It is already installed in the container. For more details, please refer to the [genai-perf README](https://docs.nvidia.com/deeplearning/triton-inference-server/user-guide/docs/perf_analyzer/genai-perf/README.html).

```
genai-perf profile \
  -m deepseek-ai/DeepSeek-R1-Distill-Llama-8B \
  --url localhost:8181 \
  --endpoint-type chat \
  --streaming \
  --service-kind openai \
  --endpoint v1/chat/completions \
  --warmup-request-count 10 \
  --random-seed 123 \
  --synthetic-input-tokens-stddev 0 \
  --output-tokens-stddev 0 \
  --tokenizer deepseek-ai/DeepSeek-R1-Distill-Llama-8B \
  --synthetic-input-tokens-mean 3000 \
  --output-tokens-mean 150 \
  --extra-inputs min_tokens:150 \
  --extra-inputs max_tokens:150 \
  --profile-export-file my_profile_export.json \
  --artifact-dir artifacts/ \
  --concurrency 10 \
  --request-count 40 \
  -- -v \
  --async
```

## Close deployment

Kill all python processes and clean up metadata files:

```
pkill -9 -f python
rm -r /tmp/nixl
```

## TODOs, limitations, known issues

- [ ] Add etcd for discovery
- [ ] Multi-node deployment support
- [ ] Enable chunked prefill
- [ ] Support mixed tp
- [ ] Process many remote prefill in one iteration
- [ ] Support recompute preemption
- [ ] Make sure decode does not preempt blocks before xfer finishes
- [ ] Layer wise transfer
- [ ] Non blocking send in prefill (cache manager should check xfer status)
- [ ] Test under load
- [ ] Support pp > 1
- [ ] Check why adding extra seed input is crashing vllm with remote prefill
- [ ] Unified worker for both prefill and decode
- [x] Require sending two parallel requests to start decode for the first time
- [x] Concurrency > 2 is not working
- [x] Parse cmdline args
- [x] Manual nixl example with tp1
- [x] Zero copy
- [x] Conditional remote prefill
- [x] Manual example with tp > 1
- [x] Run on dynamo distributed runtime
- [x] add oai http endpoint
- [x] Sample only on decode, do note return remote prefill response
- [x] Check if all transfers finished before moving to decode
- [x] Enable async output processing - could be working
