# Triton Inference Server

Triton Inference Server 2.55(r25.02) is an open-source inference serving
software that streamlines AI inferencing.

The Cloud AI SDK enables multiple backends for inference execution workflow. When these backends run on a
host with Cloud AI inference accelerators, they detect the Cloud AI cards during initialization and route inference requests (sent to the Triton server) to the hardware.

![../../../../../../_images/triton_backends.png](data:image/png;base64,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)

## Supported features

- Backends for Cloud AI hardware:

    - Python backends for LLMs and embedding models also support AIC execution.
    - QAic as a customized C++ backend for deploying compiled binaries optimized for AIC.
    - vLLM backend also supports AIC execution.
    - `onnxruntime_onnx` (v1.18.1) as platform with QAic EP (execution provider) for deploying ONNX graphs.
- Model ensemble (QAic backend, onnxruntime)
- Dynamic batching (QAic backend, onnxruntime)
- Auto device-picker
- Support for auto complete configuration
- LLM support for LlamaForCausalLM, AutoModelForCausalLM categories.
- [vLLM](https://docs.qualcomm.com/doc/80-99100-3/topic/index_vLLM.html#reference-to-vllm) (0.8.5) support for AIC execution.
- [Generate extension](https://docs.nvidia.com/deeplearning/triton-inference-server/user-guide/docs/protocol/extension_generate.html)
- OpenAI compatibility (vLLM backend)

## Create a AIC100 backend enabled Triton Docker image

To add customized backends (to process inferencing on AI 100
hardware) into a Triton server image, you need to run a few
scripts by passing the `sdk_path` as parameters. The `build_image.py` script generates a Docker image as output. This script is a part of Cloud
AI Apps SDK contents which you can run after you unzip it.

![../../../../../../_images/docker_workflow.png](data:image/png;base64,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)

cd </path/to/apps-sdk>/common/tools/docker-build
    
    python3 build_image.py --image_name qaic-triton --tag 1.20 --log_level 2 --user_specification_file </path/to/apps-sdk>/common/tools/docker-build/sample_user_specs/user_image_spec_triton_model_repo.json --apps-sdk /apps/sdk/path.zip --platform-sdk /platform/sdk/path.zip
    Copy to clipboard

The command can take 15-20 minutes to complete and generates a Triton
Docker image in a local Docker repository.

docker image ls
    Copy to clipboard

REPOSITORY   TAG      IMAGE ID       CREATED         SIZE
    qaic-triton  1.20     a0968cf3711b   3 days ago      13.6GB
    Copy to clipboard

Start the container using the docker run command with the desired image name as in the following example. The shared memory argument `--shm-size` is for
supporting ensembles and Python backends.

docker run -it --rm --privileged --shm-size=4g --ipc=host --net=host <triton-docker-image-name>:<tag> /bin/bash
    Copy to clipboard

The expected output is similar to the following:

docker ps
    CONTAINER ID   IMAGE            COMMAND                  CREATED      STATUS      PORTS     NAMES
    b88d5eb98187   qaic-triton      "/opt/tritonserver/n…"   2 days ago   Up 2 days             thirsty_beaver
    Copy to clipboard

## Create a model repository and configuration file

The model configuration file `config.pbtxt` specifies the execution properties of a
model. The file defines the input and output structure, backend, batchsize, and
parameters. You must follow Triton’s [model
repository](https://github.com/triton-inference-server/server/blob/main/docs/user_guide/model_repository.md)
and [model
configuration](https://github.com/triton-inference-server/server/blob/main/docs/user_guide/model_configuration.md)
rules while defining a configuration file.

### Configure for ONNX Runtime

For ONNX Runtime configuration file, set the platform to `onnxruntime_onnx` and the `use_qaic` parameter to true.

Parameters are key-value pairs which Triton passes to the backend runtime
environment as variables and uses in the backend processing
logic.

- `config`: Path for configuration file containing compiler options.
- `device_id`: Id of AI 100 device on which inference is targeted. (not
mandatory because the server auto picks the available device)
- `use_qaic`: Flag to indicate to use the QAic execution provider.
- `share_session`: Flag to enable the use of single session of runtime
object across model instances.

The following is an example of a `config.pbtxt` file.

name: "resnet_onnx"
    platform: "onnxruntime_onnx"
    max_batch_size : 16
    default_model_filename : "aic100/model.onnx"
    input [
      {
        name: "data"
        data_type: TYPE_FP32
        dims: [3, 224, 224 ]
      }
    ]
    output [
      {
        name: "resnetv18_dense0_fwd"
        data_type: TYPE_FP32
        dims: [1000]
      }
    ]
    parameters [
      {
        key: "config"
        value: { string_value: "1/aic100/resnet.yaml" }
      },
      {
        key: "device_id"
        value: { string_value: "0" }
      },
      {
        key: "use_qaic"
        value: { string_value: "true" }
      },
      {
        key: "share_session"
        value: { string_value: "true" }
      }
    ]
    instance_group [
      {
        count: 2
        kind: KIND_MODEL
      }
    ]
    Copy to clipboard

### Configure for QAic backend

The following is an example of a `config.pbtxt` file for a QAic backend. For a QAic backend configuration, set the `backend` parameter to `qaic`.

Parameters are user-provided key-value pairs which Triton passes the
backend runtime environment as variables and can be used in processing
logic of backend.

- `qpc_path`: Path for compiled binary of model.(programqpc.bin) (if not
provided the server searches for QPC in the model folder)
- `device_id`: Id of AI 100 device on which inference is targeted.
device is set 0 (not mandatory as the server auto picks the available
device)
- `set_size`: Size of inference queue for runtime,default is set to 20
- `no_of_activations`: Flag to enable multiple activations of a model’s
network,default is set to 1

name: "yolov5m_qaic"
        backend: "qaic"
        max_batch_size : 4
        default_model_filename : "aic100/model.onnx"
        input [
        {
           name: "images"
           data_type: TYPE_FP32
           dims: [3, 640, 640 ]
        }
        ]
        output [
        {
           name: "feature_map_1"
           data_type: TYPE_FP32
           dims: [3, 80, 80, 85]
        },
        {
           name: "feature_map_2"
           data_type: TYPE_FP32
           dims: [3, 40, 40, 85]
        },
        {
           name: "feature_map_3"
           data_type: TYPE_FP32
           dims: [3, 20, 20, 85]
        }
        ]
        parameters [
        {
           key: "qpc_path"
           value: { string_value: "/path/to/qpc" }
        },
        {
           key: "device_id"
           value: { string_value: "0" }
        }
        ]
        instance_group [
        {
           count: 2
           kind: KIND_MODEL
        }
        ]
        Copy to clipboard

### Generate the Triton configuration file

The model configuration file `config.pbtxt` is required for each model to
run on the Triton server. The `triton_config_generator.py` tool helps
to generate a minimal model configuration file if the `programqpc.bin`
or `model.onnx` file is provided. The script is in
`/opt/qti-aic/integrations/triton/release-artifacts/config-generation-script`
path inside the container.

The script takes three arguments:

- `model_repository`: Model repository for which `config.pbtxt` needs to
be generated (QAic backend)
- `all`: Generate `config.pbtxt` for ONNX (used with -model-repository)
- `model_path`: QAic model or ONNX model file path for which model
folder needs to be generated.

You can pass the `model_repository` argument, in which case the script iterates through the models and generates `config.pbtxt`
for any models that do not already have a configuration file. Use the `all` option if you also want to generate configurations for ONNX models.

Alternatively, you can provide `model_path` to generate a model folder structure with `config.pbtxt` using randomly generated model names.

## Launch a Triton server inside a container

To launch a Triton server, execute the `tritonserver` binary inside the Triton container with the model repository path as in the following example.

/opt/tritonserver/bin/tritonserver --model-repository=</path/to/repository>
    Copy to clipboard

![../../../../../../_images/triton_launch_within_container.png](data:image/png;base64,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)

## Enable Python backend for LLMs

LLM serving through Triton is enabled using `triton-qaic-backend-python`. This backend supports execution of QPC binaries for decoder-only (Causal) models, with and without KV Cache optimizations, in the LlamaForCausalLM and AutoModelForCausalLM categories.

It provides two server-to-client response modes:

- Batch: all generated tokens are cached and returned as a single response at the end of the decode stage.
- Decoupled (stream): each generated token is sent to the client as a separate response.This backend supports execution of QPC binaries for Causal models, KV Cache models of LlamaForCausalLM, AutoModelForCausalLM categories.

Two sample configurations are included: Mistral and Starcoder. Sample client scripts are also provided to test models in both stream-response and batch-response modes.

### Launch Triton server container

docker run -it --shm-size=4g --rm --privileged --net=host -v /path/to/custom/models/:/path/to/custom/models/ <triton-docker-image-name>:<tag> bash
    Copy to clipboard

### Activate Qualcomm Efficient-Transformers virtual environment

After starting the Triton container, activate the Qualcomm Efficient Transformers Python environment. Qualcomm Efficient-Transformers (qeff)
virtual environment comprises compatible packages for compilation/execution of a wide range of LLMs. Activate this environment within the container.

> 
> 
> . /opt/qeff-env/bin/activate
>     Copy to clipboard

### Generate a model repository

#### Sample models

| Model folder | Model type | Response type |
| --- | --- | --- |
| starcoder\_15b | Causal | Batch |
| starcoder\_decoupled | Causal | Decoupled (Stream) |
| mistral\_7b | KV cache | Batch |
| mistral\_decoupled | KV cache | Decoupled (Stream) |

- Pass in the QPC to the `generate_llm_model_repo.py` script available at
`/opt/qti-aic/integrations/triton/release-artifacts/llm-models/`
within the Triton container as in the following example:

python generate_llm_model_repo.py --model_name mistral_7b --aic_binary_dir <path/to/qpc> --python_backend_dir /opt/qti-aic/integrations/triton/backends/qaic/qaic_backend_python/
        Copy to clipboard

#### Use custom LLM models

- - The  `generate_llm_model_repo.py` script uses a template to
    - auto-generate the configuration for custom models. Configure required parameters
such as `use_kv_cache`, `model_name`, decoupled transaction
policy through command line options to the script. Choosing
`model_type` configures the `use_kv_cache` parameter. If not
provided, it is determined by loading QPC object which can take
several minutes for large models. This creates a model folder for in
`/opt/qti-aic/integrations/triton/release-artifacts/llm-models/llm_model_dir`.
- Configure compilation parameters such as `batch_size`, `full_batch_size`,
`prefill_seq_len`, and `ctx_len` in a JSON file. This file is copied to the
model repo. If the file is not provided, the backend compiles the model
with default configuration.
- Configuring full batch size triggers continuous batching mode. In
this mode server can handle num prompts &gt; full batch size. In regular
mode execution, the prompts are truncated/extended based on batch
size.
- If `aic_binary_dir` is provided (compiled binary is available), the backend loads this binary and skips model download and compilation.

# Sample config for compile_config.json
           {
              "num_cores": 16,
              "prefill_seq_len": 32,
              "ctx_len": 256,
              "full_batch_size": 3,
              "mxfp6_matmul": true
           }
        
        -  Keys supported in the configuration are specified below.
        
        ::
        
           'onnx_path'
           'prefill_seq_len'
           'ctx_len'
           'batch_size'
           'full_batch_size'
           'num_cores'
           'mxfp6_matmul'
           'mxint8_kv_cache'
           'aic_enable_depth_first'
           'mos'
        Copy to clipboard

python generate_llm_model_repo.py --model_name <custom_model> \
                                            --aic_binary_dir <path/to/qpc> \
                                            --compile_config_path < path/to/compilation/config/json > \
                                            --hf_model_name \
                                            --model_type <causal/kv_cache> \
                                            --decoupled
        Copy to clipboard

### Launch the Triton server and load models

- Prerequisite: You must get access for necessary models from
Hugging Face and login with Hugging Face token using
**‘huggingface-cli login’** before launching the server.
- Launch the Triton server with **llm\_model\_dir**.

/opt/tritonserver/bin/tritonserver --model-repository=<path/to/llm_model_dir>
        Copy to clipboard

### Start client container

docker run -it --rm -v /path/to/unzipped/apps-sdk/integrations/triton/release-artifacts/llm-models/:/llm-models --net=host nvcr.io/nvidia/tritonserver:22.12-py3-sdk bash
    Copy to clipboard

- Once the server has started you can run example Triton client
(client\_example\_kv.py/client\_example\_causal.py) provided to submit
inference requests to loaded models.
- Decoupled model transaction policy is supported only over gRPC
protocol. Therefore, decoupled models (stream response) use gRPC
clients whereas batch response mode uses HTTP client as a sample.

# mistral_decoupled
        python /llm-models/tests/stream-response/client_example_kv.py --prompt "My name is"
        
        # mistral_decoupled (QPC compiled for batch_size=2)
        python /llm-models/tests/stream-response/client_example_kv.py --prompt "My name is|Maroon bells"
        
        # mistral_7b
        python /llm-models/tests/batch-response/client_example_kv.py --prompt "My name is"
        
        # starcoder_decoupled
        python /llm-models/tests/stream-response/client_example_causal.py --prompt "Write a python program to print hello world"
        
        # starcoder_15b
        python /llm-models/tests/batch-response/client_example_causal.py --prompt "Write a python program to print hello world"
        Copy to clipboard

Note: For batch-response tests, the default network timeout in
`client_example_kv.py`, `client_example_causal.py` is configured as
10 min (600 sec), 100 min (6000 sec) respectively.

# User can also use generate API to do inferencing from Triton client container
    curl -X POST localhost:8000/v2/models/mistral_7b/generate -d '{"prompt": "My name is","id": "42"}'
    Copy to clipboard

## Launch Python embedding models on a Triton server

- Triton qaic Python backend for embedding models supports execution of
qpc for BERT style models. For list of supported models refer to
[cloud-ai-sdk/models/language_processing/encoder/README.md at
quic/cloud-ai-sdk ·
GitHub](https://github.com/quic/cloud-ai-sdk/blob/1.17/models/language_processing/encoder/lut_nlp_models.csv).
- This process uses the compiled binary generated by qaic-exec for serving embedding
models through Triton Python backend.
- Sample client scripts are included for models that produce sentence embeddings as outputs.

### Generate an embedding model repository

- `generate_embedding_model_repo.py` script is available at
`/opt/qti-aic/integrations/triton/release-artifacts/embedding-models/`
- This script uses a template to auto-generate the configuration for custom
models. Configure required parameters such as `model_name`,
`aic_binary_dir`, `hf_model_name` through command line options to
`generate_embedding_model_repo.py` script.
- A model folder, identified by model\_name provided, is created in required format under embedding\_model\_dir at opt/qti-aic/integrations/triton/release-artifacts/embedding-models/ config.pbtxt.

python generate_embedding_model_repo.py -h
        
        usage: generate_embedding_model_repo.py [-h] --model_name MODEL_NAME --aic_binary_dir AIC_BINARY_DIR
                                                  [--python_backend_dir PYTHON_BACKEND_DIR] --hf_model_name
                                                  HF_MODEL_NAME [--max_prompt_length MAX_PROMPT_LENGTH]
                                                  [--max_batch_size MAX_BATCH_SIZE] [--num_instances NUM_INSTANCES]
        
        options:
           -h, --help            show this help message and exit
           --model_name MODEL_NAME
                                Name of the model to generate model repo(bert-base-cased)
           --aic_binary_dir AIC_BINARY_DIR
                                Path to QPC(programqpc.bin) directory
           --python_backend_dir PYTHON_BACKEND_DIR
                                Path to QAic Python Backend Directory for Embedding models
           --hf_model_name HF_MODEL_NAME
                                Name of the model as identified on Hugging Face(google-bert/bert-base-cased)
           max_prompt_length MAX_PROMPT_LENGTH
                                Set maximum prompt length that tokenizer should support
           --max_batch_size MAX_BATCH_SIZE
                                Set maximum number of samples that should be allowed to process at same time.
                                Configure as a value less than or equal to batch size of compiled binary
           --num_instances NUM_INSTANCES
                                Set instance count. Each instance uses 1 activation on AI 100 device. Max
                                supported instance count is limited by NSP available.
           Optional: Copy the model folder to /path/to/workspace, mapped to host path, to reuse the generated model repo for future runs.
        Copy to clipboard

### Launch Triton server and load models

- Prerequisite: You can get access for necessary models from
Hugging Face and login with Hugging Face token using ‘huggingface-cli
login` before launching the server.
- Launch the Triton server with `embedding_model_dir`.

/opt/tritonserver/bin/tritonserver --model-repository=<path/to/embedding_model_dir>
        Copy to clipboard

### Start client container

1. Do the following to launch the client container:

docker run -it --rm -v /path/to/unzipped/apps-sdk/common/integrations/triton/release-artifacts/embedding-models/tests:/embedding-models/tests --net=host nvcr.io/nvidia/tritonserver:25.02-py3-sdk bash
        Copy to clipboard
2. You can run client examples after the server has started. You can use the example Triton client tests (`http_client_example.py, grpc_client_example.py, http_api_example.py`) provided to inference with models loaded.

# httpclient example with bert-base-cased model loaded on server
        python /embedding-models/tests/http_client_example.py --prompt "Earthquakes in this region are uncommon but not unexpected. It's likely people near the epicenter are going to feel aftershocks for this earthquake in the magnitude 2-3 range, and there's a small chance there can be an earthquake as large or larger, following an earthquake like this, Paul Earle, a seismologist at the USGS Earthquake Hazards Program told reporters. In terms of our operations, this is a routine earthquake. Immediately we knew this would be of high interest and important to people who don't feel earthquakes a lot." --model_name bert-base-cased
        
        # qpc compiled for batch_size>=2
        python /embedding-models/tests/http_client_example.py --prompt "Earthquakes in this region are uncommon but not unexpected. It's likely people near the epicenter are going to feel aftershocks for this earthquake in the magnitude 2-3 range, and there's a small chance there can be an earthquake as large or larger, following an earthquake like this, Paul Earle, a seismologist at the USGS Earthquake Hazards Program told reporters. In terms of our operations, this is a routine earthquake. Immediately we knew this would be of high interest and important to people who don't feel earthquakes a lot.|Earthquakes in this region are uncommon but not unexpected. It's likely people near the epicenter are going to feel aftershocks for this earthquake in the magnitude 2-3 range, and there's a small chance there can be an earthquake as large or larger, following an earthquake like this, Paul Earle, a seismologist at the USGS Earthquake Hazards Program told reporters. In terms of our operations, this is a routine earthquake. Immediately we knew this would be of high interest and important to people who don't feel earthquakes a lot." --model_name bert-base-cased
        
        # http api example with bert-base-cased model loaded on server
        python /embedding-models/tests/http_api_example.py --prompt 'Earthquakes in this region are uncommon but not unexpected. It's likely people near the epicenter are going to feel aftershocks for this earthquake in the magnitude 2-3 range, and there's a small chance there can be an earthquake as large or larger, following an earthquake like this, Paul Earle, a seismologist at the USGS Earthquake Hazards Program told reporters. In terms of our operations, this is a routine earthquake. Immediately we knew this would be of high interest and important to people who don't feel earthquakes a lot.' -m bert-base-cased -u http://localhost:8000/v2/models/bert-base-cased/infer
        Copy to clipboard

#### Benchmarking

Use GitHub `triton-inference-server/perf_analyzer` for benchmarking.

# perf analyzer example with bert-base-cased model
    # binary compiled for batch size=8, cores=2
    # num_instances/instance count set to 7 in config.pbtxt.
    
    perf_analyzer -m bert-base-cased --string-data 'Earthquakes in this region are uncommon but not unexpected. It's likely people near the epicenter are going to feel aftershocks for this earthquake in the magnitude 2-3 range, and there's a small chance there can be an earthquake as large or larger, following an earthquake like this, Paul Earle, a seismologist at the USGS Earthquake Hazards Program told reporters. In terms of our operations, this is a routine earthquake. Immediately we knew this would be of high interest and important to people who don't feel earthquakes a lot.' -b 8 --shape prompt:1 -p 10000 --concurrency 7:35:7
    Copy to clipboard

## vLLM backend for Triton

The vLLM (0.8.5) backend for Triton is a Python-based backend designed to run [supported models](https://github.com/quic/efficient-transformers/blob/release/v1.20.0/docs/source/validate.md) on the vLLM AsyncEngine. Refer to [vLLM](https://docs.qualcomm.com/doc/80-99100-3/topic/index_vLLM.html#reference-to-vllm) for more information on model.json configuration parameters, environment variables, and benchmarking support.

### Launch vLLM models

The Sample model repository for TinyLlama model is generated at `/opt/qti-aic/aic-triton-model-repositories/vllm_model` while building Triton docker with triton\_model\_repo application using [Docker](https://docs.qualcomm.com/doc/80-99100-3/topic/index_Docker_Docker.html#reference-to-docker). You can use this as is or change the model by changing the model value in `model.json`. The `model.json` file represents a key-value dictionary that is fed to the vLLM’s AsyncEngine. Modify the `model.json` as needed.

The `model.json` sample parameters are shown in the following example.

{
        "model": "model_name",
        "device_group": [0,1,2,3,4], # device_id for execution
        "max_num_seqs": <decode_bsz>, # Decode batch size
        "max_model_len": <ctx_len>, # Max Context length
        "max_seq_len_to_capture": <seq_len>, # Sequence length
        "quantization": "mxfp6", # Quantization
        "kv_cache_dtype": "mxint8", # KV cache compression
        "device": "qaic"
    }
    Copy to clipboard

For an example configuration file for vLLM models, see [config.pbtxt](https://github.com/triton-inference-server/vllm_backend/blob/r25.02/samples/model_repository/vllm_model/config.pbtxt).

- Activate the vLLM virtual environment before launching the Triton server.

source /opt/vllm-env/bin/activate
        Copy to clipboard
- Set up Hugging Face credentials.

huggingface-cli login <HF_TOKEN>
        Copy to clipboard
- Configure number of cores as per NSP availability.

export VLLM_QAIC_NUM_CORES=16
        Copy to clipboard
- Launch the Triton server.

/opt/tritonserver/bin/tritonserver --model-repository=/opt/qti-aic/aic-triton-model-repositories/vllm_model
        Copy to clipboard
- For use with `/completions` or `/chat/completions` endpoints, launch OpenAI compatible tritonserver instead of running the binary above.

    - Prerequisite:

pip install /opt/tritonserver/python/tritonserver-*.whl
            cd /opt/tritonserver/python/openai && pip install -r requirements.txt
            Copy to clipboard
- Launch an OpenAI-compatible Triton server.

python3 openai_frontend/main.py --model-repository /opt/qti-aic/aic-triton-model-repositories/vllm_model/ --tokenizer <HF_MODEL_TAG>
        Copy to clipboard

The Triton server takes a few minutes(depends on the model) to download and compile the model.

A sample [client](https://github.com/triton-inference-server/vllm_backend/blob/r25.02/samples/client.py) script is available in the sample model
repository (built as part of the Triton container using `qaic-docker`) `/opt/qti-aic/aic-triton-model-repositories/vllm_model/vllm_model`.
The sample client script (`client.py`) can be used to interface with the Triton/vLLM inference server, and can be executed from the Triton client environment.

You can also use the `generate` API to do inferencing from a Triton client container as shown in the following example:

> 
> 
> curl -X POST localhost:8000/v2/models/vllm_model/generate -d '{"text_input": "My name is","parameters":{"stream":false, "temperature": 0, "max_tokens":1000}}'
>     Copy to clipboard

Refer to [Triton OpenAI user-guide](https://docs.nvidia.com/deeplearning/triton-inference-server/user-guide/docs/client_guide/openai_readme.html) for examples about using OpenAI endpoints for inferencing, benchmarking with genai-perf tool.

## Examples

Triton example applications are released as part of the Cloud AI Apps
SDK. Inside the Triton Docker container the sample model repositories
are available at `/opt/qti-aic/aic-triton-model-repositories/`. Use the
`--model-repository` option to launch the models.

### Generate a stable diffusion model repository

To generate a stable diffusion model repository inside a Triton container, do the following:

1. Make sure you have access to gated repository [Stable Diffusion](https://huggingface.co/benjamin-paine/stable-diffusion-v1-5).

    - huggingface-cli login can be done before you run the script or,
`--auth_token` option can be passed while running the script eg:
`python generate_SD_repo.py --auth_token=<hf_token>`.
    - Run the `generate_SD_repo.py` script. The script is located at
`/opt/qti-aic/integrations/triton/release-artifacts/stable-diffusion-ensemble`
which creates a ensemble-stable-diffuison model reposoitory.
2. Start the Triton server `/opt/tritonserver/bin/tritonserver
-model-repository=/path/to/ensemble-stable-diffusion`.

    The Triton server takes about two minutes to start the first time because it
needs to compile the QPC.
3. Use the `client_example.py` for testing purpose.

Last Published: May 01, 2026

[Previous Topic
Kubernetes](https://docs.qualcomm.com/bundle/publicresource/80-99100-3/topics/index_k8s.md) [Next Topic
vLLM](https://docs.qualcomm.com/bundle/publicresource/80-99100-3/topics/index_vLLM.md)