LLM Inference

vLLM is a high-performance and memory-efficient inference engine for large language models. It uses a novel GPU KV cache management strategy to serve transformer-based models at scale, supporting multiple GPUs (including NVIDIA and AMD) with ease. vLLM enables fast decoding and efficient memory utilization, making it suitable for production-level deployments of large LLMs.

Key Features

• High Throughput Inference: Novel GPU KV caching enables faster token generation compared to traditional implementations.

• Multi-GPU Support: Scales seamlessly to multiple GPUs, including AMD (MI200s, MI300s) and NVIDIA (V100/H100/A100) resource pools.

• Easy Model Downloading: Built-in integration with Hugging Face model repositories.

• Flexible Configuration: Control precision (--dtype), context window size, parallelism (tensor-parallel-size, pipeline-parallel-size), etc.

• Lightweight & Extensible: Minimal overhead for deployment and easy to integrate with existing MLOps or monitoring solutions.

Installation and Deployment on Apolo

You can deploy vLLM on the Apolo platform using the vLLM app. Apolo automates resource allocation, persistent storage, ingress, GPU detection, and environment variable injection, so you can focus on model configuration.

Highlights of the Apolo Installation Flow:

1. Resource Allocation: Choose an Apolo preset (e.g. gpu-xlarge, mi210x2) that specifies CPU, memory, and GPU resources.

2. GPU Auto-Configuration: If your preset includes multiple GPUs, environment variables (e.g. CUDA_VISIBLE_DEVICES or HIP_VISIBLE_DEVICES) are automatically set, along with a sensible default for parallelization.

3. Ingress Setup: Enable an ingress to expose vLLM’s HTTP endpoint for external access.

4. Integration with Hugging Face: You can pass your Hugging Face token via an environment variable to pull private models.

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Apolo Deployment

Parameter Descriptions

The following parameters can be set with Apolo’s CLI (apolo run --pass-config ... install ... --set <key>=<value>). Many are optional but can be used to customize your deployment:

Resource Preset	Required. Apolo preset for resources. E.g. gpu-xlarge, H100X1, mi210x2. Sets CPU, memory, GPU count, and GPU provider.
Hugging Face Model	Required. Provide a Model Name in specified field. And Higging Face token if model is gated. E.g. sentence-transformers/all-mpnet-base-v2
Enable HTTP Ingress	Exposes an application externally over HTTPS
Hugging Face Tokenizer Name	Name or path of the huggingface tokenizer to use. If unspecified, model name or path will be used.
Server Extra Args	Optional. Specify additional args for llm. See https://docs.vllm.ai/en/v0.5.1/models/engine_args.html
Cache Config	Optional. Configure storage cache path, used to persist your model. Important for Autoscaling purposes. If not specified, PV will be created automatically and attached to the application.

Any additional chart values can also be provided through --set flags, but the above are the most common.

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Web Console UI

Step1 - Select the Preset you want to use (Currently only GPU-accelerated presets are supported)

Step2 - Select Model from HuggingFace repositories

Part 1 - vLLM app deployment

If Model is gated, please provide the HuggingFace token, as a string of Apolo Secret.

Step 3 - Install and wait for the outputs, at the Outputs section of an app

Apolo cli

Below is a streamlined example command that deploys vLLM using the app-llm-inference app that deploys to a Nvidia preset:

apolo app install -f llm.yaml --cluster <CLUSTER> --org <ORG> --project <PROJECT>

# Example of llm.yaml

template_name: "llm-inference"
input:
  preset:
   name: "gpu-l4-x1"
  hugging_face_model:
    model_hf_name: "meta-llama/Llama-3.1-8B-Instruct"
    hf_token: <INSERT_HF_TOKEN>
  ingress_http:
    http_auth: false
    enabled: true

Explanation:

• preset.name=gpu-l4-x1 requests 1 GPUs (AMD MI210). Apolo automatically sets HIP_VISIBLE_DEVICES=0,1 , ROCR_VISIBLE_DEVICES=0,1 and default parallelization flags unless overridden.

• model_hf_name: "meta-llama/Llama-3.1-8B-Instruct": The Hugging Face model to load.

• ingress_http: Creates a public domain (e.g. vllm-large.apps.<YOUR_CLUSTER_NAME>.org.neu.ro) pointing to the vLLM deployment.

Usage

import requests

API_URL = "<APP_HOST>/v1/chat/completions"

headers = {
    "Content-Type": "application/json",
}

data = {
    "model": "meta-llama/Llama-3.1-8B-Instruct",  # Must match the model name loaded by vLLM
    "messages": [
        {
            "role": "system",
            "content": "You are a helpful assistant that gives concise and clear answers.",
        },
        {
            "role": "user",
            "content": (
                "I'm preparing a presentation for non-technical stakeholders "
                "about the benefits and limitations of using large language models in our customer support workflows. "
                "Can you help me outline the key points I should include, with clear, jargon-free explanations and practical examples?"
            ),
        },
    ]
}


if __name__ == '__main__':

    response = requests.post(API_URL, headers=headers, json=data)
    response.raise_for_status()

    reply = response.text
    status_code = response.status_code
    print("Assistant:", reply)
    print("Status Code:", status_code)

References

• vLLM Official GitHub Repo

• app-llm-inference Helm Chart Repository

• Apolo Documentation (for the usage of apolo run and resource presets)

• Hugging Face Model Hub (for discovering or hosting models)