GPU Optimized LLM Inference Platform

High-performance LLM inference platform using vLLM, NVIDIA Triton Inference Server, FastAPI, and Docker for scalable GPU-accelerated AI serving.

---

Features

	Feature
⚡	High-throughput LLM serving using vLLM
🧠	OpenAI-compatible chat completion API
🚀	GPU-optimized inference pipeline
📦	NVIDIA Triton integration for tensor/model serving
🔥	FastAPI backend with REST endpoints
📊	Concurrent benchmarking support
🐳	Dockerized deployment
⚙️	Environment-based configuration
📈	Latency monitoring and performance testing

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System Architecture

                    ┌────────────────────┐
                    │   Client / User    │
                    └─────────┬──────────┘
                              │
                              ▼
                    ┌────────────────────┐
                    │    FastAPI API     │
                    │   Gateway Layer    │
                    └───────┬────────────┘
                            │
          ┌─────────────────┴─────────────────┐
          │                                   │
          ▼                                   ▼
 ┌──────────────────┐              ┌────────────────────┐
 │      vLLM        │              │  NVIDIA Triton     │
 │  LLM Inference   │              │ Inference Server   │
 │ Continuous Batch │              │  Tensor Serving    │
 │    KV Cache      │              │ Dynamic Batching   │
 └──────────────────┘              └────────────────────┘

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Tech Stack

Component	Technology
Backend API	FastAPI
LLM Serving	vLLM
GPU Model Serving	NVIDIA Triton
Containerization	Docker
Benchmarking	Python Requests + ThreadPool
Tensor Handling	NumPy
API Client	OpenAI SDK
Deployment	Docker Compose

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Why vLLM?

vLLM is optimized specifically for LLM text generation.

Key Optimizations:

• Continuous batching
• PagedAttention
• Efficient KV-cache management
• High GPU utilization
• Low latency inference

Best For: Chatbots · AI agents · Copilots · LLM APIs · Multi-user inference systems

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Why Triton?

NVIDIA Triton is used for general GPU model serving.

Triton Handles:

• Embedding models
• TensorRT pipelines
• Rerankers
• Vision models
• Speech models
• Multi-model orchestration

Current Implementation: This project includes a sample identity model to demonstrate Triton model repository structure, tensor routing, GPU inference requests, and FastAPI ↔ Triton integration.

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Project Structure

GPU_Inferencevllm/
│
├── app/
│   ├── main.py
│   ├── vllm_client.py
│   └── triton_client.py
│
├── triton/
│   └── model_repository/
│       └── identity_model/
│           ├── 1/
│           │   └── model.py
│           └── config.pbtxt
│
├── benchmark.py
├── Dockerfile
├── docker-compose.yml
├── requirements.txt
├── .env.example
└── README.md

---

Setup

1. Clone Repository

git clone https://github.com/yourusername/GPU_Inferencevllm.git
cd GPU_Inferencevllm

2. Create Virtual Environment

python -m venv .venv
source .venv/bin/activate

3. Install Dependencies

pip install -r requirements.txt

4. Configure Environment Variables

Create a .env file:

VLLM_BASE_URL=http://host.docker.internal:8000/v1
VLLM_API_KEY=dummy
VLLM_MODEL=meta-llama/Llama-3-8B-Instruct
TRITON_URL=localhost:8001

5. Start Docker Services

docker compose up --build

This starts the FastAPI backend and Triton inference server.

6. Start vLLM Server

python -m vllm.entrypoints.openai.api_server \
  --model meta-llama/Llama-3-8B-Instruct \
  --port 8000

---

API Endpoints

GET / — Root

{
  "message": "GPU Optimized LLM Inference Platform",
  "routes": ["/generate", "/triton/health", "/triton/identity"]
}

---

POST /generate — Generate Text

Request:

{
  "prompt": "Explain GPU optimization",
  "max_tokens": 100,
  "temperature": 0.2
}

Response:

{
  "prompt": "Explain GPU optimization",
  "response": "GPU optimization improves...",
  "latency_seconds": 1.42
}

---

GET /triton/health — Triton Health Check

{
  "server_live": true,
  "server_ready": true
}

---

POST /triton/identity — Triton Identity Model

Request:

{
  "values": [1.0, 2.0, 3.0]
}

Response:

{
  "input": [1.0, 2.0, 3.0],
  "output": [1.0, 2.0, 3.0],
  "latency_seconds": 0.01
}

---

Request Flow

LLM Generation:

Client → POST /generate → FastAPI → vLLM Client → vLLM Server → LLM Response

Triton Inference:

Client → POST /triton/identity → FastAPI → Triton Client → Triton Server → Tensor Output

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Benchmarking

Run concurrent benchmark tests:

python benchmark.py

Metrics collected:

• Total requests
• Requests/sec
• Average latency
• Min / Max latency

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Roadmap

• Add embedding models on Triton
• Add reranker pipelines
• TensorRT optimization
• Streaming responses
• Kubernetes deployment
• Autoscaling GPU workers
• Prometheus + Grafana monitoring
• RAG pipeline integration
• Redis KV-cache layer
• Multi-model Triton ensembles

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Enterprise Use Cases

• AI copilots
• RAG systems
• Document intelligence
• AI chat platforms
• GPU inference gateways
• Enterprise LLM serving
• Multi-model AI infrastructure

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Key Learning Outcomes

This project demonstrates:

• GPU inference optimization
• Production-grade AI serving
• vLLM architecture
• Triton inference workflows
• FastAPI orchestration
• Docker deployment
• Performance benchmarking
• Scalable AI system design

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Author

Rushi Iname