ML Model Hosting – Benchmarking & Setup Guides

This repository provides a practical reference for hosting and serving ML/NLP models at scale.
It covers performance benchmarking across multiple model-serving platforms and step-by-step setup documentation for deploying NLP models using NVIDIA Triton Inference Server.

The repo is intended for:

• ML / AI engineers
• Platform & DevOps teams
• Architects evaluating model-serving stacks
• Teams building scalable Language AI systems

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Repository Structure

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├── model-hosting-platforms-benchmarking/
├── setup-docs/
└── README.md

📊 model-hosting-platforms-benchmarking/

This folder contains benchmarking experiments and results for hosting ML models across different serving platforms.

Purpose

To help teams compare model hosting approaches based on real-world performance and operational characteristics.

Platforms Covered (examples)

• NVIDIA Triton Inference Server
• MLflow Model Serving
• FastAPI-based custom serving
• Other popular serving frameworks (as added)

Typical Benchmark Dimensions

• Inference latency (P50 / P90 / P99)
• Throughput (requests per second)
• Concurrency handling
• Resource utilization (CPU / GPU / memory)
• Scalability behavior under load
• Error rates during peak traffic

What You’ll Find

• Benchmark configurations
• Load test scenarios
• Observed metrics & results
• Comparative analysis across platforms

This section is especially useful for platform selection decisions and capacity planning.

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🛠️ setup-docs/

This folder contains detailed setup guides for deploying various NLP and Language AI models, primarily using NVIDIA Triton Inference Server.

Purpose

To provide repeatable, production-oriented deployment instructions for common NLP workloads.

Model Types Covered

• NMT (Neural Machine Translation)
• OCR (Optical Character Recognition)
• Transliteration
• Other NLP / language models (as added)

What Each Setup Guide Typically Includes

• Model format & prerequisites
• Triton model repository structure
• config.pbtxt explanations
• Pre-processing & post-processing notes
• GPU / CPU configuration guidance
• Common pitfalls & troubleshooting tips

These guides are designed to reduce time-to-deployment and encourage best practices for scalable inference.

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🧩 How to Use This Repository

• Evaluating serving platforms?
  → Start with model-hosting-platforms-benchmarking/

• Deploying NLP models on Triton?
  → Go directly to setup-docs/

• Building a Language AI platform or sandbox?
  → Use both folders together: benchmark first, then deploy with confidence.

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📌 Notes & Scope

• Benchmarks reflect specific hardware, model sizes, and configurations—use them as reference points, not absolute numbers.
• Setup guides prioritize clarity, reproducibility, and production-readiness.
• Contributions and improvements are welcome.

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🤝 Contributions

If you’d like to:

• Add new benchmarks
• Include additional serving platforms
• Extend setup guides to new models

Feel free to open a PR or raise an issue.

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📄 License

MIT