Tech Stack
Various tools example
Implementing a secure local LLM on-premise or remote requires a robust set of tools for model deployment, training, fine-tuning, and integration with existing infrastructure. Here's a detailed list of tools and software needed for each stage of implementation:
1. Model Hosting and Deployment
- Ollama: A versatile tool for deploying LLMs locally with Docker. It supports both GPU and CPU environments and ensures flexibility in configurations (Ollama).
- NVIDIA Triton Inference Server: Supports scalable inference for LLMs with GPU acceleration (NVIDIA Triton).
- TensorFlow Serving: For serving TensorFlow-based models with high throughput (TensorFlow Serving).
- Hugging Face Inference Endpoints: Simplifies deploying transformer-based models on secure environments (Hugging Face).
- ONNX Runtime: Optimized for running LLMs across various platforms (ONNX Runtime).
2. Training and Fine-Tuning
- PyTorch: Essential for training and fine-tuning LLMs (PyTorch).
- TensorFlow: Popular framework for building and customizing AI models (TensorFlow).
- Hugging Face Transformers: For adapting pre-trained models to specific tasks (Hugging Face Transformers).
- Weights & Biases (W&B): Tracks experiments, models, and datasets during training (W&B).
3. Data Management and Preprocessing
- Apache Airflow: Manages data pipelines and preprocessing workflows (Apache Airflow).
- Pandas: For handling and processing structured data (Pandas).
- DVC (Data Version Control): Tracks data versioning for reproducible AI (DVC).
4. Security and Compliance
- HashiCorp Vault: Securely manages secrets and sensitive data (Vault).
- Cloudflare Zero Trust: Ensures secure access to LLM APIs (Cloudflare Zero Trust).
- Kubernetes Secrets: Manages sensitive information in containerized deployments (Kubernetes Secrets).
5. Monitoring and Optimization
- Grafana: Real-time dashboards for monitoring LLM performance (Grafana).
- Prometheus: Tracks metrics and logs for AI models (Prometheus).
- N8N: Integrates AI workflows with external systems, simplifying automation (N8N).
6. Collaboration and Management
- GitHub/GitLab: Version control for code and model artifacts (GitHub, GitLab).
- MLflow: Tracks models and experiments from end to end (MLflow).
7. Additional Tools
- LangChain: Enhances interaction with LLMs, enabling chaining of queries and decision-making (LangChain).
- FastAPI: Builds APIs to serve LLM functionalities (FastAPI).
- Docker: Simplifies containerized deployments (Docker).
Key Resources
By combining these tools, companies can create a secure, efficient, and privacy-compliant local LLM deployment that integrates seamlessly into their workflows. For more detailed guidance, these resources provide further insights and tutorials.