Using WSL for AI App Development

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Linux is an open-source, Unix-like operating system kernel created by Linus Torvalds in 1991. It was inspired by Unix but developed as a free and open alternative. Today, Linux powers servers, cloud infrastructure, Android devices, IoT, cybersecurity, DevOps, and AI research.

Recently we have started working on a research project with a high level requirements as under:
✅ Crawls public websites for topics of your interest ( e.g. Java ).
✅ Extracts potential Java questions and generates high-quality answers consulting multiple LLMs.
✅ Uses LLMs (Large Language Models) for Q&A generation, topic classification, and logical error analysis.
✅ Runs a scheduled batch inference processing system for efficiency.
✅ Uses PostgreSQL for structured data storage and LLMs for semantic search.
✅ Optimizes inference performance on a local Linux environment using WSL and SSD-based swap.
✅ Implements benchmarking & auto-routing for cost-effective LLM selection (local hosted models and cloud hosted like Groq, GPT-4).

Virtual Box vs WSL as our Linux platform

Linux is the no brainer choice to develop , test and deploy but many developers, especially freshers and juniors, are more familiar with Windows and may not be comfortable setting up a dual-boot partition or getting a separate Linux machine. Given these constraints we had 2 choices either go with WSL or go with VirtualBox. Here is a brief comparison of WSL vs VirtualBox

1️⃣ Performance

• WSL is faster for lightweight tasks because it shares Windows resources.
• VirtualBox is slower since it runs a full virtual machine, requiring more system overhead.

2️⃣ Resource Usage

• WSL uses minimal resources and runs as a lightweight layer on Windows.
• VirtualBox allocates dedicated CPU, RAM, and storage, leading to higher resource consumption.

3️⃣ Kernel & System Architecture

• WSL 2 includes a built-in Linux kernel…