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Author: DigitLib

README.md

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+# ⚡ AI Infra Planner
+
+An interactive, highly accurate capacity planning toolkit for AI Engineers, DevOps, System Administrators and Researches.
+
+This tool mathematically calculates VRAM for Large Language Models (LLMs), accurately sizes RAM/Disk overhead for Vector Databases, and automatically compiles a complete suggestion of hardware for your workflow.
+
+[**View Live Demo**](https://digitlib.github.io/aip/)
+
+---
+
+## 🛠️ Core Features
+
+### 1. GPU / VRAM Calculator
+Calculate VRAM requirements for selected LLM workload based on real-world inference mechanics.
+* **Production-Grade Math:** Accurately splits memory into Shared (weights + framework overhead) and Per-User Cost.
+* **Architecture Aware:** Supports standard Dense models, Mixture of Experts (MoE), and calculates native KV cache reductions for MQA/GQA architectures.
+* **Granular Controls:** Tweak weight quantization (FP16, INT8, Q4), KV cache precision, context length, batch size, and concurrency.
+* **Hardware Matching:** Automatically filters a built-in database of enterprise and consumer GPUs (Nvidia H100, RTX 5090, NVIDIA Spark, etc.) to find configurations that fit your VRAM footprint.
+
+### 2. Vector Database Planner
+Size and compare open-source vector databases (Milvus, Weaviate, Qdrant, Chroma, Zvec, pgvector).
+* **Graph Overhead Precision:** Accurately calculates HNSW/graph memory bloat.
+* **Workload Tuning:** Adjust total vectors, embedding dimensions, Target QPS, High Availability (HA) replicas, and vector precision.
+* **Instant Comparison:** Visualizes RAM utilization and identifies resource use.
+* **Table Comparison:** Information on databases features and Index type.
+
+### 3. Base Machine Configurator
+Build a complete workstation/server spec around your AI workflow requirements.
+* **Smart Auto-Sizer:** Automatically populates minimum CPU core counts and System RAM based on your saved LLM and Vector DB requirements.
+* **Unified Memory Support:** Seamlessly handles dedicated GPU servers vs. Unified Memory architectures (Apple Mac Studio, NVIDIA DGX Spark).
+* **Power & Cooling:** Calculates Estimated System TDP (Thermal Design Power) and recommends 240V circuit amperage.
+* **Config Export:** Generates a clean `.txt`, complete with component specs, sub-totals, and hardware requirements.
+
+---
+
+## 🏗️ Architecture & Tech Stack
+
+This project is built for speed, privacy, and simplicity. It runs **100% locally in the browser** using static assets.
+
+* **Framework:** [Astro](https://astro.build/) (Static Site Generation)
+* **Logic:** Vanilla TypeScript / JavaScript
+* **Styling:** Pure CSS (CSS Grid, Flexbox, CSS Variables)
+* **State Management:** Browser `localStorage` (Creates a seamless "shopping cart" flow between the 3 calculators without a backend).
+
+---
+
+## 🚀 New Models or Hardware
+
+For adding new Models and/or Components, please open an Issue or a PR.

astro.config.mjs

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+// @ts-check
+import { defineConfig } from 'astro/config';
+
+// https://astro.build/config
+export default defineConfig({
+    output: 'static',        // SSG mode
+    build: { format: 'file' },
+    site: 'https://digitlib.github.io',
+    base: '/aip'
+});