<Phi2AI/>
AI Infrastructure Planner
Experiments

AI Infrastructure Planner

#Infrastructure#LLM#VRAM#DevOps#Hardware
Toolkit // Highlights

  • VRAM Calculation: Mathematically calculates VRAM requirements for various LLMs based on parameter count, quantization, and KV cache.

  • Vector Database Sizing: Accurately sizes RAM and Disk overhead for embedding models and vector search indexing.

  • Hardware Compilation: Automatically compiles a complete, optimized hardware suggestion tailored to your specific workflow.

Overview

Planning the infrastructure for deploying Large Language Models (LLMs) and AI agents locally or on bare-metal servers requires precise capacity planning. Miscalculating memory requirements can lead to Out-Of-Memory (OOM) errors and deployment failure, while over-provisioning leads to severely wasted budgets.

The AI Infrastructure Planner is an interactive, highly accurate capacity planning toolkit designed specifically for AI Engineers, DevOps, System Administrators, and Researchers who need to build efficient, self-hosted AI labs.

Core Capabilities

1. GPU / VRAM Calculator

The size of a model on a hard drive is not the same as its VRAM footprint during active inference. This module mathematically calculates the exact VRAM needed for Large Language Models.

You can tweak the architecture (e.g., Llama 3 27B), adjust the weight quantization (FP16, 8-bit, 4-bit), set the context length, and define the number of concurrent users. The app instantly provides a Per-User Cost and an automated GPU Compatibility matrix, showing you exactly which NVIDIA or AMD cards (and Apple Silicon) will fit your workload.

GPU and VRAM Calculator Interface

2. Vector Database Planner

For Retrieval-Augmented Generation (RAG) workflows, Vector Databases act as the memory backend. We accurately size the required RAM and Disk overhead based on your specific indexing strategy.

Input your total vector count, embedding dimensions, and target QPS. The planner provides side-by-side comparisons and utilization metrics for top open-source databases like Milvus, Weaviate, Qdrant, Chroma, and pgvector. It also breaks down the accuracy, latency, and cost of different Index Types (like HNSW vs. IVF_FLAT).

Vector Database Planner Interface

3. Base Machine Configurator

Based on the raw mathematical requirements from the VRAM and Vector DB stages, the planner automatically helps you compile a complete Bill of Materials (BOM).

It bridges the gap between software abstractions and physical hardware specifications (CPUs, System RAM, NVMe SSDs, Networking). It even calculates the estimated Power & Cooling (TDP) requirements, giving you a comprehensive estimate for your on-premise AI server deployment.

Base Machine Configurator Interface

Open Source Initiative

This planner is part of Phi2AI’s ongoing commitment to open-source infrastructure. By grounding our hardware choices in exact math rather than guesswork, we empower researchers and institutions to build cost-effective, sovereign AI labs.

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