LLM Inference Hardware Requirement Calculator: Accurately Estimate Resources Needed for Large Model Deployment

A web-based open-source tool that helps developers calculate the VRAM, system memory, and GPU configuration needed to run large language models. It supports multiple quantization methods and context length settings, solves the problem of complex and error-prone manual calculations, and provides an intuitive interface with accurate calculation logic.

With the rapid development and popularization of LLMs, developers and enterprises aim to deploy LLMs locally. However, different model sizes (7B to 70B+), quantization methods (FP32/FP16/INT8/INT4), and context lengths significantly impact hardware requirements. Manual calculations are complex and error-prone, especially for accurately estimating additional memory overhead like KV cache.

1. Model size and parameter count: Input parameter count as the calculation base;
2. Quantization method selection: Support multiple precisions (FP32/FP16/INT8/INT4), which directly affects memory usage;
3. Context length and KV cache: Consider the linear growth impact of sequence length on KV cache;
4. Hardware type adaptation: Support discrete GPU systems (calculate required GPU count) and unified memory systems (estimate minimum system memory).

Output Metrics: Required VRAM (including model weights + KV cache), minimum system RAM, disk usage, number of GPUs; Tech Stack: React + TypeScript + Vite; Deployment Methods: Local development (npm install/dev), production build (npm run build), Docker deployment, GitHub Pages automatic deployment.

1. Hardware procurement decisions: Evaluate whether existing hardware can run the target model and determine the number of GPUs;
2. Model selection reference: Reverse-evaluate the model size and quantization level supported by existing hardware;
3. Cloud service cost estimation: Optimize GPU instance specifications and operating costs.

Notes: Calculations are approximate; actual memory may vary by implementation; includes KV cache overhead; unified memory assumes 75% availability; discrete GPU assumes 24GB VRAM; License: MIT open-source, allowing free use, modification, and distribution.

The tool fills the gap in hardware requirement estimation for LLM deployment, avoiding insufficient or over-provisioned resources. Future plans include supporting more quantization methods (e.g., GGUF), hardware presets, inference latency estimation, multi-modal model calculations, etc., to become an essential assistant for LLM deployment planning.