Wuhr AI VRAM Insight

Live Demo | Report Issues | Feature Request | GitHub Repository | Blog

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📖 Language / 语言

English | 中文

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📖 Table of Contents

• Features
• Version Highlights
• Tech Stack
• VRAM Calculation Formulas
• Supported Models
• Quick Start
• Docker Deployment
• Project Structure
• API Documentation
• MCP Protocol Support
• Contributing
• License

🖼️ Demo Screenshots

Main Interface - Training VRAM Calculator

Professional training VRAM calculator interface supporting model parameters, batch size, sequence length, precision configuration, with real-time VRAM requirements display and GPU recommendations

Single-card GPU Recommendation System

Multi-machine Multi-card GPU Recommendation System

Intelligent GPU recommendation system that automatically matches the most suitable GPUs based on computational requirements, including utilization analysis and price comparison

✨ Features

🆕 Version Highlights

• 🔥 Advanced Fine-Tuning Support: New dedicated advanced fine-tuning calculator with 4 model types (NLP, Multimodal, MoE, CNN)
• ⚡ Parameter-Level VRAM Control: Precise control over model architecture parameters (modelSize, hiddenSize, layers, etc.)
• 🛠️ Recently Fixed: Major modelSize parameter fixes for NLP and Multimodal models - now correctly affects VRAM calculations
• 📊 Intelligent Tab Ordering: Inference→Fine-tuning→Training→GRPO→Advanced Fine-tuning, aligned with usage frequency
• 🎯 Smart Model Classification: Complete isolation between NLP models and multimodal models
• 📈 Correct Calculation Formulas: All calculation formulas rewritten based on unified LLM framework

Core Features

• 🎯 Six Calculation Modes: Inference, Fine-tuning, Training, GRPO, Multimodal, Advanced Fine-tuning
• 📊 Precise Calculations: VRAM calculation formulas based on latest engineering practices and unified LLM framework
• 🔧 Advanced Fine-tuning: Dedicated calculator for NLP, Multimodal, MoE, and CNN models with parameter-level control
• 🎨 Visualization: Pie charts showing VRAM composition for intuitive understanding of each component's proportion
• 💾 History Records: Automatic saving of calculation history with comparison analysis support
• 🔧 Configuration Presets: 12+ preset templates for quick calculation start
• 📱 Responsive Design: Perfect adaptation for mobile and desktop

Advanced Features

• 🌙 Dark Mode: Eye protection with system theme following
• ⚡ PWA Support: Installable as local application with offline usage support
• 🔗 Result Sharing: Generate sharing links and export calculation reports
• ⌨️ Keyboard Shortcuts: Improve operational efficiency
• 📈 Performance Monitoring: Real-time application performance monitoring
• 🛡️ Error Handling: Intelligent error prompts and recovery
• 🤖 MCP Protocol Support: Support for Model Context Protocol, enabling AI assistants to directly call VRAM calculation functions

Data Support

• 130+ Pre-trained Models: Covering mainstream Chinese and international open-source models with intelligent classification
• 22+ Multimodal Models: Supporting Qwen2.5-VL, QwQ-VL, LLaVA, Whisper, etc.
• 12+ Vector Models: Supporting Qwen3-Embedding, Qwen3-Reranker series
• 20+ GPU Specifications: From consumer-grade to data center-grade, including latest RTX 50 series
• Smart Recommendations: Recommend suitable GPUs based on VRAM requirements
• Price Analysis: GPU cost-effectiveness comparison

🛠 Tech Stack

• Framework: Next.js 15.3 + React 19
• Language: TypeScript 5.0
• Styling: Tailwind CSS + Glassmorphism Design
• State Management: Zustand
• Animation: Framer Motion
• Charts: Recharts
• Tools: ESLint, Prettier, Husky

📚 Supported Models

🤖 NLP/Language Models (95+ models)

Qwen Series

• Qwen2.5: 0.5B, 1.5B, 3B, 7B, 14B, 32B, 72B
• Qwen3: 1.8B, 7B, 14B, 32B, 72B

DeepSeek Series

• DeepSeek-V3-671B (Full MoE, 37B active)
• DeepSeek-V3-0324 (Latest optimized version)
• DeepSeek-R1-671B (Full reasoning model)
• DeepSeek-R1-0528 (Latest reasoning model, 685B parameters)
• deepseek-ai/DeepSeek-R1-0528 (Official Hugging Face version)
• deepseek-ai/DeepSeek-R1-0528-Qwen3-8B (8B reasoning model based on Qwen3)
• DeepSeek-R1 Series: 1.5B, 7B, 8B, 14B, 32B, 70B
• DeepSeek-Coder: 1.3B, 6.7B, 33B
• DeepSeek-MoE-16B

Llama Series

• Llama-3.1: 8B, 70B, 405B
• Llama-2: 7B, 13B, 70B

ChatGLM Series

• GLM-4-Plus (100B, Zhipu's latest large model)
• GLM-Z1-32B (Reasoning model, competing with OpenAI o1)
• GLM-4-9B, ChatGLM3-6B, ChatGLM4-9B

Yi Series (01.AI)

• Yi-Lightning (1000B MoE, 50B active)
• Yi-Large (100B)
• Yi-Medium (200B MoE, 20B active)
• Yi-6B, Yi-34B

Other Chinese Open-Source Models

• Qwen3 Series: 1.8B, 7B, 14B, 32B, 72B (Alibaba's latest)
• Qwen3-Embedding: 0.6B, 4B, 8B (Vector models)
• Qwen3-Reranker: 0.6B, 4B, 8B (Reranking models)
• MiniMax-ABAB6.5: 70B, 100B (MiniMax)
• Moonshot-v1: 32K/128K (Moonshot AI)
• Step-1V (300B multimodal), Step-2 (800B MoE) (StepFun)
• InternLM2.5: 7B, 20B (InternLM)
• Spark-Max (340B MoE), Spark-Pro (175B) (iFLYTEK)
• Baichuan2: 7B, 13B

International Mainstream Models

• Mistral-7B, Mixtral-8x7B
• Gemma: 2B, 7B
• Phi-3: Mini(3.8B), Small(7B)
• CodeLlama: 7B, 13B, 34B

🎨 Multimodal Models (22+ models) 🆕

Vision-Language Models

• Qwen2.5-VL Series: 3B, 7B, 32B, 72B
• QwQ-VL-72B: Reasoning multimodal model with strong visual reasoning capabilities
• LLaVA Series: 1.5-7B, 1.5-13B, NeXT-34B
• Idefics2-8B: High-quality visual understanding

Audio Models

• Whisper Series: Large-v3, Medium, Small
• OpenOmni-7B: Multimodal dialogue

Video Understanding Models

• Video-LLaMA-7B: Video content understanding
• Jamba-1.5-Mini: Document+video understanding

Multimodal Dialogue Models

• Phi-4-Multimodal: Microsoft's latest multimodal model
• Nougat-Base: Document understanding specialized

🔍 Vector Models (12+ models) 🆕

Qwen Vector Model Series

• Qwen3-Embedding: 0.6B, 4B, 8B (Text vectorization)
• Qwen3-Reranker: 0.6B, 4B, 8B (Document reranking)

🎯 Smart Model Classification

• NLP Grouping: Only displays text models with transformer, glm, moe architectures
• Multimodal Grouping: Only displays multimodal models with multimodal architecture
• Complete Isolation: Avoids model selection confusion, improves user experience

📐 VRAM Calculation Formulas

Precise calculation formulas based on unified LLM framework and latest engineering practices. For detailed documentation, see VRAM Calculation Formulas.

🔬 Unified LLM Framework

Total VRAM Usage = Model Weights + Optimizer States + Gradients + Activations + Other Overheads

All calculators follow this framework, with the key difference being P_train (trainable parameters).

1. Inference VRAM Calculation

Total VRAM = Quantized Model Weights + KV Cache + Activations (minimal)

Where:
- Quantized Model Weights = P_total × precision_bytes × quantization_ratio
- KV Cache = batch_size × seq_len × hidden_size × layers × 2 × precision_bytes
- Activations = 10% of training activations (smaller during inference)

2. Fine-tuning VRAM Calculation

Full Fine-tuning

P_train = P_total (all parameters require gradients)
Total VRAM = Model Weights + (P_train × optimizer_factor) + (P_train × gradient_precision) + Activations

PEFT Methods (LoRA/QLoRA/Prefix)

P_train << P_total (only few parameters require gradients)

LoRA: P_train = calculateLoRAParams(rank), approximately 1% of total parameters
QLoRA: Base model quantization + LoRA parameters
Prefix: P_train = 1% × P_total

3. Training VRAM Calculation

P_train = P_total (full training)
Total VRAM = Model Weights + Optimizer States + Gradients + Activations + Other Overheads

Where:
- Optimizer States = P_total × 4bytes × optimizer_factor (SGD=1, AdamW=2)
- Gradients = P_total × training_precision_bytes
- Activations support gradient checkpointing (70% reduction)

4. GRPO VRAM Calculation 🆕

Core Feature: Activations = k × SFT Activations, where k is the preference group size

GRPO Activations = k × SFT Activations
Where k = numGenerations (preference group size)

Comparison:
- SFT: Activations = 1 × base
- DPO: Activations ≈ 2 × base  
- GRPO(k=4): Activations = 4 × base
- GRPO(k=8): Activations = 8 × base

Typically uses PEFT methods:
- Model Weights: INT4 quantization (8x compression)
- P_train = 1% × P_total (LoRA etc.)
- VRAM Bottleneck: Activations component

5. Multimodal VRAM Calculation 🆕

Core: Total_Sequence_Length determines activation VRAM

Total_Sequence_Length = Text Tokens + Image Patches + Audio Patches + Video Patches

Where:
- Image Sequence Length = (resolution/patch_size)² × number_of_images
- Video Sequence Length = frames × patches_per_frame (source of sequence length explosion)
- Audio Sequence Length = duration(ms) / 80ms

Activation VRAM = batch_size × Total_Sequence_Length × hidden_size × layers × precision_bytes

6. Advanced Fine-Tuning VRAM Calculation 🆕

Recently Fixed: modelSize parameters now correctly affect VRAM calculations

NLP Model Fine-Tuning

Total VRAM = Model Weights + Optimizer States + Gradients + Activations + KV Cache

Where:
- Model Weights = max(calculated_params, modelSize × 1e9) × precision_bytes
- All components now properly scale with modelSize parameter
- Fixed: 7B→14B now correctly shows ~130GB VRAM increase

Multimodal Model Fine-Tuning

Total VRAM = Vision Encoder + Text Encoder + Fusion Layer + Training Components

Where:
- Vision Encoder = max(calculated_vision, modelSize × 0.3) × precision_bytes
- Text Encoder = max(calculated_text, modelSize × 0.5) × precision_bytes
- Fixed: 7B→72B now correctly shows ~693GB VRAM increase

MoE Model Fine-Tuning (Already Working)

Expert Memory = (modelSize / numExperts) × numActiveExperts × precision_bytes
Inverse relationship: More experts = smaller per-expert size = less active memory

CNN Model Fine-Tuning (Already Working)

Total VRAM = Convolutional Layers (80%) + Fully Connected Layers (20%) + Feature Maps
All components correctly scale with modelSize parameter

Precision Bytes Reference Table

Precision Type	Bytes	Description
FP32	4	Single precision float
FP16/BF16	2	Half precision float
INT8	1	8-bit integer quantization (4x compression)
INT4	0.5	4-bit integer quantization (8x compression)

Quantization Ratio Reference Table

Quantization Type	Compression Ratio	VRAM Savings
None	1.0	0%
INT8	0.25	75%
INT4	0.125	87.5%

🚀 Quick Start

Requirements

• Node.js 18+
• npm or yarn

Installation Steps

# Clone repository
git clone https://github.com/st-lzh/vram-wuhrai.git
cd vram-wuhrai

# Install dependencies
npm install

# Start development server
npm run dev

# Build production version
npm run build

# Start production server
npm start

Visit http://localhost:3000 to view the application

🐳 Docker Deployment

Using Docker Compose (Recommended)

1. Create docker-compose.yml:

version: '3.8'

services:
  app:
    image: wuhr/vram-calculator:latest
    ports:
      - "3000:3000"
    environment:
      - NODE_ENV=production
    restart: unless-stopped
    healthcheck:
      test: ["CMD", "curl", "-f", "http://localhost:3000/api/health"]
      interval: 30s
      timeout: 10s
      retries: 3
      start_period: 40s

  # Optional: Nginx reverse proxy
  nginx:
    image: nginx:alpine
    ports:
      - "80:80"
      - "443:443"
    volumes:
      - ./nginx.conf:/etc/nginx/nginx.conf:ro
      - ./ssl:/etc/nginx/ssl:ro
    depends_on:
      - app
    restart: unless-stopped

1. Start services:

# Start all services
docker-compose up -d

# View logs
docker-compose logs -f

# Stop services
docker-compose down

Using Docker Build

# Build image
docker build -t vram-calculator .

# Run container
docker run -d \
  --name vram-calculator \
  -p 3000:3000 \
  -e NODE_ENV=production \
  --restart unless-stopped \
  vram-calculator

Performance Optimization Configuration

• First Load: 178KB (Ultimate optimization)
• Code Splitting: Lazy loading all calculator components
• PWA Cache: Offline available
• Web Worker: Background computation without blocking UI

Kubernetes Deployment

apiVersion: apps/v1
kind: Deployment
metadata:
  name: vram-calculator
spec:
  replicas: 3
  selector:
    matchLabels:
      app: vram-calculator
  template:
    metadata:
      labels:
        app: vram-calculator
    spec:
      containers:
      - name: app
        image: wuhr/vram-calculator:latest
        ports:
        - containerPort: 3000
        env:
        - name: NODE_ENV
          value: "production"
        resources:
          requests:
            memory: "256Mi"
            cpu: "100m"
          limits:
            memory: "512Mi"
            cpu: "500m"
---
apiVersion: v1
kind: Service
metadata:
  name: vram-calculator-service
spec:
  selector:
    app: vram-calculator
  ports:
  - port: 80
    targetPort: 3000
  type: LoadBalancer

📁 Project Structure

ai-memory-calculator/
├── src/
│   ├── app/                    # Next.js App Router
│   │   ├── layout.tsx         # Root layout
│   │   ├── page.tsx           # Main page (secondary tab layout)
│   │   └── api/               # API routes
│   ├── components/            # React components
│   │   ├── calculators/       # Calculator components
│   │   │   ├── inference-calculator.tsx      # Inference calculator
│   │   │   ├── fine-tuning-calculator.tsx    # Fine-tuning calculator  
│   │   │   ├── training-calculator.tsx       # Training calculator
│   │   │   ├── grpo-calculator.tsx          # GRPO calculator
│   │   │   └── multimodal-calculator.tsx    # Multimodal calculator
│   │   ├── ui/               # UI components
│   │   └── ...
│   ├── hooks/                # Custom Hooks
│   ├── lib/                  # Utility libraries
│   │   └── models-data.ts    # 70+ model database + architecture classification
│   ├── store/                # Zustand state management
│   ├── types/                # TypeScript types
│   └── utils/                # Utility functions
│       └── memory-formulas.ts # Unified LLM framework calculation formulas
├── public/                   # Static assets
│   ├── workers/             # Web Workers
│   └── ...
├── docs/                    # Detailed documentation
│   ├── VRAM_CALCULATION_FORMULAS.md # Comprehensive calculation formula documentation (EN)
│   ├── VRAM_CALCULATION_FORMULAS.zh.md # Comprehensive calculation formula documentation (ZH)
│   ├── memory-calculation-formulas.md # Legacy calculation formula details
│   └── deployment.md       # Deployment guide
├── docker-compose.yml       # Docker orchestration
├── Dockerfile              # Docker image
├── next.config.ts         # Next.js configuration
└── package.json          # Project configuration

📚 API Documentation

Health Check

GET /api/health

Response:

{
  "status": "ok",
  "timestamp": "2024-01-01T00:00:00.000Z",
  "version": "1.0.0",
  "uptime": 3600
}

Performance Analytics

POST /api/analytics
Content-Type: application/json

{
  "event": "calculation",
  "type": "training",
  "duration": 150,
  "timestamp": "2024-01-01T00:00:00.000Z"
}

🤖 MCP Protocol Support

This project supports Model Context Protocol (MCP), enabling AI assistants to directly call VRAM calculation functions through a standardized protocol.

MCP Server Information

• Server Name: vram-calculator-mcp-server
• Protocol Version: 2024-11-05
• Endpoint: http://localhost:3001/api/mcp

Supported Features

📚 Resources

• Model Database: 130+ pre-trained model information
• GPU Specifications: 20+ GPU detailed specs and pricing
• Calculation Formulas: VRAM calculation formula documentation
• History Records: Calculation history and statistics

🔨 Tools

• VRAM Calculation: Inference, training, fine-tuning, GRPO, multimodal calculations
• GPU Recommendation: Intelligent GPU recommendations and cost analysis
• Configuration Optimization: Automatic configuration tuning and optimization suggestions

💬 Prompts

• Optimization Advice: Professional VRAM optimization suggestions
• GPU Selection: GPU selection guidance
• Technical Diagnosis: Problem diagnosis and solutions

Quick Start

Method 1: npm Global Installation (Recommended)

# Install MCP server
npm install -g vram-calculator-mcp-server

# Start MCP server
vram-calculator-mcp

Claude Desktop Integration:

{
  "mcpServers": {
    "vram-calculator": {
      "command": "vram-calculator-mcp"
    }
  }
}

Method 2: Local Development Mode

npm run dev
# MCP endpoint: http://localhost:3001/api/mcp

2. Connection Test

curl -X POST http://localhost:3001/api/mcp \
  -H "Content-Type: application/json" \
  -d '{
    "jsonrpc": "2.0",
    "method": "initialize",
    "params": {
      "protocolVersion": "2024-11-05",
      "capabilities": {},
      "clientInfo": {"name": "test-client", "version": "1.0.0"}
    },
    "id": 1
  }'

3. Call GPU Recommendation Tool

curl -X POST http://localhost:3001/api/mcp \
  -H "Content-Type: application/json" \
  -d '{
    "jsonrpc": "2.0",
    "method": "tools/call",
    "params": {
      "name": "recommend_gpu",
      "arguments": {"vramRequired": 16, "useCase": "training"}
    },
    "id": 2
  }'

AI Assistant Integration Example

// Initialize MCP connection
const mcpClient = new MCPClient('http://localhost:3001/api/mcp');
await mcpClient.initialize();

// Get GPU recommendations
const recommendation = await mcpClient.callTool('recommend_gpu', {
  vramRequired: 24,
  useCase: 'training'
});

// Read model information
const models = await mcpClient.readResource('models://nlp');

Detailed Documentation

• 📖 MCP Implementation Summary
• 🎯 MCP Usage Examples
• 🧪 Test Script
• 📦 npm Package

🤝 Contributing

We welcome all forms of contributions!

How to Contribute

1. Fork this repository
2. Create feature branch (git checkout -b feature/AmazingFeature)
3. Commit changes (git commit -m 'Add some AmazingFeature')
4. Push to branch (git push origin feature/AmazingFeature)
5. Open Pull Request

Development Standards

• Follow TypeScript and React best practices
• Use ESLint and Prettier to maintain consistent code style
• Write clear commit messages
• Add tests for new features
• Update relevant documentation

Calculation Formula Contributions

If you find calculation formula issues or want to add new algorithm support:

1. Describe the problem or requirement in Issues
2. Provide relevant papers or technical documentation
3. Provide reference implementation if possible

Report Issues

Use GitHub Issues to report problems, please include:

• Problem description
• Reproduction steps
• Expected behavior
• Screenshots (if applicable)
• Environment information

🏆 Future Development Goals

Short-term Goals (2025 Q1-Q2)

🔧 Technical Enhancement

• Model Support Expansion

  • Add 50+ latest LLM support (GPT-4o, Claude-3.5, Gemini-2.0)
  • Support new Chinese models (Kimi-k1, Doubao-pro, etc.)
  • Integrate latest multimodal models (GPT-4V, Gemini-Vision)

• Calculation Precision Improvement

  • Implement distributed training VRAM calculation (multi-node, multi-GPU)
  • Add memory optimization algorithms (gradient accumulation, mixed precision)
  • Support new training paradigms (MoE training, sparse training)

• Performance Optimization

  • Reduce first load time to under 100KB
  • Implement advanced caching strategies
  • Add calculation result prediction cache

🎨 User Experience Enhancement

• Interactive Features

  • Real-time calculation result comparison
  • VRAM usage timeline visualization
  • Interactive GPU selection wizard

• Advanced Analytics

  • Cost analysis calculator (GPU rental costs)
  • Training time estimation
  • Power consumption calculation

Medium-term Goals (2025 Q3-Q4)

🚀 Platform Integration

• API Services

  • RESTful API for third-party integration
  • Developer CLI tools
  • GitHub Action integration for CI/CD

• Enterprise Features

  • Multi-user workspace support
  • Calculation result export (PDF, Excel)
  • Custom model database management

🌍 Community Building

• Internationalization

  • Support for 10+ languages
  • Regional model databases (Japan, Europe, etc.)
  • UI/UX cultural adaptation

• Documentation & Education

  • Interactive tutorials for beginners
  • Video course series
  • Technical blog articles

Long-term Vision (2026+)

🔬 Research & Innovation

• AI-Driven Features

  • Intelligent model recommendation based on task requirements
  • Automatic optimization suggestions
  • Machine learning predictive VRAM analysis

• Advanced Computing Support

  • Quantum computing VRAM estimation
  • Edge device deployment calculation
  • Federated learning resource estimation

🌐 Ecosystem Development

• Platform Ecosystem

  • Custom calculator plugin system
  • Major cloud platform integration (AWS, Azure, GCP)
  • Mobile application development (iOS, Android)

• Research Collaboration

  • Academic institution partnerships
  • VRAM research open datasets
  • VRAM calculation methodology standardization

📈 Metrics & Goals

Performance Goals

• Load Time: <100KB first load by Q2 2025
• Accuracy: 95%+ calculation accuracy for mainstream models
• Coverage: Support 200+ models by end of 2025

Community Goals

• Users: 10,000+ monthly active users by end of 2025
• Contributors: 50+ open-source contributors
• Documentation: 100+ technical articles and tutorials

Technical Debt & Maintenance

• Code Quality: Maintain 95%+ test coverage
• Security: Regular security audits and updates
• Dependencies: Keep all dependencies up-to-date
• Compatibility: Support latest web standards and frameworks

🤝 How to Participate

We welcome contributions in various areas:

1. Development: Contribute core features and bug fixes
2. Documentation: Help improve and translate documentation
3. Research: Contribute to calculation formula accuracy
4. Community: Help answer questions and support users
5. Testing: Help identify and report issues

Join our GitHub Discussions to participate in feature planning and technical discussions.

🏆 Changelog

v2.1.0 (2024-12-19) 🎉

• 🛠️ Major Parameter Fix: Fixed critical modelSize parameter issues in NLP and Multimodal models
• ✨ Advanced Fine-Tuning Calculator: New dedicated calculator for 4 model types (NLP, Multimodal, MoE, CNN)
• 🔧 Parameter-Level Control: Precise control over model architecture parameters (hiddenSize, layers, etc.)
• 📊 Systematic Validation: All 8 core parameters tested and verified to work correctly
• 📚 Comprehensive Documentation: New detailed VRAM calculation formula documentation
• 🎯 Enhanced Tab Order: Added Advanced Fine-tuning tab for professional users

v2.0.0 (2024-06-23) 🎉

• ✨ Added Multimodal Model Support: Independent grouping supporting text+image+audio+video
• ✨ Added GRPO Algorithm Calculation: Correctly reflects k-fold activation value effect of preference group size
• 🔧 Refactored Calculation Formulas: Based on unified LLM framework, all formulas unified to standard
• 🎯 Optimized Tab Order: Inference→Fine-tuning→Training→GRPO, aligned with usage frequency
• 🎨 Smart Model Classification: Complete isolation between NLP and multimodal models
• 📈 Expanded Model Database: Added Chinese open-source models and multimodal models, total 100+ models
• 🚀 Performance Optimization: First load optimized to 178KB

v1.0.0 (2024-01-01)

• 🎉 Initial release
• Support for training, inference, fine-tuning modes
• 50+ NLP model support
• GPU recommendation system

📄 License

This project is licensed under the MIT License - see the LICENSE file for details

🙏 Acknowledgments

• Thanks to all contributors
• VRAM calculation formulas based on community best practices
• Uses excellent open-source projects: Next.js, React, Tailwind CSS, etc.

📞 Contact Us

• Blog: https://wuhrai.com
• Model API: https://ai.wuhrai.com
• Model Chat: https://gpt.wuhrai.com
• Email: 1139804291@qq.com
• GitHub: @wuhr-ai

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