Hardware Requirements

OpenTranscribe is designed to run on a wide range of hardware configurations, from minimal setups to high-performance systems. This guide outlines the requirements and recommendations for different use cases.

Minimum Requirements

Basic CPU-Only Setup

For small-scale testing or low-volume transcription work:

• CPU: Modern x86-64 processor (4+ cores)
• RAM: 8GB minimum
• Storage: 20GB free space (10GB for Docker images, 10GB for models and data)
• Operating System: Linux, macOS, or Windows with WSL2
• Docker: Docker Engine 20.10+ and Docker Compose 2.0+

Performance:

• Transcription speed: ~0.5-1x realtime (slower than playback)
• Suitable for: Testing, development, occasional use

CPU-Only Limitations

CPU-only mode provides significantly slower transcription speeds (0.5-1x realtime) compared to GPU acceleration (70x realtime). For production use or regular transcription work, GPU acceleration is strongly recommended.

Recommended Configuration

GPU-Accelerated Setup

For regular use and production deployments:

• CPU: Modern multi-core processor (8+ cores recommended)
• RAM: 16GB minimum, 32GB recommended
• GPU: NVIDIA GPU with 8GB+ VRAM
  • RTX 3060 (12GB) - Good entry-level option
  • RTX 3080/3090 (10-24GB) - Excellent performance
  • RTX 4070/4080/4090 (12-24GB) - Best consumer GPUs
  • Tesla T4 (16GB) - Good for servers/cloud
  • A4000/A5000/A6000 (16-48GB) - Professional workstations
• CUDA: CUDA 11.8+ with compatible drivers
• Storage: 50GB+ free space
  • 15GB for Docker images
  • 3GB for AI models (cached locally)
  • 32GB+ for media files and database
• Network: 100Mbps+ for YouTube downloads

Performance:

• Transcription speed: 70x realtime with large-v2 model
• Example: 1-hour video processes in ~50 seconds
• Suitable for: Production use, high-volume transcription

GPU Memory Requirements

• Transcription only: 4-6GB VRAM
• Transcription + Diarization: 6-8GB VRAM
• Multiple concurrent jobs: 10-12GB+ VRAM

High-Performance Configuration

Multi-GPU Setup

For maximum throughput and parallel processing:

• CPU: High-end multi-core processor (16+ cores)
• RAM: 64GB+ for optimal performance
• GPUs: Multiple NVIDIA GPUs (2-4 recommended)
  • Dedicated GPU for transcription workers
  • Separate GPU for LLM inference (if using local LLM)
• Storage: NVMe SSD with 200GB+ free space
• Network: 1Gbps+ for bulk operations

Multi-GPU Configuration Example:

# Example: 3 GPU setup
GPU 0: RTX A6000 (49GB) - Local LLM inference
GPU 1: RTX 3080 Ti (12GB) - Single transcription worker (default)
GPU 2: RTX A6000 (49GB) - 4 parallel transcription workers (scaled)

See Multi-GPU Scaling for configuration details.

Performance:

• With GPU scaling: Process 4+ videos simultaneously
• Transcription speed: 70x realtime per worker
• Total throughput: 280x realtime (4 workers)

Cloud & Server Deployments

AWS / Cloud Provider Recommendations

Use Case	Instance Type	GPU	vCPU	RAM	Estimated Cost
Light Use	g4dn.xlarge	T4 (16GB)	4	16GB	~$0.52/hour
Standard	g4dn.2xlarge	T4 (16GB)	8	32GB	~$0.75/hour
High Performance	p3.2xlarge	V100 (16GB)	8	61GB	~$3.06/hour
Multi-GPU	p3.8xlarge	4x V100	32	244GB	~$12.24/hour

Cost Optimization

• Use spot instances for non-critical workloads (50-70% cost savings)
• Stop instances when not in use
• Consider reserved instances for 24/7 deployments (40-60% savings)

Self-Hosted Server

For on-premise deployments:

• Form Factor: Rackmount server or workstation
• CPU: Xeon or EPYC processor (12-32 cores)
• RAM: 64-128GB ECC memory
• GPU: Professional GPUs (A4000, A5000, A6000 series)
• Storage:
  • 500GB-2TB NVMe SSD for OS and Docker
  • 4-16TB HDD/SSD for media storage
  • RAID configuration for redundancy
• Network: 10GbE for NAS integration
• Power: Redundant PSU recommended
• Cooling: Adequate cooling for GPU workloads

Storage Considerations

Model Cache

AI models are cached locally to avoid re-downloading:

Model Category	Size	Purpose
WhisperX Models	~1.5GB	Speech recognition
PyAnnote Models	~500MB	Speaker diarization
Wav2Vec2 Model	~360MB	Word alignment
Sentence Transformers	~80MB	Semantic search
NLTK Data	~13MB	Text processing
Total	~2.5GB	All AI models

Configuration: Set MODEL_CACHE_DIR in .env (default: ./models)

Media Storage

Plan storage based on your use case:

Content Type	Typical Size	100 Files	1000 Files
Audio (podcast)	50-100MB	5-10GB	50-100GB
Video (meeting)	200-500MB	20-50GB	200-500GB
Video (1080p)	1-2GB	100-200GB	1-2TB
Video (4K)	4-8GB	400-800GB	4-8TB

Add 10-20% overhead for:

• Database storage (transcripts, metadata)
• OpenSearch indices
• Generated waveforms and thumbnails

Network Requirements

Bandwidth

• Minimum: 10Mbps for basic operation
• Recommended: 100Mbps+ for YouTube downloads
• Optimal: 1Gbps for bulk operations and NAS integration

Ports

The following ports must be available:

Service	Default Port	Purpose
Frontend	5173	Web interface
Backend API	5174	API endpoints
Flower	5175	Task monitoring
MinIO	5176-5179	Object storage
OpenSearch	5180	Search engine
PostgreSQL	5432	Database (internal)
Redis	6379	Task queue (internal)

Operating System Support

Officially Supported

• Linux: Ubuntu 20.04+, Debian 11+, RHEL 8+, CentOS Stream 9+
• macOS: macOS 12+ (CPU-only, no NVIDIA GPU support)
• Windows: Windows 10/11 with WSL2 (GPU support via WSL2 CUDA)

GPU Support by Platform

Platform	NVIDIA GPU	Notes
Linux	✅ Full support	Best performance
macOS	❌ Not supported	Apple Silicon uses CPU
Windows	✅ via WSL2	CUDA in WSL2 required

Verification Commands

Check your system meets requirements:

# Check Docker version
docker --version
docker compose version

# Check NVIDIA GPU
nvidia-smi

# Check available memory
free -h

# Check disk space
df -h

# Check CPU cores
nproc

Performance Benchmarks

Real-world performance examples:

Entry-Level GPU (RTX 3060 12GB)

• 1-hour audio: ~50 seconds
• 3-hour meeting: ~2.5 minutes
• Concurrent jobs: 1-2 recommended

Mid-Range GPU (RTX 3080 12GB)

• 1-hour audio: ~50 seconds
• 3-hour meeting: ~2.5 minutes
• Concurrent jobs: 2-3 with GPU scaling

High-End GPU (RTX A6000 48GB)

• 1-hour audio: ~50 seconds
• 3-hour meeting: ~2.5 minutes
• Concurrent jobs: 4-6 with GPU scaling
• Can handle local LLM + transcription simultaneously

Upgrade Paths

CPU → GPU

Simplest upgrade for dramatic performance improvement:

1. Add NVIDIA GPU (8GB+ VRAM)
2. Install NVIDIA drivers and CUDA toolkit
3. Update .env with CUDA_DEVICE_ID
4. Restart OpenTranscribe

Performance gain: 50-70x faster transcription

Single GPU → Multi-GPU

For scaling throughput:

1. Add second/third GPU to system
2. Configure Multi-GPU Scaling
3. Update .env with GPU scaling settings
4. Restart with --gpu-scale flag

Performance gain: 4x throughput (with 4 parallel workers)

Local Storage → NAS

For enterprise deployments:

1. Set up NAS with 10GbE connection
2. Mount NAS storage on Docker host
3. Update UPLOAD_DIR and MODEL_CACHE_DIR in .env
4. Migrate existing data

Benefits: Centralized storage, easier backups, unlimited expansion

Next Steps

• Docker Compose Installation - Install OpenTranscribe
• GPU Setup - Configure NVIDIA GPU support
• Environment Variables - Optimize configuration
• Multi-GPU Scaling - Scale transcription throughput