Hardware Command

The hardware command provides universal GPU detection, automatic driver installation, and intelligent hardware optimization for maximum transcoding performance across NVIDIA RTX, AMD VAAPI, Intel QuickSync, and Raspberry Pi VideoCore.

Usage

bash

usenet hardware <action> [options]

Actions

Action	Description	Example
`list`	Show hardware capabilities and recommendations	`usenet hardware list`
`optimize`	Generate hardware-optimized configurations	`usenet hardware optimize --auto`
`install-drivers`	Auto-install optimal GPU drivers	`usenet hardware install-drivers`
`benchmark`	Test transcoding performance	`usenet hardware benchmark`
`profile`	Manage performance profiles	`usenet hardware profile balanced`

Hardware Detection

Show Capabilities

bash

usenet hardware list

Example output for NVIDIA RTX system:

bash

🚀 HARDWARE CAPABILITIES DETECTED

💎 GPU: NVIDIA GeForce RTX 4090
   • NVENC/NVDEC: ✓ Available (AV1, HEVC, H.264)
   • CUDA Cores: 16,384
   • VRAM: 24GB GDDR6X
   • Driver: 545.29.06 ✓ Optimal
   • Docker: nvidia-docker2 ✓ Installed

🧠 CPU: Intel Core i9-13900K
   • Cores: 24 (8P + 16E)
   • Threads: 32
   • Quick Sync: ✓ Available (AV1, HEVC, H.264)
   • AVX-512: ✓ Supported

💾 Memory: 64GB DDR5-5600
   • Available: 48GB (75%)
   • Suitable for: Dedicated performance profile

🚀 PERFORMANCE OPTIMIZATION OPPORTUNITIES:
   ✅ 4K HEVC transcoding: 60+ FPS (vs 2-5 FPS CPU-only)
   ✅ Simultaneous streams: 8+ concurrent 4K transcodes
   ✅ AV1 encoding: 50% smaller files than H.264
   ✅ HDR tone mapping: Real-time HDR10 to SDR conversion
   ✅ Power efficiency: 50W GPU vs 200W CPU transcoding

💡 RECOMMENDATIONS:
   • Use 'dedicated' performance profile for maximum throughput
   • Enable hardware transcoding in Jellyfin and Tdarr
   • Consider AV1 encoding for storage efficiency

Example output for AMD system:

bash

🚀 HARDWARE CAPABILITIES DETECTED

🔥 GPU: AMD Radeon RX 7900 XTX
   • VAAPI/AMF: ✓ Available (HEVC, H.264)
   • Compute Units: 96
   • VRAM: 24GB GDDR6
   • Driver: AMDGPU 23.20 ✓ Current
   • VA-API: libva ✓ Configured

🧠 CPU: AMD Ryzen 9 7950X
   • Cores: 16
   • Threads: 32
   • Architecture: Zen 4
   • Integrated Graphics: None

💾 Memory: 32GB DDR5-5200
   • Available: 24GB (75%)
   • Suitable for: High performance profile

⚡ PERFORMANCE OPTIMIZATION OPPORTUNITIES:
   ✅ Hardware HEVC encoding: 10x faster than CPU
   ✅ VAAPI acceleration: Energy-efficient transcoding  
   ✅ Dual-stream processing: Encode while serving
   ✅ HDR10 passthrough: Preserve HDR metadata

💡 RECOMMENDATIONS:
   • Install mesa-va-drivers for optimal VAAPI support
   • Use 'high' performance profile for best balance
   • Enable AMF encoding in supported applications

Driver Installation

Automatic Driver Installation

bash

usenet hardware install-drivers

NVIDIA systems:

bash

🔧 Installing NVIDIA drivers...

📦 Detecting system:
   • OS: Ubuntu 22.04 LTS
   • Kernel: 6.2.0-39-generic
   • GPU: RTX 4090

🚀 Installing optimal drivers:
   ✓ nvidia-driver-545 (latest stable)
   ✓ nvidia-docker2 (container support)
   ✓ nvidia-cuda-toolkit (development tools)

⚙️ Configuring Docker integration:
   ✓ /etc/docker/daemon.json updated
   ✓ Docker service restarted
   ✓ nvidia-docker runtime available

🧪 Testing installation:
   ✓ nvidia-smi responds correctly
   ✓ NVENC/NVDEC capabilities detected
   ✓ Docker GPU access confirmed

✅ Installation complete! Reboot recommended.

AMD systems:

bash

🔧 Installing AMD drivers...

📦 Detecting system:
   • OS: Ubuntu 22.04 LTS  
   • GPU: RX 7900 XTX

🚀 Installing optimal drivers:
   ✓ mesa-va-drivers (VAAPI support)
   ✓ libva-dev (development headers)
   ✓ vainfo (testing utility)

⚙️ Configuring VA-API:
   ✓ /etc/environment updated
   ✓ User added to video group
   ✓ Device permissions configured

🧪 Testing installation:
   ✓ vainfo shows available profiles
   ✓ Hardware acceleration verified
   ✓ VAAPI encoding confirmed

✅ Installation complete!

Manual Driver Options

bash

# Install specific driver version
usenet hardware install-drivers --version 545.29.06

# Install with development tools
usenet hardware install-drivers --include-dev

# Test installation without installing
usenet hardware install-drivers --dry-run

Performance Optimization

Generate Optimized Configurations

bash

usenet hardware optimize --auto

Generated optimizations:

docker-compose.optimized.yml - Hardware-tuned service configurations
jellyfin.xml - Transcoding settings for detected GPU
tdarr_configs.json - Optimized transcoding profiles
ffmpeg_profiles/ - Hardware-specific encoding presets

Performance Profiles

bash

# Set specific performance profile
usenet hardware profile dedicated

# List available profiles  
usenet hardware profile list

# Show current profile
usenet hardware profile

Profile	CPU Usage	RAM Allocation	GPU Usage	Use Case
`light`	25%	4GB	Optional	Development, testing
`balanced`	50%	8GB	Yes	Home server (default)
`high`	75%	16GB	Yes	Dedicated media server
`dedicated`	100%	All available	Yes	Media center appliance

Custom Optimization

bash

# Optimize for specific services
usenet hardware optimize --services jellyfin,tdarr

# Optimize for specific codec
usenet hardware optimize --codec hevc

# Generate profiles for all codecs
usenet hardware optimize --all-codecs

Performance Benchmarking

Transcoding Benchmarks

bash

usenet hardware benchmark

Example benchmark results:

bash

🧪 TRANSCODING PERFORMANCE BENCHMARK

Test Configuration:
• Source: 4K HEVC 10-bit HDR (50 Mbps)
• Target: 1080p H.264 8-bit SDR (8 Mbps)
• Duration: 60 seconds

📊 Results:

CPU-Only Transcoding:
⏱️  Time: 180 seconds (0.33x realtime)
⚡ Power: ~200W
🌡️  Temperature: 85°C
📈 CPU Usage: 100%

GPU-Accelerated (NVENC):
⏱️  Time: 30 seconds (2.0x realtime) 
⚡ Power: ~50W total
🌡️  Temperature: 65°C
📈 GPU Usage: 45%

Improvement:
🚀 Speed: 6x faster
💡 Power: 75% reduction  
❄️  Temperature: 20°C cooler

Custom Benchmark Tests

bash

# Test specific codec
usenet hardware benchmark --codec hevc

# Test multiple concurrent streams
usenet hardware benchmark --streams 4

# Test different resolutions
usenet hardware benchmark --resolution 1080p,4k

Hardware Configurations

Generated Docker Compose

docker-compose.optimized.yml (NVIDIA example):

yaml

services:
  jellyfin:
    deploy:
      resources:
        limits:
          memory: 8G
        reservations:
          devices:
            - driver: nvidia
              count: 1
              capabilities: [gpu]
    environment:
      - NVIDIA_VISIBLE_DEVICES=all
      - NVIDIA_DRIVER_CAPABILITIES=compute,video,utility
    volumes:
      - ./config/jellyfin_hw.xml:/config/encoding.xml:ro

  tdarr:
    deploy:
      resources:
        limits:
          memory: 16G
        reservations:
          devices:
            - driver: nvidia
              count: 1
              capabilities: [gpu]
    environment:
      - NVIDIA_VISIBLE_DEVICES=all
      - serverIP=0.0.0.0
      - serverPort=8266
      - webUIPort=8265
      - internalNode=true
      - nodeID=MainNode

Jellyfin Hardware Transcoding

Generated config/jellyfin_hw.xml:

xml

<EncodingOptions>
  <HardwareAccelerationType>nvenc</HardwareAccelerationType>
  <EnableHardwareDecoding>true</EnableHardwareDecoding>
  <EnableHardwareEncoding>true</EnableHardwareEncoding>
  <EnableToneMappingDecodingWithNvenc>true</EnableToneMappingDecodingWithNvenc>
  <AllowHardwareSubtitleExtraction>true</AllowHardwareSubtitleExtraction>
  <H264Crf>23</H264Crf>
  <H265Crf>28</H265Crf>
  <DeinterlaceMethod>yadif</DeinterlaceMethod>
  <EnableDecodingColorDepth10Hevc>true</EnableDecodingColorDepth10Hevc>
  <EnableEnhancedNvdecDecoder>true</EnableEnhancedNvdecDecoder>
</EncodingOptions>

Multi-GPU Support

GPU Selection

bash

# List available GPUs
usenet hardware list --gpus

# Optimize for specific GPU
usenet hardware optimize --gpu 0

# Use multiple GPUs
usenet hardware optimize --gpu all

Multi-GPU allocation:

bash

🔧 Multiple GPUs detected:

GPU 0: NVIDIA RTX 4090 (24GB) - Primary transcoding
GPU 1: NVIDIA RTX 3080 (10GB) - Secondary transcoding  

Service allocation:
• Jellyfin: GPU 0 (primary streaming)
• Tdarr: GPU 1 (background processing)
• Handbrake: GPU 0 (manual encoding)

Advanced Features

Custom FFmpeg Profiles

bash

# Generate custom encoding profiles
usenet hardware generate-profiles --quality high --efficiency balanced

# Create profile for specific use case
usenet hardware generate-profiles --profile "4k-archive" \
  --input-format "hevc" \
  --output-format "av1" \
  --crf 28 \
  --preset slow

Hardware Monitoring

bash

# Real-time hardware monitoring during transcoding
usenet hardware monitor

# Monitor specific metrics
usenet hardware monitor --metrics gpu-util,temp,power

# Log monitoring data
usenet hardware monitor --log /var/log/hardware-monitor.log

Power Management

bash

# Set GPU power limits (NVIDIA)
usenet hardware power-limit --gpu 0 --watts 300

# Enable GPU boost
usenet hardware boost enable

# Set performance mode
usenet hardware performance-mode max

Examples

NVIDIA RTX SetupAMD RDNA SetupIntel QuickSync SetupRaspberry Pi Setup

bash

# Detect hardware capabilities
usenet hardware list

# Install optimal drivers
usenet hardware install-drivers

# Generate optimized configs
usenet hardware optimize --auto

# Set high performance profile
usenet hardware profile high

# Benchmark performance
usenet hardware benchmark

bash

# Check VAAPI support
usenet hardware list

# Install VAAPI drivers
usenet hardware install-drivers

# Optimize for AMD hardware
usenet hardware optimize --amd

# Test hardware acceleration
usenet hardware benchmark --codec hevc

bash

# Detect QuickSync capabilities
usenet hardware list --intel

# Install Intel media drivers
usenet hardware install-drivers --intel

# Optimize for QuickSync
usenet hardware optimize --quicksync

# Set balanced profile (recommended for Intel)
usenet hardware profile balanced

bash

# Detect VideoCore capabilities
usenet hardware list

# Enable GPU memory split
usenet hardware configure --pi --gpu-mem 128

# Optimize for low power
usenet hardware profile light

# Test hardware decode
usenet hardware benchmark --pi-test

Troubleshooting

Common Issues

GPU not detected:

bash

# Check driver installation
nvidia-smi  # NVIDIA
vainfo      # AMD
intel_gpu_top  # Intel

# Reinstall drivers
usenet hardware install-drivers --force

Docker GPU access denied:

bash

# Check nvidia-docker installation
docker run --rm --gpus all nvidia/cuda:11.0-base nvidia-smi

# Fix permissions
sudo usermod -aG docker $USER
sudo systemctl restart docker

Poor transcoding performance:

bash

# Check hardware utilization
usenet hardware monitor

# Verify optimal profile
usenet hardware profile dedicated

# Check thermal throttling
usenet hardware temperature

Hardware Logs

bash

# View hardware optimization logs
usenet logs hardware

# Debug driver installation
usenet hardware install-drivers --verbose

# Monitor real-time GPU usage
usenet hardware monitor --live

Performance Analysis

bash

# Analyze transcoding bottlenecks
usenet hardware analyze

# Compare profiles
usenet hardware benchmark --compare-profiles

# Export performance report
usenet hardware report --export performance.json

Cross-Platform Support

Platform-Specific Optimizations

Ubuntu/Debian:

bash

# APT-based driver installation
usenet hardware install-drivers --apt

# Enable restricted repositories
usenet hardware enable-repos --non-free

CentOS/RHEL:

bash

# YUM/DNF-based installation  
usenet hardware install-drivers --yum

# Enable EPEL repository
usenet hardware enable-repos --epel

Arch Linux:

bash

# Pacman-based installation
usenet hardware install-drivers --pacman

# Enable AUR packages
usenet hardware enable-repos --aur

Container Runtimes

bash

# Configure for Podman
usenet hardware configure --runtime podman

# Configure for containerd
usenet hardware configure --runtime containerd

# Configure for Docker with BuildKit
usenet hardware configure --buildkit

deploy - Include hardware optimization in deployment
storage - Optimize storage layout for transcoding
services - Configure service-specific hardware settings
validate - Hardware health checks

Hardware Command ​

Usage ​

Actions ​

Hardware Detection ​

Show Capabilities ​

Driver Installation ​

Automatic Driver Installation ​

Manual Driver Options ​

Performance Optimization ​

Generate Optimized Configurations ​

Performance Profiles ​

Custom Optimization ​

Performance Benchmarking ​

Transcoding Benchmarks ​

Custom Benchmark Tests ​

Hardware Configurations ​

Generated Docker Compose ​

Jellyfin Hardware Transcoding ​

Multi-GPU Support ​

GPU Selection ​

Advanced Features ​

Custom FFmpeg Profiles ​

Hardware Monitoring ​

Power Management ​

Examples ​

Troubleshooting ​

Common Issues ​

Hardware Logs ​

Performance Analysis ​

Cross-Platform Support ​

Platform-Specific Optimizations ​

Container Runtimes ​

Related Commands ​

Hardware Command

Usage

Actions

Hardware Detection

Show Capabilities

Driver Installation

Automatic Driver Installation

Manual Driver Options

Performance Optimization

Generate Optimized Configurations

Performance Profiles

Custom Optimization

Performance Benchmarking

Transcoding Benchmarks

Custom Benchmark Tests

Hardware Configurations

Generated Docker Compose

Jellyfin Hardware Transcoding

Multi-GPU Support

GPU Selection

Advanced Features

Custom FFmpeg Profiles

Hardware Monitoring

Power Management

Examples

Troubleshooting

Common Issues

Hardware Logs

Performance Analysis

Cross-Platform Support

Platform-Specific Optimizations

Container Runtimes

Related Commands