System Requirements | Kamiwaza Docs
Documentation for Kamiwaza 0.12.0
Version: 0.12.0
Hardware Requirements
CPU
- Minimum Cores: 8+ cores
- Recommended Cores: 16+ cores for CPU-based inference workloads
- Architecture:
- Linux: x64/amd64 (64-bit)
- Windows: x64 (64-bit)
- macOS: ARM64 (Apple Silicon) only
Memory
System RAM
| Mode | Minimum | Recommended | Notes |
|---|---|---|---|
| Lite Mode | 16GB | 32GB | SQLite database; limited capacity for apps/tools |
| Full Mode | 32GB | 64GB+ | CockroachDB + DataHub; production workloads |
| GPU Workloads | 32GB | 64GB+ | System RAM alongside GPU vRAM |
GPU Memory (vRAM)
- GPU Inference: 16GB+ vRAM required
- Recommended: 32GB+ vRAM for optimal GPU inference performance
GPU (Optional)
Kamiwaza supports multiple GPU and accelerator platforms:
Discrete GPUs:
- NVIDIA GPUs with compute capability 7.0+ (Linux)
- NVIDIA RTX / Intel Arc (Windows via WSL)
Unified Memory Systems:
- NVIDIA DGX Spark - GB10 Grace Blackwell, 128GB unified memory
- AMD Ryzen AI Max+ 395 - "Strix Halo" platform, up to 128GB unified memory
- Apple Silicon M-series - Unified memory architecture (macOS only)
Storage
Storage requirements are the same across all platforms.
Storage Performance
- Required: SSD (Solid State Drive)
- Preferred: NVMe SSD for optimal performance
- Minimum: SATA SSD
- Note: Model weights can be on a separate HDD but load times will increase significantly
Storage Capacity
- Minimum: 100GB free disk space
- Recommended: 200GB+ free disk space
- Enterprise Edition: Additional space for /opt/kamiwaza persistence
Capacity Planning
| Component | Minimum | Recommended | Notes |
|---|---|---|---|
| Operating System | 20GB | 50GB | Ubuntu/RHEL base + dependencies |
| Kamiwaza Platform | 50GB | 50GB | Python environment, Ray, services |
| Model Storage | 50GB | 500GB+ | Depends on number and size of models |
| Database | 10GB | 50GB | CockroachDB for metadata |
| Vector Database | 10GB | 100GB+ | For embeddings (if enabled) |
| Logs & Metrics | 10GB | 50GB | Rotated logs, Ray dashboard data |
| Scratch Space | 20GB | 100GB | Temporary files, downloads, builds |
| Total | 170GB | 900GB+ |
Storage Performance Requirements
Local Storage (Single Node):
- Minimum: SATA SSD (500 MB/s sequential read)
- Recommended: NVMe SSD (2000+ MB/s sequential read)
Performance Targets:
- Sequential Read: 2000+ MB/s (model loading)
- Sequential Write: 1000+ MB/s (model downloads, checkpoints)
- 4K Random Read IOPS: 50,000+ (database, concurrent access)
- 4K Random Write IOPS: 20,000+ (database writes, logs)
Supported Operating Systems
Linux
- Ubuntu: 24.04 and 22.04 LTS via .deb package installation (x64/amd64 architecture only)
- Red Hat Enterprise Linux (RHEL): 9
Windows
- Windows 11 (x64 architecture) via WSL with MSI installer
macOS
- macOS 15.0 (Sequoia) or later, Apple Silicon (ARM64) only
Software Dependencies
Pre-requisites (User Must Install)
Before running the Kamiwaza installer, ensure the following are installed:
| Component | Requirement | Installation Guide |
|---|---|---|
| Docker | Docker Engine 24.0+ with Compose 2.23+ | Docker Install Guide |
| Browser | Chrome 141+ (tested and recommended) | Download Chrome |
Verifying System Requirements
Use these commands to verify your system meets the requirements before installation.
Docker
docker --version
Python
python3 --version
System Resources
free -h
Hardware Recommendation Tiers
Kamiwaza is a distributed AI platform built on Ray that supports both CPU-only and GPU-accelerated inference.
GPU Memory Requirements by Model Size
| Model Example | Parameters | Minimum vRAM | Notes |
|---|---|---|---|
| GPT-OSS 20B | 20B | 24GB | Fits 1x 24GB GPU (e.g., L4/RTX 4090) |
| GPT-OSS 120B | 120B | 80GB | 1x H100/H200 or 2x A100 80GB recommended |
| Qwen 3 235B A22B | 235B | 150GB | 2x H200 (282GB) or 2x B200 (384GB) ideal |
| Qwen 3-VL 235B A22B | 235B | 150GB | Budget +20-30% vRAM for high-res vision inputs |
Tier 1: Development & Small Models
Use Case: Local development, testing, small to medium model deployment (up to 13B parameters)
Tier 2: Production - Medium to Large Models
Use Case: Production deployment of medium to large models (13B-70B parameters), high throughput
Tier 3: Enterprise Multi-Node Cluster
Use Case: Enterprise deployment with multiple models, high availability, horizontal scaling, 99.9%+ SLA
Network Configuration
Network Bandwidth Requirements
Single Node Deployment
- Minimum: 1 Gbps (for model downloads, API traffic)
- Recommended: 10 Gbps (for high-throughput inference)
Multi-Node Cluster
- Minimum: 10 Gbps Ethernet
- Recommended: 25-40 Gbps Ethernet or InfiniBand
Required Kernel Modules (Enterprise Edition Linux Only)
- overlay
- br_netfilter
Community Edition Networking
- Uses standard Docker bridge networks
Directory Structure
Enterprise Edition
Note: This is created by the installer and present in cloud marketplace images.
/etc/kamiwaza/
├── config/
├── ssl/ # Cluster certificates
└── swarm/ # Swarm tokens
/opt/kamiwaza/
├── containers/ # Docker root (configurable)
├── logs/
├── nvm/ # Node Version Manager
└── runtime/ # Runtime files
Community Edition
We recommend ${HOME}/kamiwaza or something similar for KAMIWAZA_ROOT.
$KAMIWAZA_ROOT/
├── env.sh
├── runtime/
└── logs/
Important Notes
- System Impact: Network and kernel configurations can affect other services
- Security: Certificate generation and management for cluster communications
- Storage: Enterprise Edition requires specific storage configuration
- Network: Enterprise Edition requires specific network ports for cluster communication
- Docker: Custom Docker root configuration may affect other containers
Further documentation can be accessed for specific details on each aspect discussed above.