System Requirements | Kamiwaza Docs
Hardware Requirements
Documentation for Kamiwaza 0.11.0.
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
- AMD Ryzen AI Max+ 395
- Apple Silicon M-series
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)
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
- Requires Windows Subsystem for Linux (WSL) installed and enabled
- Administrator access required for initial setup
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
Hardware Recommendation Tiers
GPU Memory Requirements by Model Size
| Model Example | Parameters | Minimum vRAM | Notes |
|---|---|---|---|
| GPT-OSS 20B | 20B | 24GB | Includes weights + 1-batch max context; fits 1x 24GB GPU |
| GPT-OSS 120B | 120B | 80GB | ~40GB weights + 1-batch max context; 1x H100/H200 or 2x A100 80GB recommended |
| Qwen 3 235B A22B | 235B | 150GB | ~120GB weights + 1-batch max context; 2x H200 (282GB) or 2x B200 (384GB) ideal for max context |
| Qwen 3-VL 235B A22B | 235B | 150GB | Same base minimum (includes 1-batch max context); 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)
Hardware Specifications:
- CPU: 8-16 cores / 16-32 threads
- RAM: 32GB (16GB minimum for lite mode only)
- Storage: 200GB NVMe SSD (100GB minimum)
- **GPU:**Optional - Single GPU with 16-24GB VRAM
- NVIDIA RTX 4090 (24GB)
- NVIDIA RTX 4080 (16GB)
- NVIDIA T4 (16GB)
- Network: 1-10 Gbps
Tier 2: Production - Medium to Large Models
Use Case: Production deployment of medium to large models (13B-70B parameters), high throughput
Hardware Specifications:
- CPU: 32 cores / 64 threads
- RAM: 128-256GB system RAM
- Storage: 1-2TB NVMe SSD
- **GPU:**1-4 GPUs with 40GB+ VRAM each
- 1-4x NVIDIA B200 (192GB HBM3e)
- 1-4x NVIDIA H200 (141GB HBM3e)
- 1-4x NVIDIA RTX 6000 Pro Blackwell (48GB)
- 1-2x NVIDIA H100 (80GB)
- 1-4x NVIDIA A100 (40GB or 80GB)
- 1-2x NVIDIA L40S (48GB)
- 2-4x NVIDIA A10G (24GB) for tensor parallelism
- Network: 25-40 Gbps
Tier 3: Enterprise Multi-Node Cluster
Use Case: Enterprise deployment with multiple models, high availability, horizontal scaling, 99.9%+ SLA
Cluster Architecture: Head Node (Control Plane):
- CPU: 16 cores / 32 threads
- RAM: 64GB
- Storage: 500GB NVMe SSD
- GPU: Same class as worker nodes (homogeneous cluster recommended)
Worker Nodes (3+ nodes for HA):
- CPU: 32-64 cores / 64-128 threads per node
- RAM: 256-512GB per node
- Storage: 2TB NVMe SSD per node (local cache)
- GPU: 4-8 GPUs per node (same class as head node)
- Network: 40-100 Gbps (InfiniBand for HPC workloads)
Cloud Provider Instance Mapping
AWS EC2 Instance Types
| Tier | Instance Type | vCPU | RAM | GPU | Storage |
|---|---|---|---|---|---|
| Tier 1: CPU-only | m6i.2xlarge |
8 | 32GB | None | 200GB gp3 |
| Tier 1: With GPU | g5.xlarge |
4 | 16GB | 1x A10G (24GB) | 200GB gp3 |
| Tier 2: Multi-GPU | g5.12xlarge |
48 | 192GB | 4x A10G (96GB) | 2TB gp3 |
Google Cloud Platform (GCP) Instance Types
| Tier | Machine Type | vCPU | RAM | GPU | Storage |
|---|---|---|---|---|---|
| Tier 1: CPU-only | n2-standard-8 |
8 | 32GB | None | 200GB SSD |
| Tier 2: Multi-GPU | a2-highgpu-4g |
48 | 340GB | 4x A100 (160GB) | 2TB SSD |
Microsoft Azure Instance Types
| Tier | VM Size | vCPU | RAM | GPU | Storage |
|---|---|---|---|---|---|
| Tier 1: CPU-only | Standard_D8s_v5 |
8 | 32GB | None | 200GB Premium SSD |
| Tier 2: A100 Multi-GPU | Standard_NC96ads_A100_v4 |
96 | 880GB | 4x A100 (320GB) | 2TB Premium SSD |
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
Network Ports
Linux/macOS Enterprise Edition
- 443/tcp: HTTPS primary access
Windows Edition
- 443/tcp: HTTPS primary access (via WSL)
Required Kernel Modules (Enterprise Edition Linux Only)
- overlay
- br_netfilter
System Network Parameters (Enterprise Edition Linux Only)
# Required sysctl settings for Swarm networking
net.bridge.bridge-nf-call-iptables = 1
net.bridge.bridge-nf-call-ip6tables = 1
net.ipv4.ip_forward = 1
Directory Structure
Enterprise Edition
/etc/kamiwaza/
├── config/
├── ssl/ # Cluster certificates
└── swarm/ # Swarm tokens
/opt/kamiwaza/
├── containers/ # Docker root (configurable)
├── logs/
└── runtime/ # Runtime files
Community Edition
$KAMIWAZA_ROOT/
├── env.sh
├── runtime/
└── logs/
Special Considerations
Apple Silicon (M-Series)
MLX Engine Support:
- Kamiwaza supports Apple Silicon via the MLX inference engine
- Unified memory architecture (shared CPU/GPU RAM)
NVIDIA DGX Spark
The NVIDIA DGX Spark is a compact AI workstation powered by the GB10 Grace Blackwell Superchip:
- CPU: 20-core ARM
AMD Ryzen AI Max+ 395 "Strix Halo"
AMD's Strix Halo platform provides powerful AI inference in a compact form factor:
- CPU: 16-core Zen 5 (up to 5.1 GHz)
Shared Storage (Multi-Node Clusters)
Network Filesystem Requirements:
- Protocol: NFSv4, Lustre, CephFS, or S3-compatible object storage
- Network Bandwidth: 10 Gbps minimum, 40+ Gbps for production
Storage Configuration by Edition
Enterprise Edition Requirements
- Primary mountpoint for persistent storage (/opt/kamiwaza)
- Shared storage for multi-node clusters.
Community Edition
- Local filesystem storage.
Version Compatibility
- Docker Engine: 24.0 or later with Compose 2.23+
- NVIDIA Driver: 450.80.02 or later