System Requirements | Kamiwaza Docs
Documentation for Kamiwaza 0.9.3
This version of Kamiwaza is no longer actively maintained. For the current GA release, see 1.0.1.
Base System Requirements
Supported Operating Systems & Architecture
- Linux:
- Ubuntu 24.04 and 22.04 LTS via .deb package installation (x64/amd64 architecture only)
- Redhat Enterprise Linux (RHEL) 9
- Windows: 11 (x64 architecture) via WSL with MSI installer
- macOS: 12.0 or later, Apple Silicon (ARM64) only (community edition only)
CPU Requirements
- Architecture:
- Linux: x64/amd64 (64-bit)
- macOS: ARM64 (Apple Silicon) only
- Windows: x64 (64-bit)
- Minimum Cores: 8+ cores
- Recommended Cores: 16+ cores for CPU-based inference workloads
Core Software Requirements
- Python: Python 3.10 for tarball installations; Python 3.12 for .deb/.msi installations
- Docker: Docker Engine with Compose v2
- Node.js: 22.x (installed via NVM during setup)
- Browser: Chrome Version 141+ (tested and recommended)
- GPU Support: NVIDIA GPU with compute capability 7.0+ (Linux only) or NVIDIA RTX/Intel Arc (Windows via WSL)
Memory Requirements
System RAM
- Minimum: 16GB RAM
- Recommended: 32GB+ RAM for CPU-based inference workloads
- GPU Workloads: 16GB+ system RAM (32GB+ recommended)
GPU Memory (vRAM)
- GPU Inference: 16GB+ vRAM required
- Recommended: 32GB+ vRAM for optimal GPU inference performance
Storage Requirements
Storage Performance
- Required: SSD (Solid State Drive)
- Preferred: NVMe SSD for optimal performance
- Minimum: SATA SSD
- Note: Models weights can be on a separate HDD but load time will increase
Storage Capacity
Linux/macOS
- Minimum: 100GB free disk space
- Recommended: 200GB+ free disk space Windows
- Minimum: 100GB free disk space
- Recommended: 200GB+ free space on SSD
Hardware Recommendation Tiers
Kamiwaza is a distributed AI platform built on Ray that supports both CPU-only and GPU-accelerated inference. Hardware requirements vary significantly based on:
- Model size: From 0.6B to 70B+ parameters
- Deployment scale: Single-node development vs multi-node production
- Inference engine: LlamaCpp (CPU/GPU), VLLM (GPU), MLX (Apple Silicon)
- Workload type: Interactive chat, batch processing, RAG pipelines
GPU Memory Requirements by Model Size
The table below provides real-world GPU memory requirement estimates for representative models at different scales.
| Model Example | Parameters | Minimum vRAM | Notes |
|---|---|---|---|
| GPT-OSS 20B | 20B | 24GB | Fits 1x 24GB GPU |
| 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) Hardware Specifications:
- CPU: 8-16 cores / 16-32 threads
- RAM: 32GB (16GB minimum)
- Storage: 200GB NVMe SSD (100GB minimum)
- GPU: Optional - Single GPU with 16-24GB VRAM
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
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): 16 cores / 32 threads, 64GB RAM, 500GB NVMe SSD
- Worker Nodes (3+ nodes for HA): 32-64 cores / 64-128 threads per node, 256-512GB RAM per node, 2TB NVMe SSD per node
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 |
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 1: With GPU | n1-standard-8 + 1x T4 |
8 | 30GB | 1x T4 (16GB) | 200GB 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 1: With GPU | Standard_NC4as_T4_v3 |
4 | 28GB | 1x T4 (16GB) | 200GB Premium SSD |
Dependencies & Components
Required System Packages
See platform-specific installation instructions
NVIDIA Components (Linux GPU Support)
- NVIDIA Driver (550-server recommended)
- NVIDIA Container Toolkit
- nvidia-docker2
Docker Configuration Requirements
- Docker Engine with Compose v2
- User must be in docker group
Network Configuration
Network Bandwidth Requirements
Single Node Deployment
- Minimum: 1 Gbps (for model downloads, API traffic)
- Recommended: 10 Gbps (for high-throughput inference)
Special Considerations
Apple Silicon (M-Series)
MLX Engine Support:
- Unified memory architecture (shared CPU/GPU RAM)
- Performance for models up to 13B parameters; reasonable performance for larger models when context is appropriately restricted and RAM is available.