# 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

### Windows (WSL-based) Specific
- **Minimum**: 16GB RAM
- **Recommended**: 32GB+ RAM
- **Memory Allocation**: 50-75% of system RAM dedicated to Kamiwaza during installation

## Storage Requirements

### Storage Performance
- **Required**: SSD (Solid State Drive)
- **Preferred**: NVMe SSD for optimal performance
- **Minimum**: SATA SSD

### 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. These estimates assume FP8 and include overhead for context windows and batch processing.

| Model Example | Parameters | Minimum vRAM | Notes |
| --- | --- | --- | --- |
| **GPT-OSS 20B** | 20B | 24GB | Includes weights + 1-batch max context; fits 1x 24GB GPU (e.g., L4/RTX 4090) |
| **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)
- **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)

### 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

### 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

**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)

**Shared Storage:**
- High-performance NAS or distributed filesystem (Lustre, CephFS)

## 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 1: Alternative** | `g5.2xlarge` | 8 | 32GB | 1x A10G (24GB) | 200GB gp3 |
| **Tier 2: Multi-GPU** | `g5.12xlarge` | 48 | 192GB | 4x A10G (96GB) | 2TB gp3 |
| **Tier 2: Alternative** | `p4d.24xlarge` | 96 | 1152GB | 8x A100 (320GB) | 2TB gp3 |
| **Tier 3: All Nodes** | `p4d.24xlarge` | 96 | 1152GB | 8x A100 (320GB) | 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 1: With GPU** | `n1-standard-8` \+ `1x T4` | 8 | 30GB | 1x T4 (16GB) | 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 1: With GPU** | `Standard_NC4as_T4_v3` | 4 | 28GB | 1x T4 (16GB) | 200GB Premium SSD |
| **Tier 2: H100 Multi-GPU** | `Standard_NC80adis_H100_v5` | 80 | 640GB | 2x H100 (160GB) | 2TB Premium SSD |

## Windows-Specific Prerequisites
- Windows Subsystem for Linux (WSL) installed and enabled
- Administrator access required for initial setup
- Windows Terminal (recommended for optimal WSL experience)

## 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

### Windows Components (Automated via MSI Installer)
- Windows Subsystem for Linux (WSL 2)
- Ubuntu 24.04 LTS (automatically downloaded and configured)
- Docker Engine (configured within WSL)
- GPU drivers and runtime (automatically detected and configured)
- Node.js 22 (via NVM within WSL environment)

## Network Configuration
### Network Bandwidth Requirements
#### Single Node Deployment
**Network Bandwidth:**
- **Minimum:** 1 Gbps (for model downloads, API traffic)
- **Recommended:** 10 Gbps (for high-throughput inference)

#### Multi-Node Cluster
**Inter-Node Network:**
- **Minimum:** 10 Gbps Ethernet
- **Recommended:** 25-40 Gbps Ethernet or InfiniBand

## Detailed Storage Requirements
### 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 |

## 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
- **Windows Edition**: Requires WSL 2 and will create a dedicated Ubuntu 24.04 instance

## Additional Considerations
### Network Ports
#### Linux/macOS Enterprise Edition
- 443/tcp: HTTPS primary access
- 51100-51199/tcp: Deployment ports for model instances