# 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
- **Note**: Models weights can be on a separate HDD but loads time will increase

### Storage Capacity
**Linux/macOS**
- **Minimum**: 100GB free disk space
- **Recommended**: 200GB+ free disk space
- **Enterprise Edition**: Additional space for /opt/kamiwaza persistence

**Windows**
- **Minimum**: 100GB free disk space
- **Recommended**: 200GB+ free space on SSD
- **WSL**: Automatically manages Ubuntu 24.04 installation space

## 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 |

**Key Considerations:**
- **Minimum vRAM**: FP8 weights + 1-batch allocation at your target max context
- **Headroom**: For longer contexts, larger batch sizes, and concurrency, budget additional vRAM beyond minimums
- **Vision Workloads**: Image/video processing adds overhead; budget 20-30% more for vision-language models
- **Tensor Parallelism**: Distributing large models (120B+) across multiple GPUs requires high-bandwidth interconnects (NVLink 3.0+)

### 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)
- **Network:** 1-10 Gbps

**Workload Capacity:**
- Low-volume workloads: 1-10 concurrent requests (supports dozens of interactive users)
- Development, testing, and proof-of-concept deployments
- Light production workloads

### 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 (various NVIDIA models)
- **Network:** 25-40 Gbps

**Workload Capacity:**
- Medium-scale production: 100s to 1,000+ concurrent requests (supports thousands of interactive users)
- Example: Per-GPU batch size of 32 across 8 GPUs = 256 concurrent requests; batch size of 128 = 1,024 requests
- Production chat applications
- Complex RAG pipelines with embedding generation
- Batch inference

### 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)
- **Role:** Ray head, API gateway, scheduling, monitoring (head performs minimal extra work; Ray backend load is distributed across nodes)

**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 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 1: Alternative** | `g2-standard-8` \+ `1x L4` | 8 | 32GB | 1x L4 (24GB) | 200GB SSD |
| **Tier 2: Multi-GPU** | `a2-highgpu-4g` | 48 | 340GB | 4x A100 (160GB) | 2TB SSD |
| **Tier 2: Alternative** | `g2-standard-48` \+ `4x L4` | 48 | 192GB | 4x L4 (96GB) | 2TB SSD |
| **Tier 3: All Nodes** | `a2-highgpu-8g` | 96 | 680GB | 8x A100 (320GB) | 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 1: Alternative** | `Standard_NC6s_v3` | 6 | 112GB | 1x V100 (16GB) | 200GB Premium SSD |
| **Tier 2: H100 (recommended)** | `Standard_NC40ads_H100_v5` | 40 | 320GB | 1x H100 (80GB) | 2TB Premium SSD |
| **Tier 2: H100 Multi-GPU** | `Standard_NC80adis_H100_v5` | 80 | 640GB | 2x H100 (160GB) | 2TB Premium SSD |
| **Tier 2: A100 Multi-GPU** | `Standard_NC96ads_A100_v4` | 96 | 880GB | 4x A100 (320GB) | 2TB Premium SSD |
| **Tier 2: A100 Alternative** | `Standard_NC48ads_A100_v4` | 48 | 440GB | 2x A100 (160GB) | 2TB Premium SSD |
| **Tier 3: H100 (recommended)** | `Standard_ND96isr_H100_v5` | 96 | 1900GB | 8x H100 (640GB) | 2TB Premium SSD |
| **Tier 3: A100 Alternative** | `Standard_ND96asr_v4` | 96 | 900GB | 8x A100 (320GB) | 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)
### Docker Configuration Requirements
- Docker Engine with Compose v2
- User must be in docker group
- Swarm mode (Enterprise Edition)
- Docker data root configuration (configurable)
### Required Directory Structure
#### Enterprise Edition
Note this is created by the installer and present in cloud marketplace images.
```text
/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`.
```text
$KAMIWAZA_ROOT/

├── env.sh

├── runtime/

└── logs/
```

## 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)

**Considerations:**
- Internet bandwidth for downloading models from HuggingFace (one-time)
- Client API traffic for inference requests/responses
- Monitoring and logging egress

#### Multi-Node Cluster
**Inter-Node Network:**
- **Minimum:** 10 Gbps Ethernet
- **Recommended:** 25-40 Gbps Ethernet or InfiniBand
- **Latency:** < 1ms between nodes (same datacenter/availability zone)

**Why It Matters:**
- Ray distributed scheduling requires low-latency communication
- Tensor parallelism transfers large model shards between GPUs
- Shared storage access impacts model loading performance

### Required Kernel Modules (Enterprise Edition Linux Only)
Required modules for Swarm container networking:
- overlay
- br_netfilter
### System Network Parameters (Enterprise Edition Linux Only)
These will be set by the installer.
```bash
# 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
```
### Community Edition Networking
- Uses standard Docker bridge networks
- No special kernel modules or sysctl settings required
- Simplified single-node networking configuration

## 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 |
| **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)
**Storage Type:**
- **Minimum:** SATA SSD (500 MB/s sequential read)
- **Recommended:** NVMe SSD (2000+ MB/s sequential read)
- **Note:** HDD: Only recommended for non-dynamic model loads and low KV cache usage - model load times can be very long (15+ minutes); models are in memory after load

**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)

**Why It Matters:**
- 7B model (14GB): Loads in ~7 seconds on NVMe vs ~28 seconds on SATA SSD
- Concurrent model loads across Ray workers stress random read performance
- Database query performance directly tied to IOPS

#### 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
- **Network Latency:** < 5ms between nodes and storage
- **Sequential Throughput:** 5+ GB/s aggregate (10+ GB/s for large clusters)

**Object Storage (Alternative):**
- S3-compatible API (AWS S3, GCS, MinIO, etc.)
- Local caching layer recommended for frequently accessed models
- Consider bandwidth costs for cloud object storage

**Shared Storage Options:**
| Solution | Use Case | Throughput | Cost Profile |
| --- | --- | --- | --- |
| **NFS over NVMe** | Small clusters (< 5 nodes) | 1-5 GB/s | Low (commodity hardware) |
| **AWS FSx for Lustre** | AWS multi-node clusters | 1-10 GB/s | Medium (pay per GB/month + throughput) |
| **GCP Filestore High Scale** | GCP multi-node clusters | Up to 10 GB/s | Medium-High |
| **Azure NetApp Files Ultra** | Azure multi-node clusters | Up to 10 GB/s | High |
| **CephFS** | On-premises clusters | 5-20 GB/s | Medium (requires Ceph cluster) |
| **Object Storage + Cache** | Cost-optimized | Varies | Low storage, high egress |

### Storage Configuration by Edition
#### Enterprise Edition Requirements
- Primary mountpoint for persistent storage (/opt/kamiwaza)
- Scratch/temporary storage (auto-configured)
- For Azure: Additional managed disk for persistence
- Shared storage for multi-node clusters (see Shared Storage Options above)
#### Community Edition
- Local filesystem storage
- Configurable paths via environment variables
- Single-node storage only (no shared storage required)

## 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)
- Excellent performance for models up to 13B parameters; reasonable performance for larger models when context is appropriately restricted and RAM is available.
- All M-series chips work in approximately the same way, but newer chips (e.g., M4) offer substantially higher performance than older versions
- Ultra chips (Mac Studio/Mac Pro models) typically offer 50-80% more performance than Pro versions

**Notes:**
- No tensor parallelism support (single chip only)
- Not for production use; like-for-like API, UI, capabilities.
- Community edition only; single node only (Enterprise edition not available on macOS)

## Important Notes
- **System Impact**: Network and kernel configurations can affect other services
- **Security**: Certificate generation and management for cluster communications
- **GPU Support**: Available on Linux (NVIDIA GPUs) and Windows (NVIDIA RTX, Intel Arc via WSL)
- **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
- **Windows Edition**: Requires WSL 2 and will create a dedicated Ubuntu 24.04 instance
- **Administrator Access**: Windows installation requires administrator privileges for initial setup

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

#### Windows Edition
- 443/tcp: HTTPS primary access (via WSL)
- 61100-61299/tcp: Reserved ports for Windows installation

### Version Compatibility
- Docker Engine: 20.10 or later
- NVIDIA Driver: 450.80.02 or later
- ETCD: 3.5 or later
- Node.js: 22.x (installed automatically)