DESKTOP PERSONAL AI SYSTEM
AETHER A1
Bring local model prototyping, agents, tuning, and inference to the developer desktop, then move through HOLYFLOW to A8 nodes and A72 clusters.
HOLYCORES
CLOUD AI SYSTEM
AETHER
A scalable cloud AI system for production workloads.
AETHER SYSTEMS
From desktop, to full rack.
Accelerators, fabric, cooling, power and the management plane are designed as one system. The models below are planning targets subject to engineering validation.4U COMPUTE NODE
AETHER A8
A modular node for development, fine-tuning and enterprise inference, planned for up to eight NOVA accelerators with high-speed fabric and liquid-cooling readiness.
RACK-SCALE AI SYSTEM
AETHER A72
Compute nodes, switching, liquid distribution, telemetry and management integrated into a complete rack for scale training and production inference.
AETHER A1 / LOCAL AI
A complete AI development environment, on your desk.
A1 is planned for developers, researchers, and data scientists who need private data, local response, and always-available compute. Validate locally, then migrate through one software environment to datacenter and cloud systems.
COMPARE AETHER SYSTEMS
Choose the scale. Keep one foundation.
Planning parameters help teams define node, network, cooling and facility boundaries before validation.| Planning dimension | AETHER A1 | AETHER A8 | AETHER A72 |
|---|---|---|---|
| System format | Desktop personal AI system | 4U compute node | Full-rack integrated system |
| Target accelerator scale | Single integrated system | Up to 8 | Up to 72 |
| Fabric level | High-speed dual-system plan | In-node PCIe + HC-Fabric | Rack-wide non-blocking plan |
| Cooling | Quiet desktop air cooling | Air / liquid options | Rack liquid and thermal control |
| Management boundary | Personal / workgroup | Single node | Unified full-rack management |
| Target workloads | Prototyping, agents, local inference | Development, tuning, inference | Training, tuning, production |
Planning targets may change after card, network, power, cooling and facility validation.
One foundation. Three scales.
The portfolio is a continuous path from cloud systems to datacenter acceleration and edge inference—not a set of isolated devices.Unified compute model
Reuse model and operator work across deployment targets.
Composable systems
Co-design compute, fabric, cooling, and operations around real workloads.
Open evolution path
Start with validation, then expand to clusters and edge fleets.
FROM IDEA TO OPERATION
A platform is more than hardware.
Every engagement connects architecture, software, validation, and production operations.
01Characterize the workload
02Validate jointly
03Integrate the platform
04Scale and optimize
ENGAGEMENT MODEL
Validate deliberately. Scale with confidence.
01Characterize the workload
02Validate jointly
03Integrate the platform
04Scale and optimize
FAQ
Questions worth answering early.
Are all products generally available?+
These pages describe product direction. Availability, specifications, and schedules will be confirmed in formal product materials.
Why one architecture?+
It reduces repeated engineering when models move between deployment environments.
Can evaluation start small?+
The planned path starts with development and validation before scaling to production.
CHOOSE YOUR PERSPECTIVE
One platform. Different decisions.
Business leaders
Understand platform scope, deployment choices, and the long-term path.
Follow this path →System architects
Evaluate compute, fabric, software, operations, and facility constraints.
Follow this path →AI developers
Focus on model entry points, operators, tooling, and portability.
Follow this path →TECHNICAL EVALUATION
Ask the questions behind the specification.
Formal specifications and measured results will be published only after engineering validation.| Dimension | Question | Validation |
|---|---|---|
| Compute architecture | Adapt to different models and precision strategies | Build utilization and accuracy baselines with representative workloads |
| Memory hierarchy | Reduce avoidable movement and bandwidth stalls | Profile working sets and end-to-end bottlenecks |
| Scale-out fabric | Grow from device to node and cluster | Validate topology, communication, and scaling efficiency |
| Software stack | Map framework models reliably to hardware | Check coverage, correctness, and tunability |
| System engineering | Balance power, cooling, density, and serviceability | Validate against the target facility and workload |
| Operations | Deploy, observe, upgrade, and diagnose | Establish telemetry, health, version, and rollback workflows |
WORKLOAD MAP
Design around the work—not the label.
Training and fine-tuning
Throughput, communication, precision, and long-run stability.
Online and batch inference
Response time, generation rate, concurrency, and utilization.
Enterprise and sovereign AI
Data boundaries, operational control, auditability, and lifecycle.
Robotics and industrial vision
Local latency, power, interfaces, and environment.
ADOPTION PATH
Move from evidence to operations.
Development validation
Create a baseline with representative models and evaluation systems.
Private infrastructure
Integrate with customer security, datacenter, and operational practices.
Scale deployment
Expand clusters or edge fleets against capacity and service goals.
PRODUCTION READINESS
Performance is only one part of a system.
Reliability
Health, fault isolation, recovery, and long-run validation.
Security
Boot, identity, access, update, and data-boundary planning.
Observability
Telemetry that connects model behavior to system state.
Lifecycle
Versioning, compatibility, rollback, maintenance, and support.
RESOURCES & STATUS
Clarity before claims.
Platform overview
Product direction, architecture principles, workloads, and evaluation framework on this site.
Evaluation checklist
Workload definition, environment requirements, success metrics, and test planning.
Technical materials
Specifications, compatibility, SDKs, docs, release notes, and validated model status will follow maturity.
01 / SYSTEM
Co-designed from node to cluster
Compute, fabric, cooling, and system software operate as one platform.
02 / WORKLOADS
Training, tuning, and inference
One environment supports the full model lifecycle.
READY TO BUILD?