EMBEDDED / 15 W CLASS
SPARK S20
A compact M.2 module for low-power local inference in robots, cameras and intelligent terminals.
HOLYCORESEDGE AI PROCESSOR
SPARK
Real-time AI in low-power edge devices.
SPARK MODULES
From embedded module, to edge server.
Three formats span sensor endpoints, compact hosts and multi-camera vision systems. Parameters below are product planning targets.
COMPACT / 35 W CLASS
SPARK S40
A low-profile PCIe module for embedded servers, industrial vision and multi-model pipelines.
RUGGED / 75 W CLASS
SPARK S80
An actively cooled, industrial-I/O edge card designed for sustained multi-camera and robotics-server workloads.
COMPARE SPARK MODELS
Choose for the site, not a label.
Interface, power, cooling and model scale define the right edge system together.| Planning dimension | SPARK S20 | SPARK S40 | SPARK S80 |
|---|---|---|---|
| Format | M.2 module | Low-profile PCIe card | Rugged PCIe card |
| Relative compute | 1× baseline | Approx. 2× S20 | Approx. 4× S20 |
| Target memory | 8 GB | 16 GB | 32 GB |
| Host interface | PCIe 4.0 ×4 | PCIe 4.0 ×8 | PCIe 5.0 ×8 |
| Target power | Up to 15 W | Up to 35 W | Up to 75 W |
| Cooling | Passive | Passive / active options | Active |
| Target deployment | Cameras, robots, terminals | Embedded hosts, vision | Multi-camera, edge servers |
Planning targets may change after silicon, board, interface, environment and reliability validation.
DESIGN PERSPECTIVE
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 / EDGE
Compute where data happens
Local inference for robotics, vision, and industrial equipment.
02 / ONE STACK
The same software foundation
Move models from NOVA validation to SPARK deployment.
READY TO BUILD?