Heterogeneous Execution
Different CPU and GPU architectures can participate in compatible inference workloads.
Private Beta in Development
A trusted AI compute fabric
Make the compute
you already have
work together.
Kinode is building a trusted AI compute fabric that connects Macs, PCs and GPUs to run AI workloads across existing hardware.
Existing hardware. Explicit commitments. Coordinated execution.Explore the fabric
01 / The opportunity
AI compute is
fragmented.
Useful compute is already around us. But a Mac, a PC and a GPU workstation usually operate as separate islands.
For developers, startups and researchers, the next experiment can still mean expensive hardware or another cloud bill.
Personal devices
GPU workstations
Lab machines
02 / Capacity Unlock
Your computers are
more capable together.
Kinode is exploring how compatible devices can share one model when it fits on none of them alone, and when not to try.
| Workload Scenario | Single-Device Approach | Kinode Execution Possibility | Maturity / Evidence |
|---|---|---|---|
| Model fits locally | Single-Device ApproachRun locally. | Kinode Execution PossibilityPrefer local execution when distributing offers no useful advantage. | Maturity / EvidencePlan selection in qualification |
| Model exceeds individual memory limits | Single-Device ApproachCannot fit within its eligible memory budget. | Kinode Execution PossibilityEvaluate partitioning across compatible devices, subject to actual weights, KV cache, memory limits, hardware compatibility, network performance and workload requirements. | Maturity / EvidenceOversized-model physical proof pending |
| Multiple independent inference requests | Single-Device ApproachLimited to one node’s capacity. | Kinode Execution PossibilityPotential request distribution across eligible nodes. | Maturity / EvidenceProduction-scale qualification pending |
03 / Engineering thesis
Built for heterogeneous,
owner-controlled AI execution.
Resource Ownership
Explicit commitments, admission, leases and resource protection define participation.
Workload-Aware Orchestration
Choose execution strategies based on actual hardware, resource availability and workload constraints.
Our differentiation thesis is the combination of these capabilities.
See where Kinode fits04 / Evidence before promises
Built and tested
on real hardware.
Kinode has demonstrated segmented Qwen3-8B inference across physical Apple Silicon and NVIDIA CUDA devices.
Demonstrated
- Heterogeneous segmented inference on physical hardware.
- Resource-management and orchestration mechanisms with capability-specific testing.
In Progress
- Oversized-model capacity unlock.
- Performance optimization and reliability qualification.
- Beta user experience.
Start with what’s already here.
The next generation of AI infrastructure could start with the computers we already have.
We're building Kinode and preparing for our first controlled beta.
Talk to the Founder For early interest, technical conversations and collaboration.