Turn every watt
into trusted intelligence

A Trusted Foundation starts at the physical layer: data stays on the device, inference runs locally, and the enterprise owns its computing resources. That is why LoLR.AI develops its own on-device hardware. With limited power and space, compute must be used efficiently. Alongside general-purpose computing, FPGA—field-programmable gate arrays, hardware customizable for specific tasks—and dedicated accelerators distribute workloads by task type. The LoLR distributed compute network then organizes devices, edge, and cloud into a schedulable, auditable whole.

  • CPULogic and scheduling: complex decisions, system scheduling, and small unstructured tasks.
  • GPUParallel inference: large-model inference and parallel matrix operations provide core AI computing power.
  • FPGA · Dedicated accelerationPipeline acceleration for frequent tasks such as memory management, OCR, and encryption significantly reduces power consumption.
  • Coordinated schedulingDistribute workloads by task type; every device runs the same LoLR.AI OS and Skills system.
Explore the full LoLR HPCs lineup→
CPU
Logic & SchedulingRule evaluation · System scheduling · Small unstructured tasks
GPU
Parallel inference accelerationLarge-model inference · Parallel matrix operations · Core AI computing
FPGA
Dedicated hardware accelerationFPGA and dedicated accelerators · Memory management · OCR · Encryption
General-purpose computing for frequent tasks
1×
Dedicated hardware acceleration
10×

Energy efficiency comparison for frequent tasks including memory management, OCR, and encryption, measured under laboratory conditions. Overall system performance depends on the models and operating environment.

Organizing compute across devices and cloud

A single device has limited compute, while enterprise tasks span individuals, departments, and institutions. The LoLR distributed compute network brings portable and desktop devices, internal servers, and authorized cloud resources into one network, with heterogeneous processing within nodes, unified scheduling across the network, and secure links controlling transfers between nodes.

Unified Scheduling · LoLR.AI OS Choose where each task runs by data sensitivity, latency, model size, and cost Device Portable & Personal devices LoLR ticTALK · mini · BOX Process frequent tasks close to the source OCR · Transcription · Memory retrieval · Everyday Q&A CPU · GPU · FPGA · Dedicated acceleration Data stays on device · Millisecond response · Low power Edge Department & Institution networks LoLR STUDIO · STATION LoLR STATION Project-based solutions · Rack-scale Large-model inference & Multi-user access Compliance & Risk · Department Digital Experts · Full-data analysis CPU · GPU · FPGA · Dedicated acceleration · Multi-device orchestration All data stays in the domain · High concurrency · Domestic Chinese IT Cloud Authorized cloud resources LoLR Cloud · Authorized public compute access LoLR Cloud Elastic compute & Model updates Large-scale analysis · Cross-domain inference · Model & Skills updates Elastic GPU compute · Invoked under authorization and data policy Authorized tasks only · Every invocation logged Secure links: device authentication · End-to-end encryption · Access control · Full auditing Raw data stays at its originating node; tasks, results, and authorized model capabilities flow between nodes
(Swipe horizontally to view the full diagram)
Unified scheduling selects the device, edge, or cloud layer for each task; secure links control transfers between nodesLoLR Distributed Compute Network · Illustration
Heterogeneous compute nodes

Within each node, CPUs handle logic and scheduling, GPUs handle parallel inference, and FPGA and dedicated accelerators handle frequent tasks such as memory management, OCR, and encryption.

Distributed scheduling

Select device, edge, or cloud execution by data sensitivity, latency, model size, and cost; orchestrate multiple organizational devices according to concurrent demand.

Secure links

Device authentication, end-to-end encryption, access control, and complete logs. Raw data stays at its originating node; tasks and conclusions move between nodes.

Energy-efficient distributed computing

Frequent tasks run on nearby dedicated device hardware, large-model inference runs on department and institution nodes, and authorized cloud resources meet elastic demand. Compute is allocated for energy efficiency.

Network nodes: from portable devices to enterprise deployments

Compute scales across orders of magnitude, whileLoLR.AI OS and Skills stay consistent, allowing capabilities to move between nodes.

LoLR ticTALK, LoLR STUDIO, and LoLR BOX in use
LoLR ticTALK · STUDIO · BOX
LoLR ticTALKPortableLoLR ticTALKField knowledge managementProfessional recording and transcription for meetings, interviews, and field visits. LoLR miniPersonalLoLR miniProfessional knowledge management · Data sovereigntyA compact, always-available private AI space. LoLR BOXStudioLoLR BOXAutonomous agent development · Local modelsComplete professional knowledge bases and stable workflows. LoLR STUDIODepartmentLoLR STUDIOSuper Digital Experts · High concurrencyHeavy local workloads and shared team access, powered by heterogeneous dual engines. LoLR STATIONProject-based solution · Coming soonEnterprisesLoLR STATIONReal-time inference across all data · Domestic Chinese ITAn organization-wide solution for cross-department sharing, concurrency, and governance.
Trusted Foundation=Energy-efficient heterogeneous nodes+Distributed compute network+Secure linksThis enables data, compute, and business sovereignty: enterprises retain their data, control their compute, and define their business.

Within your enterprise's own boundaries
Deploy this architecture

Tell us your task types, data volume, and data boundaries. Our FDE team starts with an on-site assessment to define your device, model, and Harness solution together.

Explore industry solutions
LoLR HPCs · Trusted FoundationA complete on-device computing lineup from portable devices to organization-wide servers. LoLR Large Model Logos LoLR · Sovereign ModelsDomain expertise, foundation-model adaptation, device–cloud collaboration, and distributed scheduling. LoLR Operating System LoLR.AI OS · Enterprise HarnessAgent factory and Harness delivery tools: create, train, manage, and use experts. LoLR Mobile LoLR.AI APPMobile work, agent deployment, and on-the-go access bring the system into the field. Industry solutionsExplore scenarios, solution components, and devices by industry and role. Service & SupportSetup guides, FAQs, after-sales terms, and manual downloads.