LocalMind Specification

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LocalMind

Where Open Hardware Meets Open Source AI

LocalMind is a modular, locally hosted AI platform specification designed to deliver high-performance artificial intelligence across a wide range of machines and computing environments. The platform is designed to operate independently of mandatory SaaS AI providers while supporting continuous upgrades to models, inference systems, knowledge, agents, software, and hardware.

LocalMind treats AI as an evolving system rather than a fixed model. Its architecture separates intelligence, inference, knowledge, memory, hardware, optimization, agents, and tools into interoperable modules that can be independently upgraded, replaced, tested, and optimized. The platform is designed to maximize the capabilities of the hardware available to it while remaining adaptable to future computing architectures.

The specification is hardware-agnostic, model-agnostic, runtime-agnostic, and designed for extensibility. LocalMind may operate on laptops, desktops, workstations, servers, edge devices, local clusters, custom computing systems, and open hardware platforms.

Core Design Principles

Local-first AI operation
Open source development
Open hardware compatibility
Modular architecture
Independent component upgrades
Hardware independence
Model independence
Runtime independence
Continuous optimization
Continuous knowledge improvement
Task-oriented intelligence
Human-controlled deployment
Privacy-preserving local operation
Interoperability
Capability discovery
Versioned interfaces
Performance-driven development
Reversible upgrades
Long-term maintainability
Future-compatible architecture
Vendor-neutral design

Core Platform Module

The Core Platform Module provides the foundational services required by all LocalMind components.

Features

Modular component management
Module lifecycle management
Version management
Capability discovery
Dependency management
Configuration management
Interface management
Compatibility validation
Component health monitoring
Service coordination
Event management
Local system discovery
Resource awareness
Permission management
Upgrade coordination
Rollback coordination
Fault isolation
Recovery management
System state management
Local operation without mandatory cloud services

The core must remain as small and stable as practical so that other components can evolve without requiring fundamental changes to the platform.

Model Management Module

The Model Management Module manages the models available to LocalMind and provides a standardized interface between models and the rest of the platform.

Features

Model registration
Model discovery
Model metadata
Model versioning
Model capability profiles
Model compatibility detection
Model lifecycle management
Model loading
Model unloading
Model selection
Model routing
Model benchmarking
Model validation
Model conversion
Model optimization
Model compression
Model pruning
Model quantization
Model rollback
Multiple simultaneous models
Specialized model support
General-purpose model support
Small model support
Large model support
Mixture-of-experts support
Extensible model architecture support

The module must avoid requiring LocalMind to depend on a single model architecture or model provider.

Inference Module

The Inference Module provides the execution layer responsible for running models against assigned workloads.

Features

High-performance local inference
Low-latency execution
High-throughput execution
Streaming inference
Dynamic model selection
Dynamic runtime selection
Workload-aware execution
Hardware-aware execution
Adaptive batching
Continuous batching
Parallel execution
Speculative execution
Token generation optimization
Context-aware execution
Intelligent caching
KV-cache optimization
Memory residency optimization
Dynamic resource allocation
Runtime performance monitoring
Bottleneck detection
Automatic inference optimization
Inference fallback
Inference recovery

The inference system should optimize execution based on the actual workload and available resources rather than assuming that a particular model, runtime, or hardware platform is universally optimal.

Precision and Quantization Module

The Precision and Quantization Module manages numerical precision and model representations to improve performance, memory efficiency, and energy efficiency.

Features

FP32
FP16
BF16
INT8
INT4
Mixed precision
Extensible numerical formats
Automatic precision selection
Hardware-aware precision selection
Task-aware precision selection
Quantization benchmarking
Quality evaluation
Performance evaluation
Memory evaluation
Precision fallback
Quantization validation
Model-specific optimization

The module must allow future numerical formats to be incorporated without requiring redesign of the core platform.

Workload Intelligence Module

The Workload Intelligence Module analyzes incoming assignments and determines the resources, models, knowledge, tools, and execution strategies required to complete them.

Features

Task classification
Task decomposition
Task complexity estimation
Resource estimation
Latency requirement analysis
Quality requirement analysis
Workload prioritization
Model selection
Knowledge selection
Tool selection
Hardware selection
Inference strategy selection
Resource allocation
Deadline-aware execution
Multi-step task planning
Long-running task management
Task state persistence
Task performance measurement

The module should select the least resource-intensive configuration capable of meeting the requirements of a task whenever doing so provides a measurable efficiency advantage.

Adaptive Optimization Module

The Adaptive Optimization Module continuously evaluates the platform and searches for improved execution configurations.

Features

Continuous performance monitoring
Configuration experimentation
Automatic configuration tuning
Runtime optimization
Model optimization
Hardware optimization
Scheduling optimization
Memory optimization
Cache optimization
Precision optimization
Workload optimization
Energy optimization
Performance regression detection
Optimization benchmarking
Optimization history
Known-good configuration management
Automatic optimization rollback

Optimization decisions should be based on measurable results rather than assumptions about the superiority of a particular technology.

Hardware Abstraction Module

The Hardware Abstraction Module provides a common interface between LocalMind and the physical computing resources available to it.

Features

CPU discovery
GPU discovery
NPU discovery
TPU-compatible interfaces
FPGA discovery
ASIC discovery
RISC-V compatibility
Accelerator discovery
Hardware capability detection
Hardware capability profiles
Hardware-specific optimization
Hardware scheduling
Multi-accelerator support
Heterogeneous computing
Memory capability detection
Memory bandwidth detection
Compute capability detection
Power capability detection
Thermal capability detection
Hardware telemetry

The platform must not require a particular hardware vendor or accelerator architecture.

Open Hardware Module

The Open Hardware Module provides the interfaces required for integrating, developing, testing, and optimizing open hardware for LocalMind.

Features

Open accelerator interfaces
Custom inference accelerator support
RISC-V accelerator support
FPGA support
ASIC integration interfaces
Hardware capability declarations
Accelerator discovery
Accelerator scheduling
Hardware driver abstraction
Hardware performance profiles
Hardware benchmarking
Hardware validation
Hardware interoperability
Hardware upgrade support
Modular accelerator integration
Community-developed accelerator support
Future accelerator support

The module should allow hardware developers to create compatible computing components without requiring the platform to be redesigned for each new hardware implementation.

Memory Module

The Memory Module provides persistent and temporary memory services for models, agents, tasks, and applications.

Features

Short-term memory
Long-term memory
Persistent local memory
Structured memory
Semantic memory
Episodic memory
Task memory
User-controlled memory
Memory indexing
Memory retrieval
Memory compression
Memory pruning
Memory versioning
Memory provenance
Memory access controls
Memory optimization
Memory lifecycle management

Knowledge Module

The Knowledge Module provides structured and searchable knowledge for assigned tasks.

Features

Local knowledge bases
Task-specific knowledge bases
Knowledge domains
Knowledge packages
Knowledge indexing
Knowledge retrieval
Knowledge versioning
Knowledge provenance
Source tracking
Evidence tracking
Confidence scoring
Knowledge freshness tracking
Knowledge expiration
Superseded knowledge detection
Duplicate detection
Conflict detection
Knowledge gap identification
Knowledge validation
Knowledge synchronization

Knowledge should remain distinguishable from model parameters so that knowledge can be updated without requiring the entire model to be retrained.

Knowledge Evolution Module

The Knowledge Evolution Module allows LocalMind to improve the knowledge available for assigned tasks when validated new information or innovation becomes available.

Features

Automated knowledge discovery
Research discovery
Technical documentation discovery
Standards discovery
Algorithm discovery
Model discovery
Hardware innovation discovery
Tool discovery
Optimization discovery
Relevant innovation identification
Knowledge gap analysis
Source evaluation
Evidence evaluation
Knowledge validation
Sandbox testing
Benchmark testing
Knowledge promotion
Knowledge rejection
Knowledge archival
Knowledge replacement
Knowledge history
Provenance preservation

The system should distinguish discovery from adoption. Newly discovered information should not automatically become trusted production knowledge.

Innovation Evaluation Module

The Innovation Evaluation Module evaluates new technologies and determines whether they provide measurable value to LocalMind.

Features

Innovation intake
Relevance analysis
Capability comparison
Experimental evaluation
Sandbox execution
Benchmark comparison
Quality comparison
Latency comparison
Throughput comparison
Memory comparison
Energy comparison
Resource comparison
Security evaluation
Compatibility evaluation
Regression testing
Promotion criteria
Rejection criteria
Archival
Rollback
Innovation history

The preferred lifecycle is:

Discover → Verify → Evaluate → Test → Benchmark → Validate → Integrate → Monitor

A new technology should be adopted because it demonstrates value, not solely because it is newer.

Retrieval Module

The Retrieval Module provides local retrieval capabilities for knowledge, documents, memories, and task resources.

Features

Local retrieval-augmented generation
Document indexing
Semantic search
Keyword search
Hybrid search
Vector search
Metadata filtering
Source ranking
Relevance ranking
Citation tracking
Incremental indexing
Local knowledge repositories
Multiple knowledge sources
Retrieval benchmarking
Retrieval optimization
Knowledge synchronization

Agent Module

The Agent Module provides a modular execution framework for task-specific AI agents.

Features

Local AI agents
Specialized agents
Task-specific agents
General-purpose agents
Agent capability discovery
Agent-to-agent communication
Tool-enabled agents
Agent memory
Agent knowledge access
Agent permissions
Agent versioning
Agent upgrades
Agent rollback
Agent benchmarking
Agent monitoring
Human approval workflows
Agent lifecycle management

Agents should use the platform’s common model, memory, knowledge, inference, tool, and security interfaces.

Tool Module

The Tool Module provides standardized interfaces for external capabilities used by LocalMind.

Features

Modular tool integration
Local tool execution
Tool discovery
Tool registration
Tool versioning
Tool permissions
Sandboxed execution
Tool health monitoring
Tool benchmarking
Automatic tool selection
Task-specific tool routing
Replaceable tool implementations
Capability negotiation
Future tool compatibility

Resource Management Module

The Resource Management Module coordinates computational resources across models, agents, tools, and tasks.

Features

CPU scheduling
GPU scheduling
NPU scheduling
Accelerator scheduling
Memory allocation
VRAM allocation
Storage management
Compute prioritization
Resource quotas
Workload isolation
Thermal-aware scheduling
Power-aware scheduling
Energy-aware execution
Background workload management
Resource contention management
Dynamic resource reallocation
Priority management

Distributed Computing Module

The Distributed Computing Module allows multiple local machines and accelerators to cooperate as a unified computing environment.

Features

Multi-machine inference
Local cluster support
Distributed model execution
Distributed agents
Distributed retrieval
Distributed knowledge bases
Hardware pooling
Workload distribution
Node discovery
Node capability negotiation
Fault-tolerant execution
Node health monitoring
Local network inference
Distributed benchmarking
Resource pooling

Distributed operation must remain optional so that LocalMind can operate effectively on a single machine.

Edge Computing Module

The Edge Computing Module optimizes LocalMind for constrained and intermittently connected environments.

Features

Laptop deployment
Desktop deployment
Workstation deployment
Home server deployment
Edge-device deployment
Embedded deployment
Low-memory operation
Low-power operation
Offline operation
Intermittent-connectivity support
Local data processing
Hardware adaptation
Lightweight model support
Edge-specific optimization

Performance Benchmark Module

The Performance Benchmark Module provides standardized measurement of LocalMind configurations.

Features

Latency benchmarks
Time-to-first-token measurement
Token-generation benchmarks
Throughput benchmarks
Concurrent-request benchmarks
Memory benchmarks
Energy benchmarks
Power-efficiency benchmarks
Model-quality benchmarks
Task-completion benchmarks
Hardware utilization benchmarks
Regression testing
Performance history
Configuration comparison
Model comparison
Runtime comparison
Hardware comparison
Optimization comparison

Performance should be evaluated using representative workloads rather than relying solely on synthetic benchmarks.

Upgrade and Lifecycle Module

The Upgrade and Lifecycle Module manages the evolution of LocalMind components.

Features

Modular upgrades
Independent component upgrades
Model upgrades
Runtime upgrades
Agent upgrades
Knowledge upgrades
Plugin upgrades
Hardware upgrades
Driver upgrades
Accelerator upgrades
Compatibility testing
Dependency resolution
Pre-deployment validation
Staged deployment
Canary deployment
Upgrade snapshots
Rollback
Upgrade history
Upgrade recommendations
Known-good configuration preservation

Every upgrade should be capable of being evaluated against the current configuration before becoming the active production configuration.

Security Module

The Security Module provides local security controls for models, modules, agents, tools, data, hardware, and network access.

Features

Module permissions
Agent permissions
Tool permissions
Hardware access controls
Model isolation
Plugin isolation
Sandboxed execution
Secure module loading
Integrity verification
Package verification
Provenance verification
Audit logging
Network access controls
Local security policies
Human approval controls
Capability restrictions
Component trust policies

Privacy Module

The Privacy Module provides controls for keeping data and AI processing under local user control.

Features

Local inference
Local knowledge storage
Local memory
Local retrieval
Configurable network access
No mandatory cloud processing
User-controlled telemetry
Data minimization
Data retention controls
Local deletion controls
Privacy-preserving logging
Offline operation
Local credential management

Observability Module

The Observability Module provides visibility into platform behavior and performance.

Features

System telemetry
Inference telemetry
Model telemetry
Hardware telemetry
Agent telemetry
Knowledge telemetry
Resource utilization
Latency monitoring
Throughput monitoring
Error monitoring
Quality monitoring
Energy monitoring
Upgrade monitoring
Performance dashboards
Historical metrics
Audit trails
Component health status

Interoperability Module

The Interoperability Module defines common interfaces between LocalMind components.

Features

Open model interfaces
Open hardware interfaces
Runtime abstraction
Standardized module contracts
Capability negotiation
Version negotiation
Plugin APIs
Hardware APIs
Model APIs
Agent APIs
Knowledge APIs
Tool APIs
Import and export capabilities
Vendor-neutral interfaces
Future architecture compatibility

Interfaces should prioritize stable contracts and extensibility so that new implementations can be introduced without breaking existing components.

Reliability Module

The Reliability Module provides mechanisms for maintaining stable operation as components change.

Features

Health checks
Fault detection
Failure isolation
Automatic recovery
Service restart
Component rollback
Configuration recovery
Model fallback
Runtime fallback
Hardware fallback
Graceful degradation
Offline fallback
Redundant components
Known-good configurations
Failure history
Recovery testing

Developer Module

The Developer Module provides tools and interfaces for building LocalMind-compatible software and hardware.

Features

Modular development framework
Public module interfaces
Plugin development
Model integration APIs
Hardware integration APIs
Agent development APIs
Knowledge integration APIs
Runtime development APIs
Benchmarking APIs
Testing framework
Simulation environment
Hardware emulation
Development sandbox
Extension framework
Compatibility testing
Reference implementations

Optional Plugin Modules

LocalMind should support optional plugins that extend capabilities without modifying the core platform.

Web Research Plugin

Provides controlled access to external information sources when network access is enabled.

Additional Model Plugin

Provides integration with additional model families and model formats.

Coding Plugin

Provides software development, code analysis, testing, debugging, and local development capabilities.

Vision Plugin

Provides image understanding, visual analysis, document vision, and multimodal processing.

Audio Plugin

Provides speech recognition, audio analysis, transcription, and local audio processing.

Image Generation Plugin

Provides locally hosted image-generation capabilities through compatible models and hardware.

Video Plugin

Provides video analysis and other compatible local video-processing capabilities.

Robotics Plugin

Provides interfaces for robotics systems, sensors, actuators, and autonomous physical systems.

Simulation Plugin

Provides local simulation environments for testing models, agents, hardware configurations, and task workflows.

Additional Retrieval Plugin

Provides specialized retrieval systems and external knowledge connectors.

Database Plugin

Provides integrations with compatible local databases and structured data systems.

Distributed Network Plugin

Provides optional multi-machine networking and distributed LocalMind capabilities.

Federated Knowledge Plugin

Provides optional synchronization of validated knowledge between trusted LocalMind installations.

Hardware Development Plugin

Provides additional development and testing capabilities for compatible open hardware.

Accelerator Plugin

Provides support for newly developed inference accelerators without modifying the core hardware abstraction layer.

Benchmark Plugin

Provides additional standardized or domain-specific benchmark suites.

Domain Knowledge Plugin

Provides specialized knowledge packages for particular professional, scientific, technical, educational, or operational domains.

Modular Upgrade Requirements

LocalMind modules should be independently upgradeable whenever practical.

An upgrade system should support:

Compatibility checks
Dependency checks
Version checks
Security checks
Performance benchmarks
Regression tests
Resource checks
Configuration snapshots
Staged activation
Rollback
Upgrade history
Known-good configurations

A component upgrade should not require replacement of unrelated components unless a documented compatibility requirement makes the replacement necessary.

Continuous Improvement Requirements

LocalMind should support a continuous improvement lifecycle across software, models, hardware, knowledge, and task capabilities.

The platform should be capable of:

Discovering new capabilities
Identifying applicable innovations
Evaluating alternatives
Testing candidate improvements
Benchmarking candidates
Comparing candidates against existing implementations
Promoting validated improvements
Monitoring deployed improvements
Reverting unsuccessful improvements
Preserving improvement history

Continuous improvement must remain measurable, auditable, and reversible.

Task Optimization Objective

For each assigned task, LocalMind should seek an appropriate combination of:

Model
Knowledge
Memory
Tools
Agents
Inference runtime
Precision
Hardware
Compute resources
Execution strategy

The platform should optimize according to the requirements of the task, which may include:

Quality
Accuracy
Latency
Throughput
Memory usage
Energy usage
Compute usage
Cost
Privacy
Reliability
Availability

The platform should prefer efficient configurations when they satisfy the required task objectives.

Futureproofing Requirements

LocalMind should not depend on assumptions that a particular model architecture, numerical format, hardware architecture, inference runtime, or computing paradigm will remain dominant.

Future compatibility should be supported through:

Capability-based interfaces
Extensible module contracts
Versioned APIs
Hardware abstraction
Model abstraction
Runtime abstraction
Plugin interfaces
Capability negotiation
Backward compatibility
Migration pathways
Experimental capability support
Graceful degradation
Replaceable implementations

New technology should be able to enter the platform through modular interfaces rather than requiring the entire platform to be redesigned.

Local-First Requirements

LocalMind should be capable of operating locally without requiring a centralized AI service.

Local operation should support:

Local model execution
Local knowledge
Local memory
Local retrieval
Local agents
Local tools
Local hardware
Local benchmarking
Local upgrades
Local security controls
Local observability

Network connectivity may provide additional capabilities but should not be a mandatory dependency for the platform’s fundamental AI functions.

Open Hardware Requirements

LocalMind should provide a software foundation capable of supporting community-developed AI hardware.

Compatible hardware should be able to expose:

Compute capabilities
Memory capabilities
Supported numerical formats
Supported operations
Accelerator capabilities
Power characteristics
Thermal characteristics
Performance characteristics
Driver interfaces
Version information

The platform should use these capabilities to determine whether and how the hardware can contribute to inference and other workloads.

Performance Objective

LocalMind is designed to maximize practical AI performance from locally available resources.

The platform should optimize for:

Maximum task performance
Minimum practical latency
Maximum practical throughput
Efficient memory utilization
Efficient compute utilization
Efficient energy utilization
Efficient hardware utilization
High model quality
High task completion quality
Low infrastructure dependency

LocalMind should pursue competitive or superior performance relative to hosted AI services for applicable workloads while recognizing that performance varies according to models, hardware, workloads, and available resources.

Compliance

A LocalMind implementation should provide documented information describing:

Supported modules
Supported interfaces
Supported model formats
Supported hardware
Supported inference runtimes
Supported plugins
Performance characteristics
Known limitations
Compatibility requirements
Security considerations
Upgrade procedures

Implementations should identify deviations from required specification behavior.

Specification Evolution

The LocalMind specification should itself be modular and upgradeable.

Specification revisions should support:

Versioned requirements
Backward compatibility where practical
Deprecation policies
Migration guidance
New module definitions
New hardware capabilities
New model capabilities
New interface capabilities
New benchmark methods
Community contributions
Documented changes
Compatibility tracking

The specification should evolve as AI research, computing hardware, inference techniques, and knowledge systems evolve.

Specification Branding License (SBL)

Standard

Fully AGPL-3.0+ compliant system
Copyleft enforced for network deployments
Required attribution:
- Roxanne Ardary
- https://www.roxanneardary.com/

Optional

Specification Branding License (SBL)
- Attribution-free commercial deployment
- Pricing based on scale, usage, and deployment scope
- https://roxanneardary.com/localmind/

License & Notice Requirements

LocalMind is released under the GNU Affero General Public License v3.0 or later (AGPL-3.0+).

By contributing to any Open Arsenal project, you agree that your contributions will also be released under this license.

Please note the following:

All contributions must comply with the AGPL-3.0+ terms.
Under Section 7 of the license, all redistributions, forks, and derivative works must preserve attribution to:
Roxanne Ardary and roxanneardary.com.
LocalMind specifications are free to use with attribution. A Specification Branding License can be negotiated upon request.
The project’s notice.md file tracks attribution requirements and contributor acknowledgments. Any update that adds new contributors or modifies attribution should also update notice.md.
When submitting a pull request, ensure that any new files maintain the attribution headers where applicable.
Network-deployed versions of this software must also remain fully AGPL-3.0+ compliant, including exposure of source code modifications when applicable under the license.

For full legal details, please refer to the AGPL-3.0+ license and the project’s notice.md file.

License – LocalMind

This repository is licensed under the GNU Affero General Public License v3.0 or later (AGPL-3.0+).

Key Points

You are free to use, modify, and distribute the code.
All redistributions, forks, and derivative works or network-deployed versions must also be licensed under AGPL-3.0+ and provide attribution to Roxanne Ardary and roxanneardary.com as required under Section 7 of the license.
The software is provided “as is,” without warranty of any kind.

For the full license text, see GNU AGPL-3.0 License.