Horizon Runtime Specification

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Horizon Runtime

Built Together for the Future.

Specification

Horizon Runtime is an AGPL-3.0+ open source universal AI inference runtime specification for efficient, adaptive, hardware-independent execution of current and future artificial intelligence models.

The specification defines a modular architecture in which core capabilities provide the foundational runtime while optional plugin modules extend hardware support, model compatibility, precision formats, optimization strategies, integrations, and deployment capabilities.

Purpose

Horizon Runtime is designed to provide a universal execution layer for artificial intelligence without depending on a specific model family, hardware vendor, inference framework, numerical format, or deployment environment.

The runtime is intended to support local devices, workstations, servers, enterprise infrastructure, edge systems, private clouds, hybrid environments, and distributed clusters.

The primary design objective is to maximize useful AI computation while minimizing unnecessary memory consumption, latency, energy consumption, and hardware requirements.

Design Principles

Horizon Runtime shall follow these principles:

Modular architecture
Hardware independence
Model independence
Open source development
Open standards
Vendor neutrality
Adaptive execution
Mixed precision
Resource efficiency
Extensibility
Interoperability
Security
Observability
Reproducibility
Long-term compatibility
Community-driven development

Core Runtime

The Core Runtime provides the foundational execution environment shared by all supported AI workloads.

Core Runtime capabilities shall include:

Runtime initialization
Model lifecycle management
Execution lifecycle management
Runtime configuration
Hardware capability discovery
Model capability discovery
Resource discovery
Runtime health monitoring
Capability negotiation
Backend selection
Plugin discovery
Plugin lifecycle management
Error handling
Graceful degradation
Runtime diagnostics
Compatibility validation

The Core Runtime shall remain independent from individual model families and hardware vendors.

Model Abstraction Module

The Model Abstraction Module provides a standardized interface between models and the execution engine.

Capabilities shall include:

Model loading
Model unloading
Model validation
Model metadata discovery
Model architecture detection
Model capability detection
Tokenizer integration
Model configuration
Model version management
Model caching
Model hot swapping
Partial model loading
Lazy model loading
Streaming model loading
Model compatibility reporting

The module shall allow new model architectures to be introduced without requiring fundamental changes to the Core Runtime.

Inference Execution Module

The Inference Execution Module manages execution of AI workloads.

Capabilities shall include:

Token generation
Batch inference
Continuous batching
Streaming inference
Sequence management
Dynamic batch sizing
Operator execution
Tensor execution
Execution graph management
Execution optimization
Workload scheduling
Runtime execution profiling
Multi-request execution

The module shall support different execution strategies through defined interfaces.

Adaptive Precision Module

The Adaptive Precision Module provides mixed-precision execution and runtime precision management.

Capabilities shall include:

Dynamic precision selection
Layer-level precision assignment
Operator-level precision assignment
Tensor-level precision assignment
Weight precision management
Activation precision management
Attention precision management
Embedding precision management
Output precision management
Expert precision management
KV cache precision management
Runtime precision switching
Precision fallback
Precision validation
Accuracy-aware precision selection
Memory-aware precision selection
Latency-aware precision selection
Throughput-aware precision selection
Power-aware precision selection

The module shall support current and future numerical formats through extensible precision interfaces.

Supported precision classes may include:

INT2
INT3
INT4
INT8
FP4
FP6
FP8
BF16
FP16
FP32
Hardware-specific numerical formats

Adaptive Compute Module

The Adaptive Compute Module manages computational effort according to workload requirements.

Capabilities shall include:

Workload classification
Complexity estimation
Dynamic compute allocation
Compute budgets
Dynamic token budgets
Adaptive reasoning budgets
Request prioritization
Latency-aware execution
Throughput-aware execution
Power-aware execution
Resource-aware execution
Early termination
Confidence-based termination
Background processing
Interactive processing
Batch processing

The module shall allow computational resources to scale according to task requirements rather than applying identical compute policies to every request.

Reasoning Module

The Reasoning Module provides execution support for reasoning-oriented models and workloads.

Capabilities shall include:

Reasoning model execution
Adaptive reasoning depth
Dynamic thinking budgets
Reasoning token management
Reasoning verification
Multi-pass reasoning
Confidence estimation
Answer verification
Reasoning compression
Tool-assisted reasoning
External computation
Reasoning performance measurement
Reasoning resource allocation

The module shall permit reasoning depth and computational resources to be adjusted according to workload complexity.

Speculative Decoding Module

The Speculative Decoding Module improves generation performance by allowing draft and target execution strategies.

Capabilities shall include:

Draft model execution
Target model verification
Multi-token prediction
Adaptive draft length
Acceptance threshold management
Confidence-aware speculation
Dynamic speculation policies
Hardware-aware speculation
Multiple draft model support
Speculative decoding telemetry

Future decoding strategies shall be supported through extensible interfaces.

Memory Management Module

The Memory Management Module manages system and accelerator memory throughout inference.

Capabilities shall include:

VRAM management
RAM management
Unified memory management
Memory pooling
Tensor reuse
Memory mapping
Memory offloading
CPU offloading
Accelerator offloading
Lazy tensor loading
Streaming weights
Memory pressure detection
Memory reclamation
Memory fragmentation management
Memory budgeting
Resource-aware allocation

The module shall dynamically allocate memory according to model requirements, hardware capabilities, and active workloads.

KV Cache Module

The KV Cache Module provides optimized management of transformer attention state.

Capabilities shall include:

KV cache allocation
KV cache quantization
KV cache compression
Paged KV cache
Dynamic cache allocation
Cache eviction
Cache reuse
Prefix caching
Prompt caching
Shared prompt caching
Context-aware cache management
Cache memory budgeting
Cache performance measurement

The module shall support long-context workloads while minimizing memory consumption.

Long Context Module

The Long Context Module manages large context workloads.

Capabilities shall include:

Extended context execution
Context window management
Context streaming
Context compression
Context prioritization
Sliding-window execution
Prefix reuse
Context caching
Context memory management
Retrieval-assisted context management
Adaptive context allocation

Mixture of Experts Module

The Mixture of Experts Module provides optimized execution for sparse expert architectures.

Capabilities shall include:

Expert routing
Sparse expert execution
Dynamic expert selection
Expert caching
Expert preloading
Expert unloading
Expert hot loading
Expert prioritization
Expert load balancing
Expert utilization monitoring
Expert memory management
Expert-aware batching
Expert-aware scheduling
Distributed expert execution

Scheduler Module

The Scheduler Module manages competing inference workloads.

Capabilities shall include:

Request queues
Priority queues
Fair scheduling
Resource-aware scheduling
Deadline-aware scheduling
Latency-sensitive scheduling
Throughput-sensitive scheduling
Multi-tenant scheduling
Workload isolation
Dynamic batching
Device-aware scheduling
Memory-aware scheduling
Power-aware scheduling

Scheduling policies shall be extensible.

Hardware Abstraction Module

The Hardware Abstraction Module provides a common interface between the runtime and physical compute devices.

Capabilities shall include:

Hardware discovery
Device capability detection
Memory capability detection
Precision capability detection
Compute capability detection
Device selection
Device allocation
Device monitoring
Backend selection
Backend fallback
Heterogeneous device execution

The module shall prevent hardware-specific logic from becoming a dependency of the Core Runtime.

CPU Execution Module

The CPU execution module shall support:

x86-64 processors
ARM64 processors
Multithreaded execution
Vectorized execution
Quantized CPU inference
CPU memory optimization
CPU affinity
NUMA-aware execution
CPU resource management

Accelerator Execution Module

The Accelerator Execution Module shall provide a common interface for supported accelerator backends.

Potential backend capabilities shall include:

GPU execution
AI accelerator execution
Custom accelerator execution
Accelerator memory management
Accelerator-specific kernels
Accelerator-specific precision
Multi-device execution
Device synchronization
Backend fallback

Distributed Execution Module

The Distributed Execution Module provides multi-device and multi-node execution.

Capabilities shall include:

Multi-GPU inference
Multi-node inference
Tensor parallelism
Pipeline parallelism
Model parallelism
Data parallelism
Expert parallelism
Hybrid parallelism
Distributed model loading
Distributed KV caching
Distributed expert execution
Workload distribution
Request routing
Load balancing
Node health monitoring
Failure detection
Node recovery
Graceful degradation
Horizontal scaling

Multimodal Module

The Multimodal Module provides common execution interfaces for multiple input and output modalities.

Supported modalities shall include:

Text
Images
Audio
Video
Multimodal embeddings

Capabilities shall include:

Multimodal input processing
Multimodal context management
Cross-modal inference
Modality-specific precision policies
Multimodal batching
Modality conversion
Multimodal streaming

Vision Module

The Vision Module shall support:

Image classification
Object detection
Image segmentation
OCR
Image embeddings
Vision-language inference
Image preprocessing
Image batching
Vision model quantization
Vision model adapters

Speech and Audio Module

The Speech and Audio Module shall support:

Speech-to-text
Audio classification
Audio embeddings
Streaming transcription
Real-time audio processing
Audio batching
Audio model quantization
Voice activity detection integrations
Text-to-speech integrations

Embedding Module

The Embedding Module shall support:

Text embeddings
Image embeddings
Multimodal embeddings
Batch embedding generation
Vector normalization
Similarity computation
Embedding caching
Retrieval integration

Retrieval Module

The Retrieval Module shall provide interfaces for retrieval-augmented workloads.

Capabilities shall include:

Retrieval-augmented generation
Document retrieval
Vector search
Semantic search
Hybrid search
Retrieval filtering
Context ranking
Context compression
Retrieval caching
Retrieval-aware inference

Agent Runtime Module

The Agent Runtime Module provides infrastructure for AI agents.

Capabilities shall include:

Agent execution
Task decomposition
Task planning
Tool calling
Tool discovery
Tool authorization
Multi-step workflows
Agent memory
Agent state management
Agent handoffs
Multi-agent collaboration
Agent verification
Human approval checkpoints
Agent observability

Tool Integration Module

The Tool Integration Module shall provide controlled access to external capabilities.

Capabilities shall include:

Tool discovery
Tool registration
Tool authorization
Tool execution
Tool monitoring
Tool failure handling
Local tool integration
Remote tool integration
Database integration
Filesystem integration
API integration
Computational tool integration
Model Context Protocol integration

API Module

The API Module shall provide standardized interfaces for applications.

Capabilities shall include:

REST API
OpenAI-compatible API
Streaming API
Chat API
Completion API
Embedding API
Reasoning API
Vision API
Speech API
Model management API
Runtime management API
Health endpoints
Metrics endpoints

Developer Interface Module

The Developer Interface Module shall provide:

Command-line interfaces
SDK interfaces
Runtime APIs
Plugin APIs
Backend APIs
Model adapter APIs
Configuration APIs
Diagnostics
Debugging interfaces
Performance profiling

Potential SDK languages shall include:

Python
Rust
Go
JavaScript
TypeScript
C#
Java

Observability Module

The Observability Module shall provide runtime visibility.

Capabilities shall include:

Request metrics
Token metrics
Latency metrics
Throughput metrics
Memory metrics
CPU metrics
GPU metrics
Accelerator metrics
Power metrics
Precision metrics
Cache metrics
Expert routing metrics
Scheduling metrics
Error metrics

Logging Module

The Logging Module shall support:

Structured logging
Runtime logs
Request logs
Error logs
Performance logs
Hardware logs
Security logs
Configurable log levels
Local logging
Centralized logging integrations

Benchmarking Module

The Benchmarking Module shall provide standardized performance measurement.

Capabilities shall include:

Model benchmarks
Backend benchmarks
Precision benchmarks
Hardware benchmarks
Memory benchmarks
Latency benchmarks
Throughput benchmarks
Power benchmarks
Reasoning benchmarks
Regression benchmarks
Cross-device benchmarks
Cross-model benchmarks
Automated benchmark reporting

Accuracy Evaluation Module

The Accuracy Evaluation Module shall measure quality impacts caused by optimization.

Capabilities shall include:

Precision comparison
Quantization quality testing
Model output comparison
Regression detection
Benchmark dataset support
Task-specific evaluation
Accuracy versus latency analysis
Accuracy versus memory analysis
Accuracy versus power analysis

Security Module

The Security Module shall provide:

API authentication
API keys
Role-based access control
Permission management
Tool authorization
Model access controls
Tenant isolation
Audit logging
Secure configuration
Secrets management integrations
Network security controls
Local-only operation
Air-gapped deployment

Reliability Module

The Reliability Module shall provide:

Health checks
Runtime self-tests
Model validation
Backend validation
Graceful failure handling
Backend fallback
Request retry policies
Fault isolation
Resource exhaustion protection
Model recovery
Node recovery
Distributed failure handling

Configuration Module

The Configuration Module shall support:

Configuration files
Environment variables
Runtime configuration APIs
Model-specific policies
Hardware-specific policies
Precision policies
Scheduling policies
Memory policies
Security policies
User-defined profiles
Configuration validation
Configuration versioning

Interoperability Module

The Interoperability Module shall provide:

Open model format support
Open API support
Open standards support
MCP integration
Container integration
Kubernetes integration
Cloud-neutral deployment
Hardware-neutral execution
Vendor-neutral integration
External runtime integration

Deployment Module

The Deployment Module shall support:

Desktop deployment
Workstation deployment
Server deployment
Enterprise deployment
Private cloud deployment
Hybrid deployment
Edge deployment
Cluster deployment
Containerized deployment
Kubernetes deployment
Air-gapped deployment

Optional Plugin Modules

Optional plugin modules shall extend Horizon Runtime without requiring changes to the core architecture.

Hardware Backend Plugins

Hardware backend plugins may provide support for:

NVIDIA CUDA
AMD ROCm
Apple Metal
Vulkan
OpenCL
Future AI accelerators
Custom accelerators
FPGA execution
RISC-V AI hardware
Specialized inference hardware

Model Format Plugins

Model format plugins may provide:

GGUF
Safetensors
ONNX
MLX
AWQ
GPTQ
EXL2
Future model formats

Quantization Plugins

Quantization plugins may provide:

Weight quantization
Activation quantization
KV cache quantization
Dynamic quantization
Static quantization
Calibration
Quantization-aware optimization
Future quantization algorithms

Precision Plugins

Precision plugins may provide:

New numerical formats
Hardware-specific precision
Custom tensor formats
Precision conversion
Precision calibration
Precision optimization

Kernel Plugins

Kernel plugins may provide:

Hardware-specific kernels
Optimized operators
Fused operations
Attention kernels
Matrix multiplication kernels
Quantized kernels
Future accelerator kernels

Scheduler Plugins

Scheduler plugins may provide:

Custom scheduling policies
Priority strategies
Workload classification
Latency optimization
Throughput optimization
Power optimization
Multi-tenant policies

Memory Plugins

Memory plugins may provide:

Specialized memory allocators
Persistent caches
Distributed memory
External memory systems
Hardware-specific memory management
Advanced compression

Model Adapter Plugins

Model adapter plugins may provide:

Model-family support
Custom architectures
Tokenizer integrations
Vision adapters
Audio adapters
Embedding adapters
Future model architectures

Agent Plugins

Agent plugins may provide:

Agent frameworks
Planning systems
Memory systems
Multi-agent orchestration
Workflow engines
External tool ecosystems

Monitoring Plugins

Monitoring plugins may integrate:

Metrics platforms
Logging platforms
Tracing systems
Performance dashboards
Hardware monitoring
Enterprise observability systems

Authentication Plugins

Authentication plugins may provide:

Identity providers
Single sign-on
OAuth integrations
Enterprise directory systems
Certificate-based authentication
Custom authentication systems

Plugin Requirements

Plugins shall:

Use documented interfaces
Declare compatibility requirements
Declare supported capabilities
Avoid unnecessary modification of core components
Respect runtime security policies
Provide version information
Provide failure handling
Maintain compatibility with applicable licensing requirements

Resource Efficiency

Horizon Runtime shall prioritize efficient use of:

Compute
Memory
Storage
Network bandwidth
Accelerator capacity
CPU capacity
Power

The runtime should measure efficiency using metrics such as:

Tokens per second
Requests per second
Latency per request
Memory per model
Memory per active request
Energy per token
Performance per watt
Performance per unit of memory

Future Compatibility

Horizon Runtime shall use modular interfaces to accommodate:

New model architectures
New reasoning approaches
New precision formats
New quantization methods
New accelerator architectures
New memory technologies
New inference algorithms
New deployment environments
New AI modalities
New interoperability standards

Future functionality should be implemented through new modules or plugins whenever practical rather than replacing stable core interfaces.

Open Standards

Horizon Runtime should prioritize open standards and interoperable interfaces wherever suitable.

The runtime should avoid unnecessary dependence on:

Proprietary model formats
Proprietary APIs
Proprietary hardware
Vendor-specific deployment environments
Closed inference protocols

Community Development

Horizon Runtime is intended to support community-driven development through:

Public specifications
Open source implementation
Shared benchmarks
Community plugins
Community model adapters
Community hardware backends
Reproducible testing
Transparent development
Contributor recognition
Shared optimization research

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/horizon-runtime/

License & Notice Requirements

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

By contributing to this 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.
Horizon Runtime 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.

Notice – Horizon Runtime

Attribution Requirement: Under Section 7 of the AGPL 3.0+ license, all redistributions, forks, and derivative works, including network-deployed versions of this project, must provide attribution to Roxanne Ardary and roxanneardary.com.

Contributors

This file tracks contributors and their specific contributions to the project.

Roxanne Ardary, roxanneardary.com – June 6, 2026
Created the repository for Horizon Runtime. Designed the AGPL-3.0+ universal AI runtime architecture focused on adaptive mixed-precision inference, hardware abstraction, model interoperability, distributed execution, and future-proof AI infrastructure.
[Add other contributors here] – [Date]
[Describe contribution in one sentence]

License – Horizon Runtime

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.