OS-native ML resource orchestration
Enterprise-grade machine learning infrastructure that integrates directly with the operating system kernel for high-performance inference, efficient resource management, and seamless deployment.
Axon (Universal Model Installer)
MVP Complete
MLOS Core Runtime
Active Development
Advanced Features
Coming Soon
Production Readiness
Planning (Q2-Q3 2026)
Modern ML workloads face critical bottlenecks:
Standard ML frameworks operate at application level, introducing unnecessary latency and overhead
GPU memory fragmentation and poor scheduling waste compute resources
Disparate tools and frameworks require extensive integration work
Lack of kernel-level optimization limits throughput and latency
← Polyglot Plugin Architecture
← Orchestration & Management
← Direct OS Integration
Unlike application-layer abstractions, MLOS operates at the kernel level, treating ML models as first-class OS resources for unprecedented performance.
model.deploy({
priority: "realtime",
affinity: "gpu:0",
latency_target: "5ms"
});
✓ Model deployed successfully
✓ Zero-copy memory transfers enabled
✓ Optimized resource allocation
Preemption-aware scheduling with ML workload prioritization and dynamic resource allocation
Intelligent memory management: • Zero-copy tensor operations • Automatic memory pooling • GPU memory defragmentation • Cross-device memory coherence
Efficient memory utilization with automatic pooling and defragmentation
// Python Plugin
@mlos.plugin
class MyModel(SMIModel):
def predict(self, input):
return self.model.forward(input)
// Go Plugin
func (m *Model) Predict(input []float64) []float64 {
return m.engine.Forward(input)
}
Write plugins in any language through SMI. Language-agnostic communication via Protocol Buffers
Enterprise-grade GPU management: • Multi-GPU workload distribution • Automatic failover and recovery • GPU memory optimization • Real-time performance monitoring
Comprehensive GPU orchestration with automatic failover and optimization
MLOS is designed for high-performance ML inference with kernel-level optimizations, efficient resource management, and low-latency model serving.
Framework-agnostic plugin system for maximum flexibility and portability
HTTP/REST, gRPC, Unix IPC for flexible integration with any stack
High availability, automatic scaling, audit logging, and access control
Deploy latency-sensitive models for recommendation systems, fraud detection, and chatbots
Unified infrastructure for all organizational ML workloads
Experiment with models while maintaining production stability
Consistent interface from datacenter to edge devices
Axon is available now. MLOS Core runtime is in active development (Phase 1).
✓ Axon installed
✓ Model downloaded and cached
MLOS Core deployment coming in Phase 1
Development Status:
MLOS Foundation operates with a hybrid approach:
SMI specification and language bindings are fully open source
Protected core implementation ensures competitive advantage and quality
Examples, tutorials, and integrations developed collaboratively
Quick installation and first deployment
Complete API docs for all endpoints
Deep dive into MLOS internals and design
Real-world implementation patterns and code samples
Join forward-thinking teams building production AI with MLOS
Or explore our documentation and GitHub repositories