Kernel-native ML execution. One unified runtime for ONNX, PyTorch, and LLMs. Predictive memory management that cuts latency by 30%.
Everything you need to run models in production, without the infrastructure headaches.
ML workloads are first-class OS citizens. Priority-based scheduling ensures latency-sensitive inference always runs first.
NUMA-aware tensor placement with prefetching. Memory is ready before your model needs it.
One API for ONNX, PyTorch, TensorFlow, and GGUF models. No more juggling frameworks.
Built-in llama.cpp integration. Run Llama, Qwen, DeepSeek and other LLMs with streaming generation.
Comprehensive observability for inference latency, memory usage, and model health.
Standard HTTP interface that works with any language. Register, load, inferβdone.
Install the CLI, add your model, and start serving. MLOS handles conversion, optimization, and scaling automatically.
Every model validated in our E2E pipeline. If it's here, it works.
Updates from the MLOS Foundation
Deep dives, technical insights, and updates from the MLOS Foundation
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