Edge Microvisor Toolkit Versions

Edge Microvisor Toolkit is available in several pre-configured versions that serve different purposes. Some are published as binaries, others are available from a custom build. This document will help you select the version that best suits your needs. To do so, check out:

How to select the right EMT

How EMT differs between versions

Version	Real Time	Stable Kernel	Next Kernel
Standalone (Immutable)	Available for opt-in	✓	✓
Developer Node (Mutable)	Optional	✓	✓
EMT for EMF	Available for opt-in	✓	✓
Bootkit	-	✓	–

How usage scenarios affect EMT setup

Real Time & Deterministic

Run latency-sensitive workloads with guaranteed bounded jitter and repeatable execution timelines across one or more hosts, maintainable under steady-state and failure-recovery conditions.

Primary outcomes:

• Bounded end-to-end latency & jitter

• Repeatable scheduling windows under load

• Cross-host timing consistency for distributed stages

• Fast, predictable recovery without violating SLOs

Technology areas:

• PREEMPT_RT kernel

• Resource Director Technologies

• Intel GPU RT

• CPU & Scheduler Isolation

• Memory Determinism

• Network Determinism (TSN)

• Time & Clocks

Kernel patchsets (quilts):

• PREEMPT_RT

• Time-Sensitive Networking

Virtual Machines (Kubernetes, shared GPUs)

Run multiple virtual machines on Kubernetes that concurrently share one or more physical GPUs, with predictable fairness, isolation, and policy-driven placement—using a KubeVirt stack extended for GPU sharing.

Primary outcomes:

• Stable, repeatable GPU performance per VM under contention

• Hard/soft sharing policies (fair-share, priority tiers, or quotas)

• Safe isolation between tenants/VMs (memory, contexts, resets)

• Schedulable resources with clear admission signals (no surprise fails)

• Operational guardrails: health checks, graceful drain/eviction, rollback

Technology areas:

• SRIOV

• Intel GPU

• kubevirt

• Host virtualization

• Intel GPU device plugin

Kernel patchsets (quilts):

• SR-IOV

• DRM

AI & Vision Systems

Enable AI inference and computer-vision workloads on edge nodes using Intel GPU and NPU acceleration, exposing unified hardware-assisted pipelines through standard APIs and user-space libraries.

Primary outcomes:

• Efficient execution of deep-learning and vision inference on-device without cloud dependency

• Unified GPU/NPU compute abstraction for developers (OpenVINO backend, media pipelines)

• Deterministic frame-rate and latency for multi-stream analytics workloads (e.g., camera ingest)

• Seamless integration with containers or pods, including dynamic device discovery and sharing

• Stable ABI/API interface across OS updates and driver versions

Technology areas:

• Edge AI packages

• OpenVino

• Intel GPU and NPU drivers

• Intel GPU device plugin

How to build your own version of EMT

You can create your own custom version of Edge Microvisor Toolkit by following the guide. You can also try and learn how to build your own solution and deploy it on edge.