# 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:

1. How to select the right EMT.

   The diagram
   below will help you select the toolkit version right for your workflow.

   ![emt-version-deployment](../assets/emt-version-deployment.drawio.svg)

2. How EMT differs between versions.

   | Version | Real Time | Stable Kernel | [Next Kernel](../emt-architecture-overview.md#next-kernel) |
   |--------|---------|-------------|------|
   | [**Standalone (Immutable)**](https://github.com/open-edge-platform/edge-microvisor-toolkit-standalone-node) | Available for opt-in | ✓ | ✓ |
   | [**Developer Node (Mutable)**](../emt-architecture-overview.md#developer-node-mutable-iso-image) | Optional | ✓ | ✓ |
   | [**EMT for EMF**](../emt-deployment-edge-orchestrator.md) | Available for opt-in | ✓ | ✓ |
   | [**Bootkit**](../emt-bootkit.md) | - | ✓ | – |

3. How usage scenarios affect EMT setup.

   | Scenario | Description | Primary outcomes | Technology areas |
   |---|---|---|---|
   | Real-time & deterministic workloads | 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 | <br> - Bounded end-to-end latency & jitter <br> - Repeatable scheduling windows under load <br> - Cross-host timing consistency for distributed stages <br> - Fast, predictable recovery without violating SLOs | <br> - [PREEMPT_RT kernel](../emt-architecture-overview.md#preempt-rt-kernel) <br> - [Resource Director Technologies](../emt-architecture-overview.md#resource-director-technology) <br> - [Intel GPU RT](../emt-architecture-overview.md#intel-device-plugins-for-kubernetes) <br> - [CPU & Scheduler Isolation](../emt-architecture-overview.md#isolcpuslist) <br> - [Memory Determinism](../emt-architecture-overview.md#preempt-rt-kernel) <br> - Time & Clocks <br> - [Network Determinism (TSN)](../emt-architecture-overview.md#time-sensitive-networking-support) |
   | VM-based workloads on Kubernetes with 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 | <br> - Stable, repeatable GPU performance per VM under contention <br> - Hard/soft sharing policies (fair-share, priority tiers, or quotas) <br> - Safe isolation between tenants/VMs (memory, contexts, resets) <br> - Schedulable resources with clear admission signals (no surprise fails) <br> - Operational guardrails: health checks, graceful drain/eviction, rollback | <br> - [SRIOV](./deployment/emt-vm-host.md) <br> - [Intel GPU](../emt-system-requirements.md#discrete-gpu) <br> - [kubevirt](https://github.com/open-edge-platform/edge-microvisor-toolkit-standalone-node/blob/main/standalone-node/docs/user-guide/desktop-virtualization-image-guide.md) <br> - [Host virtualization](./deployment/emt-vm-host.md) <br> - [Intel GPU device plugin](../emt-architecture-overview.md#intel-device-plugins-for-kubernetes) |
   | AI & Vision workloads | 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 | <br> - Efficient execution of deep-learning and vision inference on-device without cloud dependency <br> - Unified GPU/NPU compute abstraction for developers (OpenVINO backend, media pipelines) <br> - Deterministic frame-rate and latency for multi-stream analytics workloads (e.g., camera ingest) <br> - Seamless integration with containers or pods, including dynamic device discovery and sharing <br> - Stable ABI/API interface across [OS updates](../architecture/emt-updates.md) and driver versions | <br> - [Edge AI packages](https://eci.intel.com/docs/3.3/packages_list.html) <br> - [OpenVino](https://docs.openvino.ai) <br> - [Intel GPU and NPU drivers](https://docs.openvino.ai/2025/openvino-workflow/running-inference/inference-devices-and-modes.html) <br> - [Intel GPU device plugin](../emt-architecture-overview.md#intel-device-plugins-for-kubernetes) |

4. How to build your own version of EMT.

   You can create your own custom version of Edge Microvisor Toolkit by following
   [the guide](./emt-building-howto.md). You can also try and learn how to
   [build your own solution and deploy it on edge](./emt-build-and-deploy.md).