Microsoft has quietly issued KB5068004 — an update that delivers Nvidia TensorRT RTX Execution Provider version 1.8.14.0 as a Windows component for Copilot+ RTX PCs running Windows 11, version 24H2. The bulletin is deliberately short: the package “includes improvements to the execution provider component,” will be installed automatically via Windows Update once the device has the latest cumulative update for 24H2, and will appear in Update history as “2025-09 Nvidia TensorRT RTX version 1.8.14.0 (KB5068004).”
The Nvidia TensorRT RTX Execution Provider (TensorRT-RTX EP) is an execution provider (EP) integration for ONNX Runtime and Windows ML that targets consumer RTX GPUs. Microsoft and NVIDIA integrated this execution provider to provide a smaller-footprint, faster-loading, and more RTX‑focused alternative to the legacy datacenter TensorRT EP and to the general-purpose CUDA EP. The ONNX Runtime documentation explicitly positions TensorRT‑RTX as the preferred EP for RTX consumer hardware, citing a smaller runtime footprint, quicker model compile and load times, and improved usability for cached models across multiple RTX GPUs.
NVIDIA’s documentation and Windows‑focused marketing likewise emphasize that the RTX‑tailored TensorRT runtime — sometimes branded “TensorRT for RTX” or “TensorRT‑RTX” — is designed for on‑device, just‑in‑time engine building and lower packaging overhead compared with datacenter TensorRT workflows. This makes it attractive for consumer AI features in Windows (for example, Windows ML and app scenarios that rely on fast, local inference).
However, the public KB lacks an itemized changelog or performance numbers. That means administrators and developers must verify outcomes on their hardware and models before assuming universal gains. Prepare to stage the rollout, validate operator coverage and driver compatibility, and maintain rollback procedures in case of regressions. Historical guidance from other Microsoft AI component rollouts shows driver mismatches and opaque changelogs as the chief practical risks — the same mitigations apply here.
Overall, KB5068004 is an incremental but strategically aligned update: it improves the runtime foundation for RTX PC‑focused AI features and lets Microsoft and NVIDIA iterate faster on the Windows AI experience. The practical value for any given device will depend on driver/firmware alignment and the specific models and operators in use — validate empirically, stage carefully, and monitor for both performance gains and visual/regression artifacts.
This bulletin is a small but meaningful step in the evolving Windows on‑device AI stack. Follow the verification steps above, and treat the update as a component‑level change requiring the same staged validation and driver coordination you use for other platform middleware updates.
Source: Microsoft Support KB5068004: Nvidia TensorRT RTX Execution Provider AI component update (1.8.14.0) - Microsoft Support
The Nvidia TensorRT RTX Execution Provider (TensorRT-RTX EP) is an execution provider (EP) integration for ONNX Runtime and Windows ML that targets consumer RTX GPUs. Microsoft and NVIDIA integrated this execution provider to provide a smaller-footprint, faster-loading, and more RTX‑focused alternative to the legacy datacenter TensorRT EP and to the general-purpose CUDA EP. The ONNX Runtime documentation explicitly positions TensorRT‑RTX as the preferred EP for RTX consumer hardware, citing a smaller runtime footprint, quicker model compile and load times, and improved usability for cached models across multiple RTX GPUs. NVIDIA’s documentation and Windows‑focused marketing likewise emphasize that the RTX‑tailored TensorRT runtime — sometimes branded “TensorRT for RTX” or “TensorRT‑RTX” — is designed for on‑device, just‑in‑time engine building and lower packaging overhead compared with datacenter TensorRT workflows. This makes it attractive for consumer AI features in Windows (for example, Windows ML and app scenarios that rely on fast, local inference).
What KB5068004 actually says
- Scope: Applies only to Copilot+ PCs running Windows 11, version 24H2 (all SKUs listed).
- Content: Installs Nvidia TensorRT RTX Execution Provider version 1.8.14.0 as an OS component and states only that it “includes improvements to the execution provider component.”
- Delivery: Automatic via Windows Update. The update requires the latest cumulative update (LCU) for Windows 11, version 24H2 before it will install.
- Visibility: After installation it will show in Settings → Windows Update → Update history with the 2025‑09 version string.
Why this update matters (technical context)
The TensorRT‑RTX Execution Provider changes the runtime surface that ONNX Runtime and Windows ML use to accelerate ONNX models on RTX GPUs. Key technical characteristics and implications:- Designed for RTX consumer hardware (Ampere or later): ONNX Runtime notes TensorRT‑RTX targets RTX GPUs from Ampere onward, focusing on the RTX PC profile rather than datacenter GPUs. That hardware targeting enables optimizations that assume consumer GPU memory/driver characteristics.
- Faster model compile/load and smaller footprint: TensorRT‑RTX uses on‑device, just‑in‑time engine generation and a lighter packaging profile. That lowers the latency for the first inference and reduces the disk / installer footprint compared with legacy TensorRT engines that often required prebuilt, model‑specific artifacts. These characteristics directly benefit interactive Windows AI features that must initialize quickly.
- Better caching and multi‑GPU usability: The RTX EP was engineered for improved cached engine reuse across RTX GPUs in the same system, which matters for devices with multi‑GPU or GPU‑switchable configurations. ONNX Runtime documentation specifically calls this out as a usability improvement over the datacenter TensorRT EP.
- Fallback semantics and operator coverage: TensorRT (any variant) does not implement every possible ONNX operator. When the EP cannot cover operators in a model, subgraphs fall back to other providers (CUDA or CPU). That behavior means end‑to‑end performance depends on operator coverage and how ONNX Runtime partitions a model across EPs. ONNX Runtime docs caution that TensorRT’s performance advantage depends on whether it supports the operators the model uses.
Cross‑referenced verification and claims
Microsoft’s KB states the package version and distribution method but provides no technical changelog; for technical claims about runtime behavior and performance, the public ONNX Runtime docs and NVIDIA’s TensorRT‑RTX documentation are the authoritative technical references. Use the following as the cross‑reference baseline:- Microsoft KB (release metadata, installation behavior and version string).
- ONNX Runtime documentation (TensorRT‑RTX EP description, runtime behavior and EP selection semantics).
- NVIDIA TensorRT for RTX docs and product / launch posts (implementation details and performance positioning for RTX PCs).
Benefits for users and developers
- End users on qualifying RTX Copilot+ PCs should see faster startup for AI‑driven Windows features that call into Windows ML or ONNX Runtime, because TensorRT‑RTX emphasizes lower compile/load latency and a smaller runtime footprint. This can make features that run local models feel more responsive.
- App developers who ship ONNX models benefit from a runtime that can build optimized engines on a user’s machine quickly, reducing the need for shipping pregenerated TensorRT engines with the app. That reduces packaging complexity and can simplify per‑GPU tuning.
- For power and thermal‑sensitive workloads, offloading more work to the GPU inference path (through a well‑implemented EP) can reduce CPU load and, in some cases, improve energy efficiency depending on the device’s power profile. Real‑world gains will vary by model and driver stack.
Risks, compatibility issues, and operational considerations
- Opaque changelog risk: Microsoft’s KB does not list precise fixes or CVE identifiers, and the update is deployed automatically. Enterprises that require traceable security fixes or CVE identifiers will find the published KB insufficient and should request coordinated advisories from Microsoft or the OEM. Treat claims of fixed vulnerabilities in such minimal KBs as unverified until explicit security advisories are published.
- Driver/stack mismatch: TensorRT‑RTX relies on GPU drivers and on-device runtimes. If OEM/NVIDIA drivers on a device are outdated or custom OEM packages differ from vendor reference drivers, compatibility and stability regressions are possible. Staged rollout and driver alignment are essential mitigations. Community experience with component updates in the Windows AI stack has shown that driver mismatches are the most common source of post‑update regressions.
- Operator support gaps: Not every ONNX operator is implemented in TensorRT‑RTX. Models that rely on unsupported ops will be partially offloaded, causing mixed EP execution that may or may not yield net performance gains. Developers must validate their specific models across EPs and prepare fallbacks. ONNX Runtime documentation warns that TensorRT’s advantage is model‑dependent.
- Fragmentation and inventory: Microsoft’s approach of per‑silicon component updates (Intel/AMD/Qualcomm/NVIDIA variants) means different devices in an environment can be running different component versions. Administrators should add component‑level detection to inventory systems so they can track which devices received KB5068004 and which remain on prior EP versions. Historical guidance for other Windows AI component updates recommends staged testing and inventorying component versions.
- Rollback and troubleshooting complexity: Because this installs as an OS component (delivered via Windows Update), the rollback path may be different from a user‑level library install. Administrators should document rollback steps and have OEM/NVIDIA drivers available for reinstallation. Monitor Event Viewer, WER buckets, and app‑level telemetry for regressions.
How to verify the update and validate behavior (recommended steps)
- Confirm installation: Settings → Windows Update → Update history should list “2025‑09 Nvidia TensorRT RTX version 1.8.14.0 (KB5068004).” If the entry is missing, confirm the device has the latest cumulative update for Windows 11, version 24H2 installed.
- Verify ONNX Runtime providers: Use the ONNX Runtime API to list registered execution providers (for example, call get_providers or get_provider_options) to confirm TensorRT‑RTX appears at runtime. ONNX Runtime docs provide the exact API calls for Python/C++ bindings.
- Run a representative benchmark: Execute a short microbenchmark of your model (latency and memory) before and after the update. Measure end‑to‑end latency and record whether portions of the model fall back to CUDA or CPU. Repeat tests across typical input sizes and batch sizes.
- Check drivers and firmware: Confirm GPU drivers are at a recommended level from NVIDIA/OEM. If regressions appear, try updating to the latest OEM/NVIDIA driver and retest; if issues persist, collect logs and file a support case.
- Monitor user‑facing features: For consumer features that depend on Windows ML (photo upscaling, live effects), verify visual quality and functional behavior — segmentation masks, denoising artifacts, and color stability — and compare with baseline captures. If visual artifacts change, capture samples for vendor support.
Practical guidance for developers
- Prefer explicit provider registration during testing: When constructing ONNX Runtime sessions, register providers intentionally (for example, ['TensorrtRtxExecutionProvider', 'CUDAExecutionProvider', 'CPUExecutionProvider']) to control priority and test fallback behavior. ONNX Runtime’s provider API lets you inspect and tune priority.
- Validate operator coverage: Use small diagnostic models that exercise the opset and operator mix used by your production model to discover unsupported ops that will cause subgraph fallback. Where fallback is expected, measure whether the mixed execution still meets performance targets.
- Test on real hardware: TensorRT‑RTX’s benefits are hardware dependent (Ampere+ RTX GPUs). Emulate or test on representative RTX hardware to avoid surprises when the library is deployed via Windows Update.
- Watch for changes in precision and quantization behavior: TensorRT often leverages FP16/INT8 paths for speed. Confirm numerical fidelity on tasks sensitive to small errors (e.g., image restoration models). If quantization is used, compare visual quality across precisions and across driver/runtime versions.
Enterprise rollout checklist (recommended)
- Inventory: Add component-level detection to endpoint inventories to capture TensorRT‑RTX version strings.
- Pilot ring: Deploy KB5068004 to a small representative pilot fleet that covers OEM diversity, driver variants, and typical workloads. Monitor for 7–14 days.
- Driver alignment: Ensure OEM/NVIDIA driver versions are in the recommended range before broad deployment.
- Telemetry & logging: Capture pre/post telemetry (latency, CPU/GPU utilization, WER buckets) and collect user‑reported visual artifacts with timestamped samples.
- Rollback plan: Document rollback steps (Update history uninstall, driver reinstall) and escalation contacts (OEM/Microsoft/NVIDIA).
Final assessment — what to expect, and what to watch
KB5068004 is a routine but important housekeeping update in Microsoft’s broader strategy of shipping small, per‑silicon AI components that enable on‑device inference and faster feature iteration. For most qualifying RTX Copilot+ PCs, the update likely improves startup latency for Windows ML and ONNX Runtime features and reduces runtime footprint for the TensorRT execution layer — benefits that are most visible in interactive scenarios and small model loads. The ONNX Runtime and NVIDIA docs describe the technical reasons for those benefits: smaller packaging, JIT engine build, and RTX‑targeted optimizations.However, the public KB lacks an itemized changelog or performance numbers. That means administrators and developers must verify outcomes on their hardware and models before assuming universal gains. Prepare to stage the rollout, validate operator coverage and driver compatibility, and maintain rollback procedures in case of regressions. Historical guidance from other Microsoft AI component rollouts shows driver mismatches and opaque changelogs as the chief practical risks — the same mitigations apply here.
Overall, KB5068004 is an incremental but strategically aligned update: it improves the runtime foundation for RTX PC‑focused AI features and lets Microsoft and NVIDIA iterate faster on the Windows AI experience. The practical value for any given device will depend on driver/firmware alignment and the specific models and operators in use — validate empirically, stage carefully, and monitor for both performance gains and visual/regression artifacts.
Quick reference (actionable)
- Check Update history for the entry: “2025‑09 Nvidia TensorRT RTX version 1.8.14.0 (KB5068004).”
- Developers: confirm provider presence with ONNX Runtime get_providers and test model end‑to‑end with and without the EP.
- Administrators: pilot the update on representative Copilot+ RTX hardware, verify OEM/NVIDIA drivers, and add the component version into inventory/patch reports.
This bulletin is a small but meaningful step in the evolving Windows on‑device AI stack. Follow the verification steps above, and treat the update as a component‑level change requiring the same staged validation and driver coordination you use for other platform middleware updates.
Source: Microsoft Support KB5068004: Nvidia TensorRT RTX Execution Provider AI component update (1.8.14.0) - Microsoft Support