KB5068004: NVIDIA TensorRT RTX EP 1.8.14.0 on Windows 11 24H2

Microsoft has quietly pushed KB5068004 — a Windows Update component that installs NVIDIA TensorRT‑RTX Execution Provider version 1.8.14.0 onto qualifying Copilot+ RTX PCs running Windows 11, version 24H2, and it arrives as an automatic, system‑level update that requires the latest 24H2 cumulative update to be present before installation.

Background / Overview​

Windows 11’s on‑device AI story now relies on a modular Execution Provider (EP) model: a system‑managed ONNX Runtime dispatches model work to vendor EPs (CPU, DirectML, NVIDIA TensorRT‑RTX, Intel OpenVINO, Qualcomm QNN, etc.) so applications can take advantage of hardware acceleration without bundling large vendor SDKs. TensorRT‑RTX is the RTX‑focused execution provider optimized for consumer GeForce/RTX GPUs (Ampere and later), designed to offer a smaller package footprint, faster model compile/load times, and better cross‑GPU caching behavior than the datacenter TensorRT EP or generic CUDA EP.
Microsoft’s KB entry for KB5068004 is intentionally terse: it states the update “includes improvements to the execution provider component,” specifies the version string (1.8.14.0), the delivery method (Windows Update, automatic), the target platform (Copilot+ PCs on Windows 11, version 24H2), and the prerequisite (latest cumulative update for 24H2). There is no published line‑by‑line changelog, performance matrix, or CVE mapping in the KB itself.

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What this component actually is​

What is the NVIDIA TensorRT‑RTX Execution Provider?​

The TensorRT‑RTX Execution Provider is an ONNX Runtime EP that leverages NVIDIA’s TensorRT for RTX runtime to JIT‑compile and run optimized inference engines on consumer RTX GPUs. Key documented characteristics include:

• Small package footprint (designed to be lighter than datacenter‑oriented TensorRT builds).
• Just‑in‑time (JIT) engine compilation to produce optimized GPU engines on the device in seconds, reducing time‑to‑first‑inference for interactive use cases.
• Runtime cache and context models that persist compiled kernels to reduce repeated JIT cost on subsequent loads.
• Targeted hardware scope: Ampere‑and‑later RTX GPUs (GeForce RTX 30xx and up); not intended for older or non‑RTX datacenter profiles.

This EP is the preferred GPU provider for consumer RTX hardware on Windows ML and ONNX Runtime in Microsoft’s Windows‑level ecosystem, designed for the latency and usability needs of local, interactive AI experiences.

How Microsoft distributes it​

KB5068004 installs the NVIDIA TensorRT‑RTX EP as a system component via Windows Update. The update will appear in Settings → Windows Update → Update history (for affected systems) and will not install unless the device already has the latest cumulative update for Windows 11, version 24H2. Microsoft’s KB provides version and install metadata but omits a technical changelog.

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Why this matters — practical benefits​

• Faster user experiences for AI features that run locally (image upscaling, background segmentation, Studio Effects, and small‑to‑mid sized model tasks). JIT compilation and runtime caching reduce time‑to‑first‑inference, which is vital for interactive UI services.
• Reduced packaging complexity for ISVs and app developers: apps can rely on the system EP rather than shipping bulky vendor runtimes in each installer, lowering install size and maintenance overhead.
• Better multi‑GPU and GPU‑switchable device behavior: TensorRT‑RTX is designed to be more portable across RTX GPUs in a single system and to reuse cached engines where helpful, which improves real‑world usability on laptops with GPU switching.
• Privacy and local processing: encouraging on‑device inference reduces the need for cloud roundtrips for latency‑sensitive or privacy‑sensitive workloads, keeping photos, webcam streams, and documents on device.

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What the KB does not tell you (and why that matters)​

Microsoft’s KB entry does not publish:

• A detailed changelog (what functions, kernels, or heuristics changed in 1.8.14.0).
• Any performance benchmarks tied to representative models or device classes.
• A mapping of security fixes or CVE identifiers (if any were addressed).

Because the KB is metadata‑only, developers and IT teams must treat specific performance or security claims as unverified until corroborated by NVIDIA release notes, ONNX Runtime logs, or controlled telemetry. The absence of a changelog increases operational risk during broad rollouts, since behavior can change subtly (numeric precision, quantization paths, operator selection) even when stability is the goal.

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Technical verification: how to confirm and measure the change​

Developers and administrators should validate the update with a short, repeatable test plan:

• Confirm the component is installed: Settings → Windows Update → Update history should show “Nvidia TensorRT RTX version 1.8.14.0 (KB5068004).”
• Verify provider registration in ONNX Runtime: use ONNX Runtime APIs (get_providers, get_provider_options) to confirm the TensorRT‑RTX EP is present and registered for sessions. Example (Python):
• session = ort.InferenceSession(model_path, providers=['NvTensorRTRTXExecutionProvider'])
• Call get_providers() to confirm provider order and presence.
• Measure time‑to‑first‑inference and steady‑state throughput before and after the update with representative models and inputs. Capture latency, GPU/CPU utilization, memory use and thermal telemetry.
• Inspect operator partitioning and fallbacks by enabling ONNX Runtime detailed logging — verify which subgraphs the EP covers and which fall back to CUDA or CPU.
• If models use quantization (FP16/INT8), compare numeric fidelity and visual outputs (for image/video models) across precisions and driver/runtime versions. Small numerical differences can produce visible artifacts in production pipelines.

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Risks and compatibility issues to watch​

• Driver/runtime mismatch: EP behavior depends on NVIDIA drivers and any associated TensorRT runtime components. Out‑of‑sync or OEM‑modified drivers can cause regressions. Align OEM/NVIDIA drivers before broad deployment.
• Operator coverage and fallback semantics: not every ONNX operator is implemented by TensorRT‑RTX; models that use unsupported ops will be partitioned and partially executed on CPU/CUDA providers, which can change latency profiles or outputs. Validate operator coverage for your model topology.
• Precision/quantization differences: TensorRT often leverages FP16/INT8—to gain throughput it may change kernel selection and numerical results. Image restoration, segmentation, and post‑processing pipelines are sensitive; validate visual fidelity.
• Opaque changelog and security traceability: enterprises requiring CVE‑mapped fixes or traceable security patches must treat KB metadata as insufficient; request coordinated advisories or vendor release notes if security posture depends on the component.
• Rollback complexity: Because this installs as an OS component via Windows Update, uninstallation or rollback may not be as straightforward as removing a user‑level package. Maintain system images and tested rollback runbooks for critical fleets.

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Recommended rollout plan (practical, step‑by‑step)​

• Inventory: add component‑level detection to endpoint inventories so you can track which devices received 1.8.14.0. Include OEM model, driver version, and GPU model in the record.
• Pilot ring (7–14 days): select representative devices covering OEM diversity, GPU models (RTX 30xx / 40xx / 50xx), and driver variants. Collect telemetry before and after the update under typical workloads.
• Align drivers/firmware: update to OEM‑recommended NVIDIA drivers before mass rollout. If problems appear after applying KB5068004, attempt driver upgrades or rollbacks as part of the troubleshooting path.
• Verification suite: run functional regression tests (Photos app features, Teams/Zoom Studio Effects, Windows Hello, third‑party inference apps) alongside synthetic model benchmarks to detect both performance regressions and visual artifacts. Save repro inputs and logs for escalation.
• Staged deployment: expand to larger rings only after pilot stability; keep telemetry and crash/dump capture active for at least 7–14 days in each stage.
• Rollback and escalation: document LCU uninstall steps, system restore procedures, and vendor escalation contacts (OEM, Microsoft, NVIDIA). If a regression affects production workflows, collect Update history entries, Event Viewer logs, WER buckets and driver versions for vendor support.

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Developer guidance (model and runtime recommendations)​

• Use explicit provider registration during testing to control priority (rather than relying on automatic provider selection). That helps detect differences in EP selection and fallbacks. Example (Python): session = ort.InferenceSession(model_path, providers=['NvTensorRTRTXExecutionProvider', 'CUDAExecutionProvider', 'CPUExecutionProvider']).
• Validate operator coverage with diagnostic models that exercise the exact opset and custom ops your production models use. Where fallbacks are expected, measure end‑to‑end latency to ensure mixed execution meets SLAs.
• Profile quantized vs. floating‑point paths: if you plan to rely on INT8 or FP16 kernels, run side‑by‑side visual/metric checks to ensure perceptual fidelity remains within acceptable bounds.
• Cache EP contexts where appropriate: using the EP context model or runtime cache can dramatically reduce session creation times for models with substantial compile time; evaluate disk use and cache eviction behavior on low‑storage devices.
• Add device‑level tests to CI where possible: incorporate long‑run stability tests and a small device farm (or cloud GPU profiles) to detect regressions introduced by runtime or driver updates early in the pipeline.

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Cross‑verification of claims (what’s independently confirmed)​

• Microsoft’s KB confirms the update exists, its version (1.8.14.0), its target scope (Copilot+ PCs on Windows 11, version 24H2), and that it installs automatically via Windows Update, subject to having the latest 24H2 cumulative update.
• ONNX Runtime documentation confirms the TensorRT‑RTX EP’s purpose, hardware scope (Ampere+ RTX GPUs), its features (JIT compilation, runtime cache, smaller package footprint), and the API surface for registration and options. This provides technical context for how the Windows component is used at runtime.
• NVIDIA’s TensorRT for RTX documentation further documents how the runtime behaves and the intended integration points for RTX client usage (porting notes, supported data types, and constraints). Use these vendor docs when investigating model‑level regressions or seeking low‑level runtime behavior.

Where Microsoft’s KB is silent (detailed changelog, CVE mapping, per‑model performance deltas), these upstream documents plus controlled measurements form the verification path; the KB alone is insufficient to prove exact performance or security fixes.

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Quick checklist (for IT admins and power users)​

• Confirm device is Copilot+ and running Windows 11, version 24H2 with the latest cumulative update.
• Check Update history after Windows Update applies the component for the “Nvidia TensorRT RTX version 1.8.14.0 (KB5068004)” entry.
• Verify ONNX Runtime providers (get_providers) and enable detailed logs during test runs to observe provider selection and operator fallback.
• Pilot the update on representative hardware, align drivers, collect telemetry, and stage rollout in waves.

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Final assessment — strengths, caveats, and editorial verdict​

KB5068004 is a targeted, low‑surface‑area update that feeds Microsoft’s strategy of modular, per‑silicon runtime improvements for Windows ML. For end users on qualifying RTX Copilot+ PCs, the update should broadly improve perceived responsiveness for local AI features thanks to TensorRT‑RTX’s JIT engine generation and runtime caching. For developers, the EP reduces the friction of shipping optimized inference on RTX hardware because the system runtime handles provider distribution and updates.
However, the lack of a public changelog in Microsoft’s KB elevates verification responsibility: IT teams must pilot, measure, and ensure driver alignment before broad deployment. The most common regressions in previous component updates come from driver/firmware mismatches or unexpected operator fallbacks; those are the practical risks to mitigate. Enterprises that require traceable security remediation should request vendor advisories if the component impacts trusted software or threat surfaces.
In short: KB5068004 is a sensible, forward‑looking platform update that can materially help Windows’ on‑device AI experiences on RTX PCs — but its real‑world effects are model‑ and driver‑dependent, and prudent staging, measurement, and rollback planning remains essential.

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Concluding action items for busy readers:

• Check Update history now and confirm presence of “Nvidia TensorRT RTX version 1.8.14.0 (KB5068004)” on qualifying machines.
• For developers: add ONNX Runtime provider checks and short model latency/fidelity tests into your pre‑release validation matrix.
• For admins: pilot on a small ring (7–14 days), align NVIDIA/OEM drivers, gather telemetry, and stage rollout.

Treat specific performance or security claims as provisional until you can reproduce them with your models and devices or verify vendor release notes that map to 1.8.14.0.

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Source: Microsoft Support KB5068004: Nvidia TensorRT-RTX Execution Provider Update (1.8.14.0) - Microsoft Support