NVENC

Key takeaways

NVENC belongs to science; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect NVENC to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of NVENC from memory before moving on to harder problems.

Reference excerpt

NVENC (short for Nvidia Encoder) is a feature in Nvidia graphics cards that performs video encoding, offloading this compute-intensive task from the CPU to a dedicated part of the GPU. It was introduced with the Kepler-based GeForce 600 series in March 2012 (GT 610, GT620 and GT630 is Fermi Architecture). The encoder is supported in many livestreaming and recording programs, such as vMix, Wirecast, Open Broadcaster Software (OBS) and Bandicam, as well as video editing apps, such as Adobe Premiere Pro or DaVinci Resolve. It also works with Share game capture, which is included in Nvidia's GeForce Experience software. Until March 2023 consumer-targeted GeForce graphics cards officially support no more than three simultaneously encoding video streams, regardless of the count of the cards installed, but this restriction can be circumvented on Linux and Windows systems by applying an unofficial patch to the drivers. Doing so also unlocks NVIDIA Frame Buffer Capture (NVFBC), a fast desktop capture API that uses the capabilities of the GPU and its driver to accelerate capture. Professional cards support between three and unrestricted simultaneous streams per card, depending on card model and compression quality, the restrictions were loosened in 2023 allowing up to 5 simultaneously encoding video streams. From January 2024 onwards, eight simultaneous encoding video streams became the baseline. From November 2025 onwards, twelve simultaneous encoding video streams became the baseline. Nvidia chips also feature an onboard decoder, NVDEC (short for Nvidia Decoder), to offload video decoding from the CPU to a dedicated part of the GPU.

Versions NVENC has undergone several hardware revisions since its introduction with the first Kepler GPU (GK104). Graphics cards with NVENC of the second and third generations in its were not released.

First generation, Kepler GK1xx The first generation of NVENC, which is shared by all Kepler-based GPUs, supports H.264 high-profile (YUV420, I/P/B frames, CAVLC/CABAC), H.264 SVC Temporal Encode VCE, and Display Encode Mode (DEM). Nvidia's documentation states a peak encoder throughput of 8× realtime at a resolution of 1920×1080 (where the baseline "1×" equals 30 Hz). Actual throughput varies on the selected preset, user-controlled parameters and settings, and the GPU/memory clock frequencies. The published 8× rating is achievable with the NVENC high-performance preset, which sacrifices compression efficiency and quality for encoder throughput. The high-quality preset is considerably slower but produces fewer compression artifacts

Second generation, Maxwell GM107 Introduced with the first-generation Maxwell architecture, second generation NVENC adds support for the high-performance HP444 profile (YUV4:4:4, predictive lossless encoding), and increases encoder throughput up to 16× realtime, which corresponds to about 1080p @ 480 Hz with the high-performance preset. Maxwell GM108 does not have NVENC hardware encoder support.

Third generation, Maxwell GM20x Introduced with the second-generation Maxwell architecture, third generation NVENC implements the video compression algorithm High Efficiency Video Coding (a.k.a. HEVC, H.265) and also increases the H.264 encoder's throughput to cover 4K-resolution at 60 Hz (2160p60). However, it does not support B-frames for HEVC encoding (just I and P frames). The maximum NVENC HEVC coding tree unit (CU) size is 32 (the HEVC standard allows a maximum of 64), and its minimum CU size is 8. HEVC encoding also lacks Sample Adaptive Offset (SAO). Adaptive quantization, look-ahead rate control, adaptive B-frames (H.264 only) and adaptive GOP features were added with the release of Nvidia Video Codec SDK 7. These features rely on CUDA cores for hardware acceleration. SDK 7 supports two forms of adaptive quantization; Spatial AQ (H.264 and HEVC) and Temporal AQ (H.264 only). As of 2025, Nvidia's consumer-grade (GeForce) cards are restricted to 12 simultaneous encoding jobs while its lower-end professional Quadro cards are restricted to eight simultaneous encoding jobs. Its higher-end Quadro cards do not have this restriction.

Fourth generation, Pascal GP10x Fourth generation NVENC implements HEVC Main10 10-bit hardware encoding. It also doubles the encoding performance of 4K H.264 & HEVC when compared to previous generation NVENC. It supports HEVC 8K, 4:4:4 chroma subsampling, lossless encoding, and sample adaptive offset (SAO). Nvidia Video Codec SDK 8 added Pascal exclusive Weighted Prediction feature (CUDA based). Weighted prediction is not supported if the encode session is configured with B frames (H.264). There is no B-Frame support for HEVC encoding, and the maximum CU size is 32×32. The NVIDIA GT 1030 and the Mobile Quadro P500 are GP108 chips that don't support the NVENC encoder. In laptop graphics, NVIDIA MX Graphics do not include NVENC as they are based on a Maxwell-generation GM108 or a Pascal-generation GP108 chip. The GeForce MX350 is a GP107 chip whose NVENC encoder is disabled during manufacture.

Fifth generation, Volta GV10x/Turing TU117 Volta NVENC has similar performance as Pascal's NVENC. It does not offer support for HEVC B-Frames. In mobile graphics, as with most other GeForce MX-series graphics, the GeForce MX450 does not support NVENC as it is a TU117 chip whose hardware encoder is permanently disabled in its manufacture. The GeForce MX550, however, does support NVENC as its hardware encoder remains enabled at manufacturing level.

Sixth generation, Turing TU10x/TU116 Sixth generation NVENC implements HEVC 8K encoding at 30 FPS, HEVC B-frames and HEVC B-frames as reference (with support for each and middle modes) and Alpha HEVC support and provides up to 25% bitrate savings for HEVC and up to 15% bitrate savings for H.264. The initial launch of the Nvidia GeForce GTX 1650 was exempt from this generation however, as it used Volta NVENC instead of Turing. Nvidia updated the NVENC encoder of the GTX 1650 cards in 2020 to also use the Turing engine. The GTX 1650 Super uses the Turing NVENC engine as it is based on the TU116 rather than the TU117 used in the original GTX 1650.

… excerpt ends here. Continue reading the full article.

Worked examples

Example 1 — a first encounter with NVENC

Start with the simplest possible case. Write down what NVENC claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to NVENC before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about NVENC ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of NVENC

In research: NVENC appears in science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses NVENC in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: NVENC is common in secondary-school and first-year university syllabi. It links to neighbouring topics Hardware acceleration, Nvidia IP cores, Video acceleration, so understanding it makes those chapters shorter.
In everyday life: Look for NVENC outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

How to study NVENC in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what NVENC means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain NVENC out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is NVENC in simple terms?

NVENC (short for Nvidia Encoder) is a feature in Nvidia graphics cards that performs video encoding, offloading this compute-intensive task from the CPU to a dedicated part of the GPU. It was introduced with the Kepler-based GeForce 600 series in March 2012 (GT 610, GT620 and GT630 is Fermi Archite…

Why does NVENC matter?

Because it connects several science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study NVENC?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on NVENC.