Why do traditional codecs like H.264 still dominate if AI is everywhere?

Traditional codecs like H.264, H.265, and AV1 have hardware acceleration available on almost all devices, making them cheap to run, while neural codecs tend to be fairly large and power-hungry. Additionally, neural codecs fail across platforms: small numerical differences between an encoder on one device and a decoder on another can break the entropy model and collapse the entire video stream.

What is the main technical problem preventing neural codecs from working across devices?

When you encode a video on one platform's NPU (like Apple's) and decode it on another manufacturer's NPU (like Intel's), small numerical differences cause the encoder and decoder to disagree about the entropy model, which breaks entropy decoding and fails the entire stream. Simply quantizing the model and switching to integer math does not reliably fix this problem.

Video Generationr/MachineLearningJul 30, 2026Jul 31, 2026, 06:00 JST2 min read

Neural video codec aims to work across device platforms

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Key takeaway

Despite neural networks replacing hand-engineered systems in many AI applications, traditional video codecs like H.264 and H.265 still dominate in the real world.
A new research effort called MLVC is addressing two key barriers: the high compute and power demands of neural codecs compared to hardware-accelerated traditional codecs, and the critical cross-platform compatibility problem where small numerical differences between encoders and decoders on different device platforms (such as Apple and Intel NPUs) cause entropy decoding to fail and collapse the entire video stream.

3 Key Points

What happened
A research effort called MLVC (Multi-platform Learned Video Codec) is tackling why AI-based video codecs have not replaced traditional hand-engineered systems like H.264, H.265, and AV1 in real-world use, despite neural networks replacing hand-engineered systems in many other domains over the past 14 years.
Why it matters
Neural codecs face two major obstacles: they are computationally expensive and power-hungry compared to traditional codecs that have hardware acceleration everywhere, and they break across platforms when small numerical differences between an encoder on one device (like an Apple NPU) and a decoder on another (like an Intel NPU) cause the entropy model to disagree, collapsing the entire video stream. Cross-platform compatibility is a critical blocker for deployment.
What to watch
The research explores whether NPUs (neural processing units) can be a practical fit for neural codecs, and whether fully specified fixed-point arithmetic or other techniques can solve the numerical compatibility problem that currently prevents a single encoded stream from working reliably across different manufacturers' hardware.

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Context & Analysis

The gap between AI's dominance in many domains and the continued use of hand-engineered video codecs reveals a practical engineering problem that pure algorithmic innovation alone cannot solve. While AlexNet in 2012 marked the beginning of neural networks replacing hand-engineered systems across computer vision and other fields, video codecs have remained a notable exception. The body identifies two distinct barriers: one is economic and infrastructural (hardware acceleration for traditional codecs is ubiquitous, while neural codecs require significant compute), but the more fundamental obstacle is technical—the cross-platform compatibility issue. When encoding and decoding occur on different manufacturers' neural processing units, numerical precision differences in how each platform computes the entropy model cause the stream to fail entirely. The research suggests that NPUs may offer a path forward in terms of efficiency, but only if the numerical determinism problem can be solved, potentially through fully specified fixed-point arithmetic or similar techniques that guarantee bit-exact reproducibility across different hardware implementations.

FAQ

Why do traditional codecs like H.264 still dominate if AI is everywhere?: Traditional codecs like H.264, H.265, and AV1 have hardware acceleration available on almost all devices, making them cheap to run, while neural codecs tend to be fairly large and power-hungry. Additionally, neural codecs fail across platforms: small numerical differences between an encoder on one device and a decoder on another can break the entropy model and collapse the entire video stream.
What is the main technical problem preventing neural codecs from working across devices?: When you encode a video on one platform's NPU (like Apple's) and decode it on another manufacturer's NPU (like Intel's), small numerical differences cause the encoder and decoder to disagree about the entropy model, which breaks entropy decoding and fails the entire stream. Simply quantizing the model and switching to integer math does not reliably fix this problem.

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