The missing piece: ONNX at the heart of FFmpeg

It's not news that FFmpeg, the Swiss Army knife of video pipelines, supports Deep Neural Network-based filters. But the announcement that an AMD engineer has officially contributed an ONNX Runtime backend to the library's DNN filter marks a step forward for anyone working with video processing and artificial intelligence in environments that want – or need – to keep data under local control.

FFmpeg's DNN filter can already run AI models for upscaling, object detection, background segmentation, and more, directly inside the encoding or transcoding pipeline. So far, however, runtime options were limited, often tied to specific frameworks. With the arrival of ONNX Runtime – a cross-platform runtime optimized for heterogeneous hardware – the range of usable accelerators expands significantly: GPUs, NPUs, and potentially any silicon with the appropriate drivers and ONNX ecosystem.

Why AMD's contribution shifts the balance

The move is more than symbolic. AMD, which is investing heavily in on-premise and edge inference solutions, delivers to the community a tool that unlocks two immediate benefits. First, the ability to run pre-trained models on various platforms without replacing the entire video pipeline: seamless integration into a tool that already handles the bulk of multimedia work. Second, direct access to NPUs and GPUs without proprietary software stacks or cloud service calls.

For those managing video streams in surveillance, manufacturing, healthcare, or media & entertainment, this means processing sensitive data locally, with ultra-low latencies and full compliance with sovereignty requirements (think GDPR or industry regulations). It's no coincidence that local AI deployment is becoming a requirement rather than an option: integrations like this lower technical friction and speed adoption.

ONNX: the glue of hybrid inference

ONNX (Open Neural Network Exchange) is now a de-facto standard for model interoperability. Having an ONNX Runtime backend in FFmpeg means you can take a model exported from PyTorch, TensorFlow, or other frameworks, convert it to ONNX, and run it directly on the available hardware, taking advantage of the optimizations ONNX Runtime offers for different architectures. Additionally, the runtime supports quantization and other techniques to reduce computational footprint – a critical aspect for resource-constrained edge devices.

AMD is not alone in this race, but the choice to contribute upstream (i.e., directly to FFmpeg's main codebase) signals an intent to embed the technology in the open infrastructure fabric. This avoids forks and ensures long-term maintenance, reducing risk for companies building on top of it.

Beyond the cloud: inference as a local infrastructure service

Performance on real-world workloads remains to be seen – the source provides no benchmarks – but the trend is clear: AI inference is moving toward the data, not the other way. Real-time 4K video, complex scenes, industrial settings where bandwidth is costly or absent: ideal scenarios for keeping AI processing within the local perimeter.

For AI-RADAR readers evaluating on-premise architectures, such integrations are pieces that simplify total cost of ownership. Fewer cloud services to pay, less network latency, more control. And even if this isn't a generative LLM, the principle is the same: bring AI as close to the source as possible, leveraging dedicated hardware.

Ultimately, AMD's contribution to FFmpeg is not a revolution, but a tangible enabler. Small openings in the infrastructure that, together, pave the way for increasingly local, open, and pervasive AI.