From its first machine learning models in 2017 to running hundreds of millions of inferences daily on NVIDIA GPUs — a decade of going deeper on the world’s best AI infrastructure.
Zefr’s content understanding engine has always run on NVIDIA GPU infrastructure, from early NVIDIA Tesla V100s to today’s NVIDIA RTX Pro 6000 Blackwell Server Edition and NVIDIA DGX Station GB300. More recently, Zefr has begun leveraging NVIDIA’s open-weight model ecosystem, NVIDIA Nemotron, as well.
The Origin: The Problem That Required a New Kind of Infrastructure
Zefr has always been at the forefront of understanding content for brands. When customers first asked Zefr to make sense of YouTube at scale, it was immediately clear that keywords weren’t going to cut it. User generated content is too vast, too nuanced, and too multilingual for hard-coded rules, and that meant probabilistic systems, which in turn meant serious computation.
Zefr’s first models used Gradient Boosting Machines (GBMs), trained on human-labeled data produced by Zefr employees. The results were a meaningful step-change over keyword matching. But more importantly, they surfaced a foundational insight: the quality of content understanding would always be bound by the quality of hardware and algorithms working together.
Zefr is dedicated to understanding user generated content at scale, which makes investment in machine learning and AI essential, not because it’s the cool thing to do, but because it is the right tool for the job.
The Infrastructure Journey: Each NVIDIA Advancement Unlocked a New Level of Understanding
Since those first GBMs were trained on CPUs, Zefr has continuously leveled up its algorithms, data, hardware, software, and people with NVIDIA infrastructure at the center of each major subsequent step forward.
2017: Gradient Boosting Machines on CPU
Zefr’s first probabilistic models, trained on human labels, proved that machine learning could outperform rules-based systems. In the beginning specialized hardware was not required, as these models could be trained and served on CPUs.
2018: Transformers: “Attention is all you need”
Zefr migrated to transformer-based neural networks (the architecture now underpinning every frontier AI model) delivering richer content representations and substantially better understanding across languages and formats. This shift in software also required a shift in hardware.
2019: NVIDIA GPUs: training and inference at scale
Shifting from CPU to NVIDIA GPU hardware delivered the single biggest performance unlock in Zefr’s history — massive gains at both training and inference time. Better GPUs enabled larger, more capable models. That compounding relationship between hardware and model quality has defined Zefr’s trajectory ever since.
2022: GenAI Labeling with GPT‑4
Following the release of ChatGPT, Zefr replaced significant portions of its manual labeling workflow with synthetic labels generated by frontier models, like GPT‑4, dramatically accelerating the training data pipeline and improving label quality at scale.
Today: Open-Weight Models on NVIDIA Infrastructure at Global Scale
Today, NVIDIA accelerates hundreds of millions of Zefr inferences daily across text, image, and video using custom transformer models across multiple modalities.. On the software side, Zefr runs NVIDIA Triton Inference Server and NVIDIA TensorRT LLM for large‑scale visual understanding and inferencing, with CV-CUDA and nvImageCodec handling high‑throughput pre/post‑processing on GPUs. Zefr is also evaluating open‑weight models such as NVIDIA Nemotron to achieve throughput and cost levels that closed APIs cannot match. Today’s stack runs primarily on NVIDIA RTX Pro 6000 Blackwell Server Edition, while Zefr is additionally experimenting with NVIDIA DGX Station powered by GB300 GPUs for inference.
The Scale Reality: Why NVIDIA Infrastructure is the Only Answer at Zefr’s Scale
Zefr’s customers demand analysis of hundreds of millions of social media posts per day, across dozens of countries, with nuanced understanding of text, image, and video simultaneously. Closed-weight APIs from any provider, however capable, cannot meet that demand at competitive economics.
That reality has deepened Zefr’s relationship with NVIDIA year over year and the returns compound: more powerful hardware unlocks more capable models, more capable models produce more accurate content understanding, and more accurate understanding delivers stronger outcomes for the brands Zefr serves.
Zefr also continues to use closed-weight models from providers including Gemini, GPT, and Claude where appropriate. But at the scale of global social media monitoring, NVIDIA’s Nemotron open-weight ecosystem (running on NVIDIA’s own GPU hardware) is what makes the economics work.
What’s Next: NVIDIA Infrastructure as the Foundation for Zefr’s Agentic Future
Zefr’s journey doesn’t stop at classification. Looking ahead, Zefr’s data will power custom reporting and actionable insights through agentic workloads — systems that don’t just label content, but reason about it, surface patterns, and drive decisions for clients in real time.
That agentic layer will be built on the same foundation that has powered Zefr’s growth: NVIDIA GPUs, NVIDIA Nemotron open-weight model ecosystem, and a deepening collaboration with the company that continues to define what’s possible in accelerated computing. For Zefr, NVIDIA infrastructure isn’t just enabling the product — it’s the reason Zefr can lead.