AI Video Processing At Scale

With Atlas and Soluna

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At Atlas Cloud, we recently announced our partnership with Soluna, a leader in sustainable data center solutions (check it out here). 

Through this collaboration, we now have access to 64 Nvidia H100 GPUs with room for expansion. This provides us with the scalability and efficiency needed to power advanced AI-driven video processing workloads. 

By combining our expertise in AI optimization with Soluna’s green energy infrastructure, we’re setting a new standard for sustainable, high-performance computing.

But this unique partnership raises several questions–Why does scaling AI video processing even matter? What’s driving the need for this kind of innovation?

What is AI Video Processing?

AI Video Processing refers to the use of advanced algorithms—often powered by machine learning and deep neural networks—to analyze and interpret existing video content. Tasks can include object detection, facial recognition, motion tracking, and video summarization. By sifting through massive amounts of footage in near real-time, AI video processing streamlines workflows, reduces manual labor, and produces insights that can inform data-driven decision-making. Whether it’s automating surveillance reviews or extracting highlights from hours of raw footage, AI video processing elevates efficiency and accuracy in handling large-scale visual data

What is AI Video Generation?

AI Video Generation, on the other hand goes beyond processing existing footage—it involves creating entirely new video content from scratch or transforming raw inputs (like text, images, or audio) into dynamic visual narratives. Using generative AI models, systems can produce hyper-realistic simulations, synthesize human avatars, upscale low-resolution footage, or even generate personalized video campaigns at scale. Unlike traditional methods, AI video generation automates labor-intensive tasks, enabling rapid iteration and customization while maintaining high fidelity.

Why Does AI Video Processing Matter?

When leveraged at scale, AI video processing has the potential to reshape industries. This includes entertainment, obviously, but as NVIDIA CEO Jensen Huang highlighted at CES 2025, it also encompasses autonomous driving and robotics systems, surveillance and security, healthcare, and more.

It enables AI to analyze massive datasets, simulate real-world scenarios, create synthetic environments, and create predictive models with unprecedented visual accuracy.

Jensen Huang, CES 2025 Keynote Address

Use Cases Across AI Video Processing and Generation

AI video processing comes in many forms—object detection, facial recognition, motion tracking, and video summarization, to name a few. Each type requires processing massive volumes of data to extract meaningful insights, but the sheer computational complexity of AI algorithms has traditionally made long-form video processing a tough nut to crack. Some use examples include:

Media & Entertainment

• Synthetic Content Creation: Generate background scenes, CGI characters, or entire storyboards from text prompts, drastically reducing production costs and timelines.

• Localization: Automatically dub videos into multiple languages using AI-synthesized voices and lip-synced avatars, accelerating global distribution.

Retail & Marketing

• Personalized Ads: Produce thousands of custom video ads for different demographics using dynamic AI templates, optimizing engagement and conversion.

• Virtual Try-Ons: Offer real-time videos of customers “wearing” products virtually, enhancing the online shopping experience.

Corporate Training

• Simulated Scenarios: Create immersive videos for high-risk roles—like emergency responders or manufacturing engineers—using AI-generated environments for safer, more effective training.

• On-Demand Tutorials: Automatically convert text-based guides into interactive video tutorials, making knowledge transfer more engaging and efficient.

Smart Cities & Manufacturing

• Digital Twins: Generate real-time video simulations of factory floors or urban infrastructure to predict failures, improve resource allocation, and optimize operations.

• AI-Powered QA: Streamline quality control by automatically inspecting manufacturing processes via AI-generated visual reports, reducing errors and downtime.

Beyond These Sectors

• Autonomous Vehicles: AI-driven video simulations can train self-driving cars by generating billions of miles of virtual driving scenarios—accelerating development cycles without increasing physical risk.

• Healthcare: Surgical robots leverage AI video processing to analyze medical imaging in real time, supporting precise, minimally invasive interventions.

• Surveillance and Security: Advanced algorithms sift through massive volumes of footage, identifying patterns and anomalies for proactive threat detection.

What is Scaled AI Video Processing?

These standard AI video use case typically involve processing a few hours of footage or generating a limited set of video assets. In many instances, these projects serve as proofs of concept rather than fully industrialized solutions, and they usually run on off-the-shelf hardware that can meet moderate infrastructure demands.  

By contrast, scaled use cases involve high-volume data ingestion, real-time processing, and the creation of extensive video libraries. They often require complex workflows, multiple stakeholders, and sometimes global reach.  

As a result, enterprises tackling these larger projects need specialized hardware—such as GPU clusters, edge servers, or large-scale computing environments—as well as sophisticated software orchestration to manage massive computational loads efficiently. (That’s exactly why we entered into this partnership with Soluna!)

Benefits of Video Processing and Generation and Scale

When you scale AI video processing, the first benefit is deeper insights and faster decisions. With larger datasets, patterns and trends become more apparent, enabling leaders to act with greater speed and confidence.  

Second, scaling unlocks expanded monetization opportunities, whether you’re personalizing video ads for niche audiences or offering on-demand content for subscription-based models.  

Third, operational efficiency gets a significant boost: repetitive tasks can be automated on a vast scale, freeing teams to focus on high-value, strategic activities.  

Finally, developing large-scale AI video capabilities provides a vital competitive edge. If you’re the first to deliver real-time product try-ons or next-level predictive maintenance, you set the pace for your industry, rather than chasing it.

Effectively scaling AI video processing means:

• Handling Larger Datasets: Today’s videos are longer and more complex, requiring models that can process hours of footage. Larger datasets also provide more opportunities for AI to learn patterns and relationships, enabling deeper insights
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• Improving Training Accuracy: With larger datasets, AI algorithms need to be trained to minimize hallucinations, identify subtle patterns, and improve predictions, making them more reliable for tasks like object detection and video summarization.
  

• Optimizing Inference: Once models are trained on these rich datasets, the real challenge lies in inference—putting that training to work quickly and effectively. Inference involves analyzing frames, recognizing patterns, and delivering insights in near real-time.
  Optimizing inference workloads reduces bottlenecks, allowing AI to handle massive workloads without sacrificing accuracy or performance, even in processing long-form video.

A visual representation of inference prior to AI video processing

Real-World Impact

Despite the clear advantages, enterprises face numerous hurdles when scaling AI video. Infrastructure and compute power are top concerns, as traditional setups can quickly become overwhelmed by massive video streams.  

You may need GPU clusters, cloud orchestration, and possibly edge-based computing to meet real-time processing demands. Data management and governance also become significantly more complicated at scale: storing, securing, and regulating colossal volumes of video is no small feat, especially under strict data privacy laws.  

Additionally, model training and optimization grow more complex as your data volume increases, necessitating frequent retraining and fine-tuning to maintain accuracy and cost-effectiveness. Quality assurance, too, can be tricky—errors in AI-generated content can escalate rapidly if left unchecked.  

Finally, cost management looms large in enterprise-scale rollouts, as running high-performance GPU clusters and allocating large-scale cloud storage can strain budgets unless carefully planned.

At Atlas Cloud, we’re at the cutting edge of AI video processing. With scalable infrastructure powered by Nvidia H100 GPUs, we deliver energy-efficient solutions for video processing and analysis. Video is becoming more deeply engrained into AI training and companies will need more computational power to match. We're future-proofing our infrastructure for exactly that reason.  

Whether it's optimizing inference workloads, improving training quality, or providing industry-leading GPUs at cost and scale, we're pushing video processing toward democratized viability. Ready to Scale Up? Our scalable AI infrastructure is ready to support your vision.