Beamr to Demonstrate ML-Safe Video Data Compression for Physical AI at GTC 2026

Beamr (NASDAQ: BMR) will demonstrate ML-safe video data compression for physical AI at GTC 2026, showcasing GPU-accelerated workflows that the company says reduce file sizes by up to 50% while preserving ML model accuracy.

Rhea-AI Summary

Beamr (NASDAQ: BMR) will demonstrate ML-safe video data compression for physical AI at GTC 2026, showcasing GPU-accelerated workflows that the company says reduce file sizes by up to 50% while preserving ML model accuracy.

The joint demo with VAST Data highlights ingestion-to-training pipelines for AV, robotics, and video-language model workflows on petabyte-scale datasets.

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Details

News Market Reaction – BMR

On Mar 12, the day this news came out, BMR closed 1.76% above the previous close.

Data tracked by StockTitan Argus for the Mar 12 session.

Key Figures

File size reduction: up to 50% Data scale: petabyte-scale Data under management: tens or hundreds of petabytes +2 more

File size reduction

up to 50%

GPU-accelerated AV workflows demo at GTC 2026

Data scale

petabyte-scale

Training and validation video volumes for physical AI

Data under management

tens or hundreds of petabytes

Real-world and synthetic video data for AI teams

Benchmark period

past year

CABR ML-safe compression benchmark tests window

Booth number

3109

Beamr booth location at GTC 2026

Previous AI Reports

5 past events · Latest: Feb 26

Same Type 5 events

1. Feb 26

   AI strategy letter

   24h Move

   -9.8%

   CEO letter on AI video progress, CABR validation, and 2026 execution focus.

2. Oct 15

   AI conference demos

   24h Move

   -5.0%

   Showcasing NVIDIA GPU video workflows and CABR with up to 50% size cuts.

3. Feb 27

   GTC AI presentation

   24h Move

   -5.3%

   CEO talk at NVIDIA GTC on AI-driven video compression and workflows.

4. Jan 27

   AI webinar event

   24h Move

   -15.2%

   Webinar with Oracle and NVIDIA on AI video pipelines and CABR technology.

5. Dec 2

   AI vision interview

   24h Move

   -3.4%

   CEO interview discussing GPU-accelerated video optimization and AI initiatives.

24h Move is the share-price change in the day after each event; other market factors may also have contributed.

Key Terms

ml-safe, autonomous vehicles, gpu-accelerated, content-adaptive bitrate (cabr), +2 more

6 terms

ml-safe technical

"it will demonstrate a validated ML-safe video data compression for physical AI applications"

ml-safe means data, content or a process is suitable for use with machine learning systems because it avoids privacy risks, sensitive information, and formats that would break model training. Investors should care because ml-safe materials can be used to build or validate AI tools without legal or ethical roadblocks—like giving a chef pre-washed, pre-cut ingredients that are ready to cook rather than raw, uncertain supplies that could cause delays or liability.

autonomous vehicles technical

"GPU-accelerated workflows for autonomous vehicles (AV) that reduce file sizes"

Vehicles that use on-board sensors, cameras and software to navigate and drive without a human actively controlling them; think of them as robotic chauffeurs that can perceive roads, make decisions and follow traffic rules. For investors, they matter because they can reshape transportation costs, create new revenue streams (rides, logistics, software) and change regulatory and liability risks, so their adoption affects manufacturers, tech suppliers, insurers and transportation demand.

gpu-accelerated technical

"The company will showcase GPU-accelerated workflows for autonomous vehicles (AV)"

Using graphics processing units (GPUs) to run compute‑heavy tasks much faster than standard central processors by handling many small operations at once. For investors, GPU‑acceleration can shorten time to insight and lower costs for advanced workloads like artificial intelligence, large‑scale data analysis, or simulations, potentially boosting product performance, enabling new services, and improving competitive position—think switching from a bicycle to a high‑speed train for moving large data loads.

content-adaptive bitrate (cabr) technical

"Beamr's patented Content-Adaptive Bitrate (CABR) technology addresses these challenges directly."

Content-adaptive bitrate (CABR) is a video-delivery method that adjusts streaming quality in real time based on both the viewer’s network conditions and the complexity of what's on-screen, so simple scenes use less data while complex scenes get more detail. For investors, CABR matters because it can improve user experience and reduce bandwidth costs, which affects subscriber retention, platform scalability, and the economics of streaming services.

video-language models (vlms) technical

"where video-language models (VLMs) enable scalable semantic curation"

Video-language models (VLMs) are artificial intelligence systems that watch video and convert what they see into meaningful text or use text to describe, search, or generate video content — like a translator that turns moving images into searchable words and back again. Investors care because VLMs can automate content tagging, improve ad targeting, speed regulatory oversight, and create new product lines, which can lower costs, boost revenue, or change competitive dynamics in media and tech businesses.

foundation models technical

"preserve ML accuracy in various environments, including world foundation models."

Foundation models are very large artificial intelligence systems trained on broad, general data so they can be quickly adapted to many different tasks, like a powerful, general-purpose engine or a Swiss Army knife for software. They matter to investors because they can lower costs and speed innovation across industries, create new products or revenue streams, and change competitive dynamics, while also introducing operational and regulatory risks that can affect a company’s financial outlook.

AI-generated analysis. How Rhea-AI works. Not financial advice.

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Herzliya, Israel, March 12, 2026 (GLOBE NEWSWIRE) -- Beamr Imaging Ltd. (NASDAQ: BMR), a leader in video optimization technology and solutions, today announced it will demonstrate a validated ML-safe video data compression for physical AI applications at GTC 2026. The company will showcase GPU-accelerated workflows for autonomous vehicles (AV) that reduce file sizes by up to 50% while preserving machine learning (ML) model accuracy.

Physical AI applications, including AVs, robotics, and smart spaces, use petabyte-scale volumes of video data for training and validation, and volumes continue to grow rapidly. Teams managing tens or hundreds of petabytes of real-world and synthetic video data require reliable compression workflows that preserve ML accuracy in various environments, including world foundation models. However, conventional compression methods failed to address the tradeoff between storage and networking efficiency and ML model integrity, directly impacting the performance of AI systems.

Beamr's patented Content-Adaptive Bitrate (CABR) technology addresses these challenges directly. A series of benchmark tests performed over the past year validated that CABR delivers ML-safe video data compression with up to 50% file size reduction. Testing using industry-standard datasets confirmed model accuracy is preserved across multiple precision and quality metrics.

At GTC, Beamr will debut a joint demonstration with VAST Data, the AI Operating System company, enabling video RAG/VSS solutions that leverage GPU-accelerated compression to reduce file sizes while delivering ML-safe results during data ingestion. Running on the VAST AI Operating System, the pipeline unifies high-throughput data access on scalable data infrastructure, database services, and real-time processing and orchestration to accelerate AI pipelines built on massive video datasets. The collaboration targets organizations managing massive video datasets in AV, media and entertainment, and other industries, where video-language models (VLMs) enable scalable semantic curation, allowing to efficiently search, filter, and prioritize training data across petabyte-scale datasets.

"We are showcasing that organizations can achieve the full benefits of validated, ML-safe video data compression at scale and with confidence," said Beamr CEO, Sharon Carmel. "Beamr engagement with leading companies and our own rigorous benchmark testing, validates the GPU-accelerated approach across the data pipeline, from ingestion through training and validation, for both real-world and synthetic data."

Companies managing petabyte-scale video data are invited to meet Beamr's video data experts at GTC 2026 to discuss ML-safe compression workflows. To schedule a meeting at Beamr’s booth, 3109, please schedule a meeting.

About Beamr

Beamr (Nasdaq: BMR) is a world leader in content-adaptive video compression, trusted by top media companies including Netflix and Paramount. Beamr’s perceptual optimization technology (CABR) is backed by 53 patents and a winner of Emmy® Award for Technology and Engineering. The innovative technology reduces video file sizes by up to 50% while preserving quality and enabling AI-powered enhancements.

Beamr powers efficient video workflows across high-growth markets, such as media and entertainment, user-generated content, machine learning, and autonomous vehicles. Its flexible deployment options include on-premises, private or public cloud, with convenient availability for Amazon Web Services (AWS) and Oracle Cloud Infrastructure (OCI) customers.

For more details, please visit www.beamr.com or the investors’ website www.investors.beamr.com

Forward-Looking Statements

This press release contains “forward-looking statements” that are subject to substantial risks and uncertainties. Forward-looking statements in this communication may include, among other things, statements about Beamr’s strategic and business plans, technology, relationships, objectives and expectations for its business, the impact of trends on and interest in its business, intellectual property or product and its future results, operations and financial performance and condition. All statements, other than statements of historical fact, contained in this press release are forward-looking statements. Forward-looking statements contained in this press release may be identified by the use of words such as “anticipate,” “believe,” “contemplate,” “could,” “estimate,” “expect,” “intend,” “seek,” “may,” “might,” “plan,” “potential,” “predict,” “project,” “target,” “aim,” “should,” “will” “would,” or the negative of these words or other similar expressions, although not all forward-looking statements contain these words. Forward-looking statements are based on the Company’s current expectations and are subject to inherent uncertainties, risks and assumptions that are difficult to predict. Further, certain forward-looking statements are based on assumptions as to future events that may not prove to be accurate. For a more detailed description of the risks and uncertainties affecting the Company, reference is made to the Company’s reports filed from time to time with the Securities and Exchange Commission (“SEC”), including, but not limited to, the risks detailed in the Company’s annual report filed with the SEC on February 26, 2026 and in subsequent filings with the SEC. Forward-looking statements contained in this announcement are made as of the date hereof and the Company undertakes no duty to update such information except as required under applicable law.

Investor Contact:
investorrelations@beamr.com

FAQ

AI-generated questions and answers. How Rhea-AI works. Not financial advice.

What will Beamr (BMR) demonstrate at GTC 2026 on March 12, 2026?

Beamr will demonstrate GPU-accelerated, ML-safe video compression that reduces file sizes by up to 50%. According to the company, the demo shows an end-to-end pipeline for ingestion, processing, and ML validation for physical AI workloads.

How does Beamr's claim of "up to 50%" file size reduction affect BMR data costs?

Up to a 50% reduction could materially lower storage and bandwidth needs for large video datasets. According to the company, validated benchmarks show size cuts while preserving ML model accuracy across industry-standard datasets.

What is the role of VAST Data in Beamr's GTC 2026 demonstration for BMR?

VAST Data supplies the AI Operating System that runs the GPU-accelerated compression pipeline. According to the company, the joint demo integrates high-throughput data access, orchestration, and database services for petabyte workflows.

Does Beamr (BMR) say ML model accuracy is preserved after compression?

Yes—Beamr states that testing confirmed ML accuracy is preserved across multiple precision and quality metrics. According to the company, benchmarks used industry-standard datasets for real-world and synthetic data validation.

Which industries does Beamr target with its ML-safe compression showcased at GTC 2026?

Beamr targets autonomous vehicles, robotics, media and entertainment, and smart spaces managing petabyte-scale video. According to the company, the solution supports video-language models for scalable semantic curation.

Can investors meet Beamr (BMR) team at GTC 2026 and where is the booth?

Yes—Beamr invites companies managing petabyte video data to meet its experts at GTC 2026. According to the company, Beamr's booth is 3109 and meetings can be scheduled with its video data team.