Beamr Imaging Ltd. will demonstrate its ML‑safe video compression technology at NVIDIA’s GTC 2026 conference in San Jose, scheduled for March 16‑19, 2026. The presentation marks the company’s first on‑stage showcase of the technology.
The demonstration will feature GPU‑accelerated workflows that reduce file sizes by up to 50 % while preserving machine‑learning model accuracy for autonomous vehicles, robotics and smart‑space applications. Beamr’s patented Content‑Adaptive Bitrate (CABR) technology has been benchmarked to maintain accuracy across multiple precision and quality metrics, addressing the trade‑off between storage efficiency and AI performance.
Beamr will partner with VAST Data for a joint showcase, highlighting a pipeline that unifies high‑throughput data access, database services and real‑time orchestration to accelerate AI pipelines built on massive video datasets.
The event represents a major operational milestone for Beamr, which previously showcased its technology at NVIDIA GTC Paris in June 2025 and in January 2026. The demonstration signals Beamr’s intent to capture growing demand for efficient video processing in autonomous‑driving and AI‑training markets.
Beamr’s financial context shows flat revenue of $3.09 million for the full year ended December 31, 2025, and a widened net loss of $6.02 million, compared to a $3.35 million loss in 2024. The company’s market capitalization was $26.85 million as of March 5, 2026, reflecting continued investment in its technology.
CEO Sharon Carmel emphasized the significance of the demonstration: “We are showcasing that organizations can achieve the full benefits of validated, ML‑safe video data compression at scale and with confidence,” she said. “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.”
By unveiling its CABR technology at GTC 2026, Beamr positions itself as a potential partner for companies seeking to reduce terabyte‑scale video data costs without compromising model fidelity, reinforcing its strategic focus on physical AI applications.
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