Speedata Raises $44M Series B to Challenge Nvidia in Data Analytics

Speedata Raises $44M to Accelerate Big Data Analytics with New APU Chip

Tel Aviv-based startup Speedata has secured $44 million in Series B funding, bringing its total raised to $114 million. The company is developing an analytics processing unit (APU) designed to surpass graphics processing units (GPUs) in big data analytics and AI workloads.

Purpose-Built for Data Processing

The funding round was led by existing investors, including Walden Catalyst Ventures, 83North, Koch Disruptive Technologies, Pitango First, and Viola Ventures. Strategic investors such as Lip-Bu Tan, CEO of Cadence Design Systems and Managing Partner at Walden Catalyst Ventures, and Eyal Waldman, Co-Founder and former CEO of Mellanox Technologies, also participated.

Speedata's APU architecture addresses specific bottlenecks in data analytics at the computing level. Unlike GPUs, originally designed for graphics and later adapted for AI and data tasks, the APU is purpose-built for data processing.

“For decades, data analytics have relied on standard processing units, and more recently, companies like Nvidia have invested in pushing GPUs for analytics workloads,” said Adi Gelvan, CEO of Speedata. “But these are either general-purpose processors or processors designed for other workloads, not chips built from the ground up for data analytics. Our APU is purpose-built for data processing and a single APU can replace racks of servers, delivering dramatically better performance.”

From Research to Reality

Founded in 2019, Speedata's team includes pioneers in Coarse-Grained Reconfigurable Architecture (CGRA) technology. They recognized the limitations of using general-purpose processors for increasingly complex data analytics workloads. Their solution: a single, dedicated processor designed for faster processing and reduced energy consumption.

Currently, the APU targets Apache Spark workloads, with plans to support all major data analytics platforms.

“We aim at becoming the standard processor for data processing—just as GPUs became the default for AI training, we want APUs to be the default for data analytics across every database and analytics platform,” Gelvan stated.

Real-World Results and Future Plans

Speedata reports that several large companies are currently testing its APU. The company plans to officially launch the product at the Databricks Data & AI Summit in June, where it will publicly showcase the APU for the first time.

In one case study, Speedata's APU completed a pharmaceutical workload in 19 minutes, compared to 90 hours with a non-specialized processing unit—a 280x speed improvement.

Since its last fundraising round, Speedata has finalized the design and manufacturing of its first APU. The company is now moving from testing on field-programmable gate arrays (FPGAs) to launching working hardware and scaling its go-to-market operations.