Speedata Secures $44M to Propel Next-Gen Analytics Chip to Market

Tel Aviv-based chipmaker Speedata has raised $44 million in Series B funding, further positioning itself as a challenger to GPU giant Nvidia in the high-performance data analytics space. With this latest investment, Speedata’s total funding now stands at $114 million.

Investors Double Down on Speedata’s APU Vision

The funding round saw continued backing from Speedata’s existing investors: Walden Catalyst Ventures, 83North, Koch Disruptive Technologies, Pitango First, and Viola Ventures. Strategic investors also joined the round, including Intel CEO and Walden Catalyst managing partner Lip-Bu Tan, and Eyal Waldman, co-founder and former CEO of Mellanox Technologies.

What Makes Speedata Different? A New Class of Processor

At the core of Speedata’s innovation is its Analytics Processing Unit (APU) — a custom chip architecture designed specifically to accelerate data analytics and AI tasks. While GPUs have been widely adopted for such workloads, they were originally developed for rendering graphics and later repurposed.

“For decades, data analytics have relied on standard processing units, and more recently, companies like Nvidia have invested in pushing GPUs for analytics workloads,” explained Speedata CEO Adi Gelvan in an interview with TechCrunch.
“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.”

Founders with Deep Roots in Chip Innovation

Founded in 2019 by a team of six seasoned technologists, several of whom pioneered the Multi-Threaded Coarse-Grained Reconfigurable Architecture (CGRA), Speedata emerged from the belief that data analytics deserved a dedicated processor. Rather than relying on vast arrays of general-purpose servers, they aimed to deliver the same output more efficiently and at higher speeds.

“We saw this as an opportunity to put our decades of research in silicon into transforming how the industry processes data,” said Gelvan.

Performance Breakthroughs: From Days to Minutes

Speedata reports staggering performance gains with its APU. In one pharmaceutical case study, a task that once required 90 hours on traditional hardware was completed in just 19 minutes using Speedata’s chip — a 280x improvement in speed.

Targeting the Analytics Stack

Currently optimized for Apache Spark workloads, Speedata’s APU is designed to eventually support all major data 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 told TechCrunch.

Although Speedata has not publicly named its early enterprise testers, it confirmed that large organizations are already piloting the APU.

Public Debut Set for Databricks’ Data & AI Summit

The company is preparing for the official unveiling of its APU at Databricks’ Data & AI Summit, taking place in the second week of June. The launch marks a pivotal milestone in Speedata’s journey from concept to commercialization.

“We’ve moved from concept to testing on a field-programmable gate array (FPGA), and now we are proud to say we have working hardware that we are currently launching,” Gelvan said.
“We already have a growing pipeline of enterprise customers eagerly waiting for this technology and we’re ready to scale our go-to-market operations.”