Speedata Raises $44M Series B to Compete with Nvidia

Speedata Secures $44 Million in Series B Funding

Speedata, a startup headquartered in Tel Aviv, has successfully completed a $44 million Series B funding round. This brings the total amount of capital raised by the company, which is focused on developing an analytics processing unit (APU), to $114 million.

Investor Details

The latest funding round was spearheaded by existing investors. These include Walden Catalyst Ventures, 83North, Koch Disruptive Technologies, Pitango First, and Viola Ventures. Furthermore, strategic investments were made by Lip-Bu Tan, CEO of Intel and managing partner at Walden Catalyst Ventures, and Eyal Waldman, the co-founder and former CEO of Mellanox Technologies.

APU Architecture and its Advantages

Speedata’s APU architecture is specifically engineered to tackle the performance limitations inherent in data analytics at the hardware level. This contrasts with graphics processing units (GPUs), which were originally created for graphics rendering and subsequently adapted for AI and data-intensive applications, as explained by the company.

According to Adi Gelvan, CEO of Speedata, conventional data analytics have historically relied on standard processors. More recently, companies such as Nvidia have focused on utilizing GPUs for these workloads. However, Gelvan asserts that these solutions are either general-purpose or designed for applications other than data analytics. “Our APU is purpose-built for data processing and a single APU can replace racks of servers, delivering dramatically better performance,” he stated in a TechCrunch interview.

Company Origins and Core Technology

Founded in 2019 by a team of six, Speedata’s origins lie in pioneering research concerning Multi-Threaded Coarse-Grained Reconfigurable Architecture (CGRA) technology. The founders, collaborating with experts in ASIC design, identified a key issue: the reliance on general-purpose processors for data analytics. As workloads increased in complexity, this often necessitated the deployment of hundreds of servers.

The team envisioned a dedicated processor capable of performing these tasks more efficiently and with reduced energy consumption. “We saw this as an opportunity to put our decades of research in silicon into transforming how the industry processes data,” Gelvan explained.

Roadmap and Target Workloads

Currently, Speedata’s APU is optimized for Apache Spark workloads. However, the company’s future plans involve expanding support to encompass all major data analytics platforms, as indicated by the CEO.

Gelvan articulated the company’s ambition: “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.”

Early Adoption and Product Launch

Speedata reports that several large organizations are currently evaluating its APU, though the company has not yet disclosed their identities. The official product launch is scheduled to occur at Databricks’ Data & AI Summit during the second week of June, where the APU will be publicly demonstrated for the first time.

The startup highlights a specific instance where its APU completed a pharmaceutical workload in just 19 minutes. This represents a substantial improvement over the 90 hours required by a conventional processing unit, equating to a 280x speed increase.

Recent Milestones and Future Outlook

Since its previous funding round, Speedata has achieved significant progress, including the completion of the design and manufacturing of its initial APU in late 2024.

“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. 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,” Gelvan concluded.