Published by Global Banking & Finance Review®
Posted on November 10, 2025
2 min readLast updated: January 21, 2026
Add as preferred source on GoogleTsavorite secures $100M in pre-orders for AI chips to scale workflows, driven by demand for its Omni Processing Unit. Chips to be delivered next year.
(Reuters) -AI startup Tsavorite Scalable Intelligence said on Monday it has secured more than $100 million in pre-orders from enterprises and cloud providers across the U.S., Asia and Europe to deploy its chips that are used to scale AI workloads.
The company has seen strong demand for its Omni Processing Unit (OPU), a compute architecture which unifies central processing units, graphics processing units, memory and connectivity in one device, enabling it to be configured to meet diverse market and application needs.
The growing complexity of artificial intelligence workloads is driving the industry to scalable solutions that address challenges such as power consumption, scalability and cost, facilitating broader adoption of the AI infrastructure.
Tsavorite declined to disclose its current valuation or the total amount of funding it has raised so far.
Founded by former Intel and chip industry veterans in 2023, the company expects to deliver its chips and enterprise class AI appliances capable of supporting all agentic AI workflows by next year.
"We've built the first truly composable, developer-friendly, AI platform that delivers step-change gains in efficiency, cost and scale from edge to hyperscale," CEO Shalesh Thusoo said.
The company is using Samsung's SF4X platform solution to fabricate the OPU.
(Reporting by Harshita Mary Varghese in Bengaluru; Editing by Krishna Chandra Eluri)
An AI chip is a specialized hardware designed to accelerate artificial intelligence tasks, enhancing the performance of AI applications by processing data more efficiently.
The Omni Processing Unit (OPU) is a compute architecture that integrates various processing units and memory into a single device, allowing for flexible configurations to meet diverse application needs.
AI workloads refer to the computational tasks and processes required to train and run artificial intelligence models, which can be complex and resource-intensive.
Scalability refers to the capability of a system to handle a growing amount of work or its potential to accommodate growth without compromising performance.
Power consumption in computing refers to the amount of electrical energy used by a computer system or component during operation, which is crucial for efficiency and cost management.
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