OLIX

OLIX designs optical AI accelerators that use light-based processors for training and inference on large models.

ActiveArtificial Intelligence (AI)HardwareMachine LearningManufacturingOptical Communication

Updated: Feb 17, 2026 ·

About

OLIX, based in London, England, pioneers optical AI accelerators, leveraging light-based processors to achieve unprecedented speeds and efficiencies in training and inference for large AI models, transforming the landscape of artificial intelligence, hardware, machine learning, manufacturing, and optical communication across Europe and beyond.

Recent News & Mentions

Feb 11, 2026FundingOLIX - Series A

Financial History

OLIX has raised $220.0M across 1 funding round. Most recently, it raised $220.0M Series A in February 2026 at a valuation of approximately $10.0M.

Total Raised

$220.0M

Valuation

$10.0M

Funding Rounds Raised

Date	Round	Lead Investors	Other Investors
Feb 11, 2026	$220.0M Series A	Hummingbird Ventures	Crane Venture Partners, Entrepreneurs First, Fundomo, LocalGlobe, Plural Platform, Transition, Vertex Ventures

Financial History

OLIX has raised $220.0M across 1 funding round.

Total Raised

$220.0M

Valuation

$10.0M

Deep Dive

THE POWER OF OPTICAL AI ACCELERATORS

OLIX is at the forefront of a revolutionary shift in AI computing, replacing traditional electronic components with light-based processors. This fundamental change allows for significantly faster data processing and reduced energy consumption, addressing critical bottlenecks in current AI infrastructure. By harnessing the speed of light, OLIX's accelerators are designed to handle the immense computational demands of the largest and most complex AI models with unparalleled efficiency.

TRAINING AND INFERENCE AT LIGHT SPEED

The core innovation from OLIX lies in its ability to accelerate both the training and inference phases of AI models. For training, this means models can learn faster from vast datasets, leading to quicker development cycles and more sophisticated AI capabilities. For inference, OLIX's technology enables real-time decision-making and analysis, crucial for applications ranging from autonomous vehicles to advanced data analytics, all while minimizing latency.

REVOLUTIONIZING AI HARDWARE AND MANUFACTURING

OLIX's optical AI accelerators are not just about speed; they represent a significant advancement in hardware design and manufacturing processes. By integrating optical components, OLIX is creating a new class of AI hardware that is more compact, energy-efficient, and scalable than its electronic predecessors. This innovation has profound implications for the manufacturing sector, enabling the production of more powerful and sustainable AI computing solutions.

ADVANCING OPTICAL COMMUNICATION FOR AI

The development of optical AI accelerators by OLIX naturally intersects with advancements in optical communication. The efficient movement of data within and between these light-based processors relies on cutting-edge optical communication technologies. OLIX's work contributes to and benefits from the ongoing evolution of high-speed, high-bandwidth optical networks, fostering a symbiotic relationship between AI processing and data transfer.

Frequently Asked Questions

What are optical AI accelerators?

Optical AI accelerators are cutting-edge hardware components developed by companies like OLIX that use light-based processors instead of traditional electronics to perform AI computations, offering significant advantages in speed and energy efficiency.

Where is OLIX located?

OLIX is based in London, England, United Kingdom, serving the European market and globally.

What industries does OLIX impact?

OLIX's technology has a transformative impact across several key industries, including Artificial Intelligence (AI), Hardware, Machine Learning, Manufacturing, and Optical Communication.

How do light-based processors improve AI performance?

Light-based processors enhance AI performance by enabling faster data processing and reducing energy consumption compared to electronic systems. This allows for more efficient training and inference of large, complex AI models.