CEREBRAS
Tokenised Economic Right (not equity) to potential future liquidity from WLTH’s (Metamasters DAO Corp) allocation in Cerebras Systems. Slices are not securities of, nor endorsed by, the underlying company. They are unsecured contractual rights against WLTH (Metamasters DAO Corp). Residents of the U.S., UK, EU, and other restricted jurisdictions are ineligible. Review the full Terms & Conditions before purchasing.
Category: AI Hardware, Semiconductor Infrastructure & High-Performance Compute Stage: Late-stage private Opportunity Type: Tokenised Economic Right
Company Overview
Cerebras Systems is an artificial intelligence hardware company developing specialised processors and integrated systems purpose-built for training and inference of large-scale AI models.
Founded in 2016 and headquartered in Sunnyvale, California, Cerebras is best known for its Wafer-Scale Engine (WSE), one of the largest computer chips ever built, designed to accelerate deep learning workloads beyond the limits of conventional GPU clusters.
Rather than relying on distributed multi-GPU scaling architectures, Cerebras takes a vertically integrated systems approach, combining custom silicon, interconnect, memory architecture, and software into unified AI supercomputing platforms.
The company serves AI research labs, enterprise customers, and government institutions requiring ultra-high-performance model training infrastructure.
Cerebras remains privately held and venture-backed, positioning itself as a next-generation AI compute alternative to traditional GPU-centric architectures.
The Problem
Training frontier AI models requires enormous computational resources and increasingly complex distributed GPU clusters.
Scaling across thousands of GPUs introduces networking bottlenecks, latency, and energy inefficiencies.
AI hardware supply constraints have created cost inflation and limited access to advanced compute infrastructure.
Traditional chip architectures were not originally designed for trillion-parameter deep learning workloads.
Power consumption and operational complexity continue to rise as models scale.
The Solution
Development of wafer-scale processors designed specifically for AI training and inference.
Elimination of multi-chip communication bottlenecks by integrating compute cores on a single silicon wafer.
High-bandwidth on-chip memory architecture optimised for large neural network workloads.
Integrated hardware-software stack simplifying deployment relative to large distributed GPU clusters.
AI supercomputing systems offered as on-premise infrastructure and cloud-accessible compute services.
Founding Team
Andrew Feldman – Co-Founder & CEO Serial entrepreneur with prior experience in semiconductor and networking companies; leads Cerebras’ strategy in wafer-scale AI computing.
Gary Lauterbach – Co-Founder & CTO Veteran computer architect with prior experience at Sun Microsystems and SeaMicro; leads chip architecture and engineering.
Sean Lie – Co-Founder & Chief Hardware Architect Expert in processor design and large-scale systems engineering.
Team: Engineering leadership includes experienced semiconductor architects, systems designers, and AI infrastructure specialists with backgrounds at major technology and chip companies.
Business Model
Sale of integrated AI supercomputing systems to enterprise, research, and government customers.
Cloud-based access to Cerebras-powered AI compute clusters.
Long-term infrastructure-style deployments for large AI labs and sovereign compute initiatives.
Revenue driven by demand for large-model training and specialised high-performance inference workloads.
Vertically integrated approach capturing value across silicon, systems, and software stack.
Traction and Validation
Founded in 2016; unveiled first-generation Wafer-Scale Engine in 2019.
Multiple generations of WSE processors released with increasing transistor counts and performance gains.
Deployed AI supercomputers for national laboratories, research institutions, and enterprise AI customers.
Secured significant venture funding from institutional investors and strategic backers.
Positioned as a specialised AI hardware competitor to GPU-dominated ecosystems.
Recognised for architectural innovation in large-scale AI system design.
Intellectual Property and Strategic Assets
Owned
Wafer-Scale Engine (WSE) architecture and associated semiconductor IP.
Proprietary interconnect, memory, and compute fabric design.
Integrated AI software stack optimised for wafer-scale training.
Access / Partnered
Semiconductor fabrication partnerships for advanced node manufacturing.
Enterprise, research, and sovereign customers deploying large-scale AI systems.
Brand & Positioning
Positioned as a differentiated AI hardware innovator focused exclusively on deep learning acceleration at scale.
Disclaimer
No affiliation — nominative reference only.
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