What is the Cerebras Wafer-Scale Engine (WSE)?

The Wafer-Scale Engine is a single, massive computer chip that is the size of a full silicon wafer. By integrating millions of cores, memory, and fabric onto a single piece of silicon, it is designed to run large AI models as if on a single device, eliminating the communication bottlenecks that occur when distributing a model across thousands of smaller GPUs.

How is Cerebras different from NVIDIA?

Cerebras uses a scale-up approach with a single, massive processor (the WSE) to simplify programming and achieve linear performance scaling for large AI models. In contrast, NVIDIA uses a scale-out approach, clustering thousands of smaller, general-purpose GPUs. While NVIDIA has a vast, mature ecosystem (CUDA), it requires complex distributed programming to coordinate the GPUs, which Cerebras aims to eliminate.

How can I access Cerebras compute power?

There are two primary ways to access Cerebras technology. Organizations can purchase a physical CS-3 system for on-premise installation in their own data center, or they can access compute resources through the Cerebras Cloud and its partner network, which includes the Condor Galaxy AI supercomputer, typically on a pay-per-use or subscription basis.

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Cerebras Systems

Paid

ai hardwaresupercomputinglarge language modelsdeep learninggenerative aiwafer-scale integrationhpcmodel trainingcloud computing

A deep-tech company that builds the world's largest computer chips and AI supercomputers, designed to drastically reduce the time and complexity of training massive generative AI models for enterprise and research.

Try it out

Cerebras Systems designs and manufactures massive, wafer-scale processors specifically engineered to accelerate deep learning and generative AI workloads. Their flagship product, the Condor Galaxy AI supercomputer network, leverages these Wafer-Scale Engines to provide unprecedented compute power for training large language models (LLMs). Rather than focusing on software, Cerebras provides access to its specialized hardware through both on-premise systems (CS-3) and a dedicated cloud service. This unique hardware-first approach allows them to overcome traditional GPU cluster limitations, promising faster training times and linear performance scaling with simplified programming. The company's core value proposition is delivering purpose-built supercomputing for the most demanding AI tasks to large enterprises, governments, and research institutions.

Pros

Unprecedented compute density on a single chip, the Wafer-Scale Engine, enabling entire neural networks to fit on one device.
Simplified scaling for large models, eliminating the need for complex distributed programming (MPI/NCCL) required for large GPU clusters.
Achieves near-perfect linear performance scaling, which significantly reduces training time for massive models.
Hardware-level support for dynamic and unstructured sparsity, which can increase computational efficiency for certain models.
Flexible deployment options, including on-premise systems (CS-3) and dedicated cloud access (Cerebras Cloud).

Cons

Proprietary hardware and software stack leads to vendor lock-in.
The hardware and programming ecosystem is far less mature and widely adopted than the NVIDIA CUDA standard.
Exorbitantly high cost of entry for on-premise systems, placing it out of reach for most organizations.
Substantial power, cooling, and space requirements for data center deployment.
Pricing for cloud services is opaque and requires direct sales consultation.

Key features

Wafer-Scale Engine (WSE-3), a single chip with 4 trillion transistors and 900,000 AI cores.
CS-3 System, the on-premise computer built around the WSE-3.
Condor Galaxy network, a cloud-based AI supercomputer connecting multiple CS-3 systems.
Cerebras Software Platform (CSoft) which integrates with standard ML frameworks.
Push-button scaling for large language models up to trillions of parameters.
Native processing of long sequences without memory limitations.
Weight streaming technology for executing extremely large models.
Support for major machine learning frameworks like PyTorch.

Integrations

PyTorchTensorFlowHugging FaceKubernetesSlurm Workload ManagerNumPyG42 CloudCirrascale Cloud Services

Target audience

Large enterprises (pharmaceuticals, finance, energy), government agencies, national labs, and major AI research institutions that need to train or fine-tune massive foundation models.

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Founded

2016

Headquarters

Sunnyvale, USA

Pricing Tiers

Cerebras CS-3 System

The physical, on-premise AI system built around the WSE-3 chip. For purchase by large enterprises and research institutions for their own data centers. Pricing is in the millions and determined by consultation.

Custom Quote

Cerebras Cloud

Cloud-based access to Cerebras's network of AI supercomputers, including the Condor Galaxy. Pricing is custom and based on the required compute resources and project duration, available via direct inquiry.

On-Demand / Subscription

Frequently Asked Questions

Top Alternatives to Cerebras Systems

NVIDIA

NVIDIA is the dominant market leader; one would choose it for its mature CUDA software ecosystem, broad industry support, and flexibility for various workloads beyond just large model training.

Google Cloud TPU

A strong alternative for those already in the GCP ecosystem, Google's Tensor Processing Units (TPUs) are highly optimized for TensorFlow and specific Google-developed models.

SambaNova Systems

A direct competitor offering a full-stack, integrated hardware/software platform as a service, which may appeal to organizations wanting a fully managed generative AI solution.

Graphcore

Another company building unique AI processors (IPUs) with a different architecture that may be better suited for novel AI research and algorithms requiring fine-grained parallelism.

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