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Cerebras

Paid
ai hardwaresupercomputingdeep learninglarge language modelswafer-scaleneural networkshpcmlopsgenerative aiai accelerator

Cerebras Systems builds wafer-scale AI accelerators and supercomputers designed to dramatically reduce the time and cost of training the world's largest neural networks for enterprise and research applications.

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Cerebras Systems is an AI company that manufactures specialized hardware systems for deep learning. Its foundational innovation is the Wafer-Scale Engine (WSE), a massive chip that integrates compute, memory, and communication to overcome the limitations of traditional multi-GPU clusters. The technology is aimed at large enterprises, national labs, and research institutions tackling computationally intensive AI problems, such as training and fine-tuning massive generative AI models. By co-designing its hardware and software, Cerebras offers a simpler programming model and claims faster performance on large models compared to distributed GPU clusters. Customers can purchase Cerebras's CS-3 systems directly or access their compute capabilities through cloud partners for a more flexible consumption model.

Pros

  • Vastly accelerates training times for extremely large-scale models compared to traditional GPU clusters.
  • Simplified programming model for PyTorch/TensorFlow abstracts away the complexity of distributed computing.
  • Wafer-scale architecture minimizes communication bottlenecks, leading to near-linear performance scaling for specific workloads.
  • On-chip memory and interconnects provide extremely high bandwidth, ideal for models with massive parameter counts.
  • Offers both on-premise systems (CS-3) and cloud access, providing deployment flexibility.

Cons

  • Extremely high capital cost for on-premise systems, making it inaccessible for smaller organizations.
  • Highly specialized hardware may not be as flexible or cost-effective as general-purpose GPUs for smaller, varied workloads.
  • The software ecosystem is less mature than NVIDIA's CUDA, with a smaller developer community.
  • Performance advantages are most pronounced on a specific class of very large models and may not universally exceed GPU performance.
  • Relies on a single vendor for the complete hardware/software stack, creating potential for vendor lock-in.

Key features

  • Wafer-Scale Engine 3 (WSE-3): A single chip with 4 trillion transistors, 900,000 AI cores, and 44GB of on-chip SRAM.
  • CS-3 System: Shipped as a complete supercomputer built around the WSE-3, delivering up to 125 petaflops of peak AI performance.
  • Cerebras Software Platform (CSoft): Enables training models in standard frameworks like PyTorch and TensorFlow without manual code changes for distribution.
  • Weight Streaming: Technology for training models with trillions of parameters by streaming weights from external memory.
  • Hardware Acceleration for Sparsity: Natively supports unstructured and structured sparsity to accelerate computation.
  • Cerebras AI Model Studio: A cloud service for fine-tuning and running inference on open-source Llama 2 models.
  • Support for Multi-System Clusters (Condor Galaxy): Ability to link multiple CS-3 systems together to function as a single, massive AI supercomputer.

Integrations

PyTorchTensorFlowHugging FaceKubernetesSlurmMicrosoft AzureG42 CloudCirrascaleOracle Cloud Infrastructure

Target audience

AI researchers, machine learning engineers, and data scientists at large enterprises, government agencies, and academic institutions who need to train and deploy extremely large-scale AI models.

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Founded

2016

Headquarters

Sunnyvale, USA

Pricing Tiers

On-Premise Purchase

Direct purchase of one or more Cerebras CS-3 systems for deployment in your own data center. Includes the hardware, software suite, and support services. Aimed at organizations requiring maximum performance and data control.

Custom Quote

Cloud Access

Access Cerebras compute clusters through cloud partners like G42 Cloud and Microsoft Azure. Billed based on usage or reserved capacity. Ideal for projects of varying scale and for avoiding capital expenditure on hardware.

Pay-as-you-go / Custom

AI Model Studio

A cloud-based service for fine-tuning and running inference on a curated set of open-source models. Billed based on tokens or compute time, this is ideal for users who want to leverage pre-trained models without managing infrastructure.

Pay-per-use

Frequently Asked Questions

Top Alternatives to Cerebras

NVIDIA

NVIDIA's DGX systems and GPUs (e.g., H100/B200) are the market-dominant alternative, offering a vast, mature software ecosystem (CUDA) and broad flexibility for all types of AI workloads.

SambaNova Systems

SambaNova offers a competitive full-stack, integrated hardware/software system for enterprise AI, using a 'Reconfigurable Dataflow Architecture' to challenge GPU dominance in large model training and inference.

Groq

Groq focuses on ultra-low latency for AI inference with its Language Processing Unit (LPU), making it a strong alternative for real-time applications rather than large-scale training, which is Cerebras's primary strength.

Google Cloud TPU

Google's Tensor Processing Units are custom AI accelerators available on the Google Cloud Platform, offering a highly optimized hardware/software stack for training and inference, especially for Google's own models.

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