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Cerebras

Paid
ai hardwaresupercomputinglarge language modelsllmhpcdeep learningwafer-scaleenterprise aineural networks

Cerebras offers unparalleled, specialized hardware for training enormous AI models, but its high cost and niche architecture make it a solution for only the most demanding, large-scale enterprise and research use cases.


Cerebras builds a new class of computer systems, including wafer-scale chips, to accelerate training for the world's largest AI and HPC models.

Cerebras Systems is pioneering a new approach to artificial intelligence compute, moving beyond the limitations of traditional GPU clusters. The company designs and manufactures specialized hardware, centered around its Wafer-Scale Engine (WSE), the largest chip ever built. This chip is integrated into a full computer system, like the CS-3, which is engineered to train massive AI models with trillions of parameters as if they were running on a single, powerful device. This drastically simplifies the distributed computing challenges associated with training large-scale models. Cerebras is built for large enterprises, research institutions, and governments that need to train foundational AI models, particularly large language models (LLMs) and complex high-performance computing (HPC) simulations. The company offers its technology through two primary models: direct purchase of its on-premise CS-3 systems and cloud-based access to its Condor Galaxy AI supercomputer. By providing extreme performance and simplified scaling, Cerebras aims to reduce the time and complexity required to make breakthroughs in AI.

Pros

  • Unprecedented speed for training single, massive AI models.
  • Dramatically simplifies the complexity of distributed training.
  • Eliminates common bottlenecks found in large GPU clusters.
  • Available as both a cloud service and an on-premise system.
  • Leading performance-per-dollar for large model training.
  • Minimal software changes needed to run models from standard frameworks.

Cons

  • Extremely high cost, aimed at multi-million dollar budgets.
  • Less flexible than general-purpose GPUs for a wide variety of smaller workloads.
  • Smaller software and community ecosystem compared to NVIDIA CUDA.
  • Not cost-effective for model inference or smaller-scale AI tasks.

Key features

  • Wafer-Scale Engine (WSE-3): The industry's largest and fastest AI chip.
  • CS-3 System: An integrated, on-premise system for AI/HPC workloads.
  • Condor Galaxy: A network of interconnected AI supercomputers available via the cloud.
  • Push-button scaling for multi-trillion parameter models.
  • Native support for unstructured and dynamic sparsity to accelerate training.
  • Simplified programming model that abstracts away distributed computing.
  • High-speed memory and interconnect for bottleneck-free performance.
  • Purpose-built for Large Language Models (LLMs) and scientific computing.

Integrations

PyTorchTensorFlowJAX

Target audience

Large enterprises, research labs, supercomputing centers, and governments looking to train massive-scale AI models.


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Founded

2016

Headquarters

Sunnyvale, United States

Pricing Tiers

CS-3 System

Contact Sales

Cerebras Cloud

Contact Sales


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