M4A

Cerebras

AI supercomputers with wafer-scale engines for model training and inference

enterpriseproductionhardwaretraininginferencesupercomputerwafer-scale
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Memory Types

Integrations

pytorch, huggingface


Overview


Cerebras builds AI supercomputers using wafer-scale engine (WSE) technology - the largest chip ever built. Each WSE contains 2.6 trillion transistors and 850,000 cores on a single wafer. This unique architecture enables dramatically faster AI model training and inference compared to traditional GPU clusters.


The company recently launched AI inference services showcasing extreme speed, and their systems are used by leading AI labs and research institutions. Cerebras is publicly traded and provides both cloud access and on-premise systems for organizations with the most demanding AI workloads.


Key Features


  • **Wafer-Scale Engine**: Largest chip ever built
  • **Extreme Performance**: 100x faster than GPU clusters for some workloads
  • **Inference Cloud**: Fast LLM inference service
  • **Training Acceleration**: Dramatically faster model training
  • **Large Models**: Train models with trillions of parameters
  • **Energy Efficient**: Better performance per watt than GPUs
  • **On-Premise**: Deploy systems in your datacenter
  • **Cloud Access**: Cerebras cloud for easier access

    • When to Use Cerebras


    Cerebras is ideal for:

  • Training very large custom models
  • Research institutions with extreme compute needs
  • Organizations training foundation models
  • Applications requiring fastest possible inference
  • Companies with massive AI budgets
  • Government and defense AI projects

    • Pros


  • Revolutionary hardware technology
  • Dramatically faster than GPUs for some workloads
  • Can train massive models efficiently
  • Energy efficient at scale
  • Publicly traded company
  • Proven in production at major labs
  • Good for competitive advantage
  • Both cloud and on-premise options

    • Cons


  • Extremely expensive
  • Overkill for most applications
  • Limited to organizations with huge budgets
  • Requires significant expertise
  • Hardware dependency (single vendor)
  • Long procurement cycles for systems
  • Limited model/framework support compared to GPUs
  • Small ecosystem relative to NVIDIA

    • Pricing


  • **Systems**: $2-3 million per CS-2 system
  • **Cloud**: Premium pricing for inference/training
  • **Enterprise Only**: Contact sales for pricing
  • **Research**: Some academic pricing available

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