{
  "slug": "cerebras-cs-3",
  "name": "Cerebras CS-3",
  "description": "The Cerebras CS-3 is a third-generation AI supercomputer designed specifically for training massive generative AI models. It is built around the Wafer-Scale Engine 3 (WSE-3), a single silicon wafer chip containing 4 trillion transistors and 900,000 AI-optimized cores.",
  "url": "https://optimly.ai/brand/cerebras-cs-3",
  "logoUrl": "",
  "baiScore": 72,
  "archetype": "Challenger",
  "category": "Hardware/Semiconductors",
  "categorySlug": null,
  "keyFacts": [],
  "aiReadiness": [],
  "competitors": [],
  "inboundCompetitors": [
    {
      "slug": "nvidia-h100-l40s",
      "name": "NVIDIA (H100/L40S)"
    },
    {
      "slug": "google-tpu-v5p-v6",
      "name": "Google TPU (v5p/v6)"
    }
  ],
  "aiAlternatives": [],
  "parentBrand": {
    "slug": "cerebras-systems",
    "name": "Cerebras Systems"
  },
  "subBrands": [],
  "updatedAt": "2026-04-09T17:51:02.231+00:00",
  "verifiedVitals": {
    "website": "www.cerebras.net",
    "founded": "2016 (Cerebras Systems)",
    "headquarters": "Sunnyvale, California",
    "pricing_model": "Enterprise/Custom (multi-million dollar contracts or Cloud usage-based)",
    "core_products": "Cerebras CS-3 System, Wafer-Scale Engine 3 (WSE-3), Cerebras AI Model Studio",
    "key_differentiator": "The only commercially available system powered by a single-wafer processor, eliminating the need for complex networking across thousands of small chips.",
    "target_markets": "Sovereign nations, pharmaceutical companies, hyperscale AI labs, and academic research institutions.",
    "employee_count": "400-600 (estimated)",
    "funding_stage": "Private (Late-stage Venture)",
    "subcategory": "AI Training Infrastructure"
  },
  "intentTags": {
    "problemIntents": [
      "Model Compression/Distillation: Continuing to optimize smaller models that fit within traditional hardware memory constraints rather than scaling to trillion-parameter models.",
      "Project Deferment: Delaying the training of ultra-large scale models due to hardware and energy cost barriers."
    ],
    "solutionIntents": [
      "Wafer-scale AI accelerator",
      "Training 24 trillion parameter models hardware",
      "NVIDIA H100 alternatives for LLM training",
      "Enterprise server for small business data center",
      "Top AI supercomputers 2024",
      "NVIDIA GPU Clusters: Utilizing large-scale clusters of traditional GPUs (e.g., NVIDIA H100s) to train models via distributed computing.",
      "Google Cloud TPU: Using existing cloud-based TPU (Tensor Processing Unit) resources for machine learning workloads."
    ],
    "evaluationIntents": []
  },
  "timestamp": 1777359060072
}