Cerebras
An AI chip startup that builds the world's largest processor — the Wafer-Scale Engine — designed specifically for training and running AI models at unprecedented speeds.
Cerebras' WSE-3 chip contains 4 trillion transistors across an entire silicon wafer (approximately 46,000 square millimeters), compared to NVIDIA's H100 at roughly 800 square millimeters. The company's CS-3 system can train LLMs at speeds competitive with large GPU clusters while using significantly less power. Cerebras has raised over $700 million and partnered with organizations including the Mayo Clinic and the UAE's G42. The company offers cloud-based inference that delivers the fastest per-token LLM output speeds commercially available.
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Related Terms
AI Compute
The computational resources — primarily GPU and TPU processing power — required to train and run AI models, typically measured in FLOP (floating-point operations) or GPU-hours.
Capex (Capital Expenditure)
Long-term investment spending by companies on physical assets like data centers, GPU clusters, and networking infrastructure — the backbone of AI deployment at scale.
Data Center
A facility housing computer systems and infrastructure used to process, store, and distribute data — increasingly built specifically for AI training and inference workloads.
Fine-Tuning
The process of further training a pre-trained AI model on a specific, smaller dataset to specialize it for a particular task or domain, requiring far less compute than training from scratch.
Foundation Model
A large AI model trained on broad data that can be adapted to a wide range of downstream tasks — examples include GPT-4, Claude, Gemini, and Llama.
Frontier Model
The most capable and advanced AI models at any given time, typically trained with the largest compute budgets and achieving state-of-the-art performance on benchmarks.
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