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How Cerebras is breaking the GPU bottleneck on AI inference
- Cerebras Systems has launched its third-generation chip, the Wafer-Scale Engine 2 (WSE 2), a revolutionary AI processor that delivers enhanced performance and efficiency in process inference tasks.
- Cerebras aims to disrupt the AI market, which has long been dominated by Nvidia's graphics processing units (GPUs), and Groq, a startup that provides advanced AI-specialized compute hardware.
- The new product offers a competitive alternative to Nvidia's offerings in AI inference, where fast processing and efficiency are more important.
- This article explains how Cerebras’ new product is a challenge to Nvidia's dominance, and how it stacks up against those of Groq.
- While Nvidia's GPUs have been dominant in the training of large language models (LLMs) until now, Cerebras' new processor is aimed at the inference process by which a trained AI model evaluates new data and produces accurate results.
- Cerebras’ chip is designed to handle heavy inference workloads that GPUs usually consume considerable power and generate higher levels of heat, making them expensive to maintain.
- Cerebras is positioning itself as a competitive solution with its affordable pricing starting at just 10 cents per million tokens and the capability to handle huge workloads without having to network.
- The new Cerebras chip is considerably larger, at 56x larger than the biggest GPUs, making it the physically largest neural network chip ever produced.
- Cerebras’ recent chip launch is drawing industry attention, and several industry leaders are lauding its technology.
- Enterprise decision-makers need to navigate an evolving landscape given the emergence of Cerebras and Groq, which offer compelling new alternatives to Nvidia.
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