DeepSeek, a new AI model, has taken the tech world by storm by achieving groundbreaking results while operating at a fraction of the cost compared to rivals like ChatGPT and Llama. This efficiency stems from innovative techniques such as 'mixture-of-experts' and 'inference-time compute scaling,' allowing DeepSeek to process information with fewer resources. DeepSeek's success has challenged the industry's prevailing dogma of bigger models being better, sparking debate and prompting a reassessment of AI development strategies.
The ultimate action-packed science and technology magazine bursting with exciting information about the universeEngaging articles, amazing illustrations & exclusive interviewsartificial intelligencethan AI models made by some of the leading Silicon Valley giants — namely OpenAI's ChatGPT, Meta’s Llama and Anthropic's Claude. And most staggeringly, the model achieved these results while being trained and run at a fraction of the cost.
Key to this is a"mixture-of-experts" system that splits DeepSeek's models into submodels each specializing in a specific task or data type. This is accompanied by a load-bearing system that, instead of applying an overall penalty to slow an overburdened system like other models do, dynamically shifts tasks from overworked to underworked submodels.
This efficiency extends to the training of DeepSeek's models, which experts cite as an unintended consequence of U.S. export restrictions.'s access to Nvidia's state-of-the-art H100 chips is limited, so DeepSeek claims it instead built its models using H800 chips, which have a reduced chip-to-chip data transfer rate. Nvidia designed this"weaker" chip in 2023 specifically to circumvent the export controls.
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