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The Engineering Unlocks Behind DeepSeek | YC Decoded
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02/06/25
Takeaways
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DeepSeek's R1 is an open-source reasoning model that claims to achieve performance comparable to OpenAI's models at a significantly lower cost, causing a stir in the AI community.
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DeepSeek has been publishing research and releasing model weights for months, following a path similar to Meta's LLaMA model, unlike other major AI labs that keep their weights closed.
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DeepSeek's V3 model, released in December, is a general-purpose model that performs comparably to other leading models like OpenAI's GPT-4 and Anthropic's Claude 3.5.
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DeepSeek's R1 model builds on V3 by applying various algorithmic improvements to enhance its reasoning capabilities, achieving performance similar to OpenAI's models on complex reasoning benchmarks.
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Many of the algorithmic innovations that contribute to R1's performance were discussed in previous papers, including the V3 paper and DeepSeek's V2 paper from May 2024.
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DeepSeek optimized its models for efficiency by using 8-bit floating point format for training, which significantly reduces memory usage without sacrificing performance.
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A crucial enhancement in DeepSeek's models is the fp8 accumulation fix, which helps prevent small numerical errors from compounding during calculations, leading to more efficient training across thousands of GPUs. Additionally, DeepSeek needed to optimize GPU usage due to hardware constraints and export controls on GPU sales to China, as their GPUs were often idle, achieving only about 35% utilization.
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Nvidia's key advantage lies in its integrated solution, which includes networking with InfiniBand, CUDA software, and a developer-friendly experience, allowing AI researchers to effectively program GPU clusters as a unified system.
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DeepSeek's V3 model utilizes a mixture of experts architecture, activating only 37 billion out of 671 billion parameters for each token prediction, significantly reducing computation compared to models like Llama 3, which activates all 405 billion parameters.
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DeepSeek introduced novel techniques to efficiently train models with a mixture of experts architecture, stabilizing performance and increasing GPU utilization.
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V3 employs multi-head latent attention (MLA) to address key-value storage limitations, compressing matrices into a latent representation and reconstructing them only when necessary, which reduced cache size by 93.3% and increased generation throughput by 5.76 times.
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Unlike traditional models that predict only the next token, V3 uses multi-token prediction (MTP) to anticipate multiple future tokens at each step, enhancing training efficiency and improving representation planning.
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DeepSeek's R1 reasoning model achieves impressive performance by applying reinforcement learning specifically to think step by step through complex problems, similar to OpenAI's approach with their model, o1.
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Reinforcement learning is used by both OpenAI and DeepSeek to shape the behavior of their language models based on feedback and reward signals, enhancing their usefulness and alignment.
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DeepSeek's training pipeline focuses on assembling problems with verifiable outputs, particularly in math and coding, allowing the model to learn to think critically and produce correct answers without external examples.
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DeepSeek employs a novel technique called Group Relative Policy Optimization (GRPO) to evaluate model outputs based on accuracy and formatting, which significantly contributes to the model's reasoning capabilities.
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Through thousands of reinforcement learning steps, DeepSeek's model learned advanced reasoning skills, including extended chains of thought and the ability to recognize and correct its own mistakes.
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DeepSeek's R1 model is one of the first large models to achieve top-tier results purely through reinforcement learning, marking a significant milestone in AI development. Additionally, DeepSeek introduced a cold start phase for fine-tuning on structured reasoning examples before reinforcement learning, which eliminated language mixing issues and made outputs far more comprehensible.
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R1 achieves comparable performance to OpenAI's models on certain math and coding benchmarks, but the pace of innovation is accelerating, as evidenced by OpenAI's release of GPT-3.5 just two weeks after R1.
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DeepSeek's model R1 is freely accessible through their website and app, allowing users to download, run locally, and customize it, which contributes to its hype cycle.
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The $5.5 million training cost for OpenAI's GPT-3.5 only refers to the final training run and does not include the costs associated with R1's training or the extensive R&D and hardware expenses, which are likely in the hundreds of millions.
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A UC Berkeley lab recently applied R1's key techniques to produce complex reasoning in a smaller model for just $30, demonstrating that there is still room for new players in the AI field.
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DeepSeek's advancements indicate that there is potential for rebuilding the stack for optimizing GPU workloads, improving software at the inference layer, and developing AI-generated kernels, which is promising for AI applications in both consumer and B2B sectors.
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This is the best possible time to be building a startup, as the cost of intelligence continues to decrease, making AI applications more accessible.