DeepSeek R1 Fully Tested - Insane Performance

1. DeepSeek R1 demonstrates impressive human-like reasoning.

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The model exhibits a human-like internal monologue, showcasing its ability to think through problems in a structured manner.

• It engages in back-and-forth reasoning, similar to human thought processes.
• The model's approach to problem-solving involves planning before coding.
• This human-like thinking is more advanced compared to previous models.

2. DeepSeek R1 successfully codes complex games.

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The model was able to write a fully functional Snake game and Tetris in Python on the first attempt.

• It planned the game structure and features before outputting code.
• The output for both games was complete and functional.
• This indicates a high level of coding capability and understanding.

3. The model's thinking process can be time-consuming.

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DeepSeek R1's reasoning may take longer due to its thorough thinking approach.

• Complex problems can require several minutes of processing time.
• The model's depth of thought leads to more accurate outputs.
• Efficiency in inference will improve as computational power increases.

4. DeepSeek R1's performance relies on powerful hardware.

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The model requires substantial computational resources to operate effectively.

• It runs on a system with 8 AMD Instinct GPUs and significant storage.
• This hardware is necessary to support the model's 671 billion parameters.
• The performance highlights the importance of infrastructure in AI capabilities.

5. DeepSeek R1 excels in logical reasoning tasks.

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The model effectively solved logic problems, demonstrating its reasoning capabilities.

• It accurately interpreted size restrictions for mailable envelopes.
• The model navigated complex scenarios with multiple variables.
• This showcases its ability to handle ambiguity and nuanced questions.

6. DeepSeek R1's censorship reflects its training origins.

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As a Chinese model, DeepSeek R1 has built-in censorship protocols.

• It cannot provide information on sensitive political topics.
• This limitation is a result of its training data and guidelines.
• Understanding these constraints is crucial for users.

7. Self-hosting the model reveals censorship limitations.

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Even when self-hosted, the model exhibits censorship, particularly regarding sensitive topics like Taiwan's independence.

• The core vanilla version does not provide unrestricted information.
• Censorship appears to be hardcoded into the model's responses.
• Users may encounter limitations similar to those in US models.

8. Censorship reflects moral implications in AI responses.

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The model's reluctance to provide certain information indicates a consideration of moral implications.

• It hesitated to answer questions about illegal activities.
• This suggests an embedded ethical framework in the AI's design.
• Users may find the model's responses influenced by societal norms.

9. The model's performance is notably impressive.

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The model executed tasks flawlessly, demonstrating high performance in generating responses.

• It successfully generated ten sentences ending with 'Apple'.
• The performance was described as extremely impressive.
• The model's capabilities were enhanced by powerful GPU support.