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Atmrix

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LangChain Cast

12/18/24

@ LangChain

We faced unique challenges in scaling our platform, particularly with GPU local attention caching, which led to unexpected negative results during testing.

Video

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How We Built It: Character.AI - Fireside Chat with Harrison Chase & James Groeneveld

@ LangChain

How We Built It: Character.AI - Fireside Chat with Harrison Chase & James Groeneveld

12/18/24

Related Takeaways

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LangChain Cast

12/18/24

@ LangChain

Implementing multi-query attention resulted in a fivefold reduction in GPU cache usage, which is crucial for efficiently generating responses.

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LangChain Cast

12/18/24

@ LangChain

Without GPU caching, we would face significant financial challenges, as our 95% cache hit ratio is essential for cost-effective service delivery.

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OpenAI Cast

04/11/25

@ OpenAI

Scaling from 10,000 GPUs to 100,000 GPUs introduces many issues, as rare occurrences can become catastrophic at larger scales, affecting infrastructure and failure rates.

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LangChain Cast

12/18/24

@ LangChain

Our architectural choices, such as multi-query attention, enabled us to control speed and latency more precisely without hurting user engagement.

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OpenAI Cast

04/11/25

@ OpenAI

We had to be very paranoid about scaling laws, ensuring that any wins from new features were persistent across different scales.

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LangChain Cast

12/18/24

@ LangChain

The database layer has been a critical challenge in scaling our product, leading to catastrophic issues that required immediate attention from our team.

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OpenAI Cast

04/11/25

@ OpenAI

The effort to parallelize work aggressively during the run was crucial to avoid performance cliffs that could make the model untrainable.

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Sequoia Capital Cast

05/12/25

@ Sequoia Capital

American companies had to adapt quickly to scale AI inference after their previous providers' servers failed, leading to significant growth for Fireworks in capturing inference metrics.

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Jeff Dean

08/08/17

@ Y Combinator

We're experimenting with reinforcement learning to optimize the placement of TensorFlow operations across multiple GPUs for better performance.