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GPUs Go Brrr
how make gpu fast?
Hazy Research
We are a CS research group at Stanford led by Professor Chris Ré. Machine learning is fundamentally changing the ways that people build and maintain software, and we’re interested in understanding those shifts and building the foundations for the next generation of machine learning systems.
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Visit hazyresearch.stanford.edu → On Hacker News Apr 2023 – Nov 2025
AI & Machine Learning 9Systems & Performance 6Science & Space 1Software Engineering 1Databases & Data 1
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We bought the whole GPU, so we're damn well going to use the whole GPU
Benjamin Spector* , Jordan Juravsky* , Stuart Sul* , Dylan Lim , Owen Dugan , Simran Arora , Chris Ré
3
From deep to long learning?
For the last two years , a line of work in our lab has been to increase sequence length. We thought longer sequences would enable a new era of machine learning foundation models: they could learn from longer contexts, multiple media sources, complex demonstrations, and more. All data ready and waiting to be learned…
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AMD GPUs Go Brrr
multi silicon ai is coming
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HipKittens: Fast and furious AMD kernels
multi silicon ai is coming
6
Look Ma, No Bubbles: Designing a Low-Latency Megakernel for Llama-1B
Benjamin Spector* , Jordan Juravsky* , Stuart Sul , Owen Dugan , Dylan Lim , Dan Fu , Simran Arora , Chris Ré
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How to scale LLMs better with an alternative to transformers
Monarch Mixer: Revisiting BERT, Without Attention or MLPs
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Batch computing and the coming age of AI systems
There’s a lot of excitement right now about human-in-the-loop systems supercharged by foundation models including chat assistants ( ChatGPT ), word processing ( Microsoft Office ), graphic design ( Stable Diffusion ), and code editing ( Copilot ). However, these systems only begin to scratch the surface of the role…
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Learning From DNA: a grand challenge in biology
Is DNA all you need?