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After giving GPU Programming a hands-on try, I have come to appreciate the level of complexity in AI compute:
After giving GPU Programming a hands-on try, I have come to appreciate the level of complexity in AI compute:
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- Existing/leading frameworks (CUDA, OpenCL, DSLs, even Triton), still fall at the mercy of low-level compute that requires deeper understanding and experience.
- Existing/leading frameworks (CUDA, OpenCL, DSLs, even Triton), still fall at the mercy of low-level compute that requires deeper understanding and experience.
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- Ambiguous optimizations methods that will literally drive you mad 🤯
- Ambiguous optimizations methods that will literally drive you mad 🤯
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- Triton is cool but not cool enough (high level abstractions that fall back to low level compute issues as you build more specialized kernels)
- Triton is cool but not cool enough (high level abstractions that fall back to low level compute issues as you build more specialized kernels)
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- As for CUDA, optimization requires considering all major components of the GPU (DRAM, SRAM, ALUs) 🤕
- As for CUDA, optimization requires considering all major components of the GPU (DRAM, SRAM, ALUs) 🤕
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- Models today require stallion written GPU kernels to reduce storage and compute cost.
- Models today require stallion written GPU kernels to reduce storage and compute cost.
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- GPTQ was a big save 👍🏼
- GPTQ was a big save 👍🏼
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@karpathy
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  is right expertise in this area is scarce and the reason is quite obvious - uncertainties: we are still struggling to get peak performance from multi-connected GPUs while maintaining precision and reducing cost. 
  is right expertise in this area is scarce and the reason is quite obvious - uncertainties: we are still struggling to get peak performance from multi-connected GPUs while maintaining precision and reducing cost. 
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May the Scaling Laws favor us lol.
May the Scaling Laws favor us lol.
After giving GPU Programming a hands-on try, I have come to appreciate the level of complexity in AI compute:

- Existing/leading frameworks (CUDA, OpenCL, DSLs, even Triton), still fall at the mercy of low-level compute that requires deeper understanding and experience.
- Ambiguous optimizations methods that will literally drive you mad 🤯
- Triton is cool but not cool enough (high level abstractions that fall back to low level compute issues as you build more specialized kernels)
- As for CUDA, optimization requires considering all major components of the GPU (DRAM, SRAM, ALUs) 🤕
- Models today require stallion written GPU kernels to reduce storage and compute cost.
- GPTQ was a big save 👍🏼

@karpathy  is right expertise in this area is scarce and the reason is quite obvious - uncertainties: we are still struggling to get peak performance from multi-connected GPUs while maintaining precision and reducing cost. 

May the Scaling Laws favor us lol.
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