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Sharon Zhou

@realSharonZhou • 27,371 subscribers

Recursively self-improving | VP Eng & AI, @AMD | Prev: Founder & CEO, Lamini. CS Faculty & PhD @Stanford. @Google. @Harvard | @MIT 35 under 35. Angel investor.

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It's here: We just hit superhuman performance on AI kernel optimization! Real customer models & production settings. Not toy problems (what I typically see). This is the year that Claude writes its own kernels, Codex its own kernels, for every new GPU that it wants to run on -- something that takes months to port between GPU generations today. This has a massive impact to scaling intelligence. More compute means getting the next frontier model sooner.

It's here: We just hit superhuman performance on AI kernel optimization! Real customer models & production settings. Not toy problems (what I typically see). This is the year that Claude writes its own kernels, Codex its own kernels, for every new GPU that it wants to run on -- something that takes months to port between GPU generations today. This has a massive impact to scaling intelligence. More compute means getting the next frontier model sooner.

240,793 просмотров • 3 месяцев назад

Mood: agents optimizing kernels Claude won on kernel optimization: gemm_bf16 at 1.19x vs Codex's 0.94x. Codex was faster (~1.3h vs ~3.4h) but produced no reinjectable optimizations. Claude used (hipBLASLt) as a drop-in replacement for the custom Triton kernel. For Codex, shape mismatch caused slight regression. Still improving, open sourcing soon --- AMD-AGI team (Sina Rafati, Emad Barsoum, and many more)

Mood: agents optimizing kernels Claude won on kernel optimization: gemm_bf16 at 1.19x vs Codex's 0.94x. Codex was faster (~1.3h vs ~3.4h) but produced no reinjectable optimizations. Claude used (hipBLASLt) as a drop-in replacement for the custom Triton kernel. For Codex, shape mismatch caused slight regression. Still improving, open sourcing soon --- AMD-AGI team (Sina Rafati, Emad Barsoum, and many more)

23,583 просмотров • 2 месяцев назад

I like to think of evals as something active, not passive -- it's a North Star that steers LLMs toward higher intelligence. Evals should drive your RL/SFT/post-training decisions. Internal evals at frontier labs make a huge difference -- and you can see it in how models behave differently (GPT seems better at one-shot tasks, Claude at multi-turn). If you want to learn more about building evals that actually improve your model in post-training, check out our AMD x DeeplearningAI course "Fine-tuning & RL for LLMs: Intro to Post-training" (content is free):

I like to think of evals as something active, not passive -- it's a North Star that steers LLMs toward higher intelligence. Evals should drive your RL/SFT/post-training decisions. Internal evals at frontier labs make a huge difference -- and you can see it in how models behave differently (GPT seems better at one-shot tasks, Claude at multi-turn). If you want to learn more about building evals that actually improve your model in post-training, check out our AMD x DeeplearningAI course "Fine-tuning & RL for LLMs: Intro to Post-training" (content is free):

16,613 просмотров • 4 месяцев назад

Super excited to launch a new AI course! 🚀 Fine-Tuning & Reinforcement Learning for LLMs: Intro to Post-Training A collaboration between AMD 🤝 Andrew Ng’s DeepLearning.AI to give every developer the tools & compute to work with the same post-training techniques, used across today’s leading AI labs. 🎓 Learn for free → 🧵

Super excited to launch a new AI course! 🚀 Fine-Tuning & Reinforcement Learning for LLMs: Intro to Post-Training A collaboration between AMD 🤝 Andrew Ng’s DeepLearning.AI to give every developer the tools & compute to work with the same post-training techniques, used across today’s leading AI labs. 🎓 Learn for free → 🧵

20,386 просмотров • 7 месяцев назад

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