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Régis Pierrard

regisss

regisss

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posted an update 6 days ago

Nice paper comparing the fp8 inference efficiency of Nvidia H100 and Intel Gaudi2: https://huggingface.co/papers/2502.01070 The conclusion is interesting: "Our findings highlight that the Gaudi 2, by leveraging FP8, achieves higher throughput-to-power efficiency during LLM inference" One aspect of AI hardware accelerators that is often overlooked is how they consume less energy than GPUs. It's nice to see researchers starting carrying out experiments to measure this! Gaudi3 results soon...

upvoted a paper 7 days ago

An Investigation of FP8 Across Accelerators for LLM Inference

reacted to fdaudens's post with 🔥 7 days ago

⭐️ The AI Energy Score project just launched - this is a game-changer for making informed decisions about AI deployment. You can now see exactly how much energy your chosen model will consume, with a simple 5-star rating system. Think appliance energy labels, but for AI. Looking at transcription models on the leaderboard is fascinating: choosing between whisper-tiny or whisper-large-v3 can make a 7x difference. Real-time data on these tradeoffs changes everything. 166 models already evaluated across 10 different tasks, from text generation to image classification. The whole thing is public and you can submit your own models to test. Why this matters: - Teams can pick efficient models that still get the job done - Developers can optimize for energy use from day one - Organizations can finally predict their AI environmental impact If you're building with AI at any scale, definitely worth checking out. 👉 leaderboard: https://lnkd.in/esrSxetj 👉 blog post: https://lnkd.in/eFJvzHi8 Huge work led by @sasha with @bgamazay @yjernite @sarahooker @regisss @meg

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Nice paper comparing the fp8 inference efficiency of Nvidia H100 and Intel Gaudi2: An Investigation of FP8 Across Accelerators for LLM Inference (2502.01070)

The conclusion is interesting: "Our findings highlight that the Gaudi 2, by leveraging FP8, achieves higher throughput-to-power efficiency during LLM inference"

One aspect of AI hardware accelerators that is often overlooked is how they consume less energy than GPUs. It's nice to see researchers starting carrying out experiments to measure this!

Gaudi3 results soon...

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