there are two problems, you've identified both:
1. accelerator support involves system package and driver version resolution that we didn't have to meaningfully confront with CPUs (CPUs just had to deal with arch)
2. accelerator binaries are large — so we had to move to s3
AI HARDWARE
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Accelerator Support: System Dependencies and Binary Storage Challenges
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PyTorch modernizes accelerator support with improved uv integration
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we're willing to fix it to work better with uv (which is fantastic) cc: @_seemethere I think PyTorch was forced to solve the accelerator support problem before Python packaging was ready for it, so we solved it as best as we can — but we're willing to move to a better world if
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MacBook Air M3 with 24GB RAM Handles Llama 3.2 Well
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Yeah. But in the meantime, the MacBook Air M3 24 Gb is just fine for Llama 3.2 😛
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Choosing TTS Engines for Lightweight GPU Hardware Deployment
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It depends on the hardware you choose to deploy the TTS engine. For lightweight GPUs I’d recommend using MeloTTS
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KKR and ECP Partner to Build AI-Ready Data Center Infrastructure
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'KKR and ECP plan to engage with industry leaders including utilities, power and data center developers, and independent power producers to accelerate the delivery of data center campuses required by hyperscalers.' https://
media.kkr.com/news-details/?
news_id=8f924dd6-41ea-480d-9a96-d854c7232bbc
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Mistral AI and Qualcomm Integrate Mistrals at Edge Devices
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[#Article] Mistral AI and Qualcomm collaborate to integrate Mistrals into edge devices https://actuia.com/actualite/mistral-ai-et-qualcomm-collaborent-pour-integrer-les-ministraux-aux-appareils-en-peripherie/
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Axelera AI Enhances Workplace Safety with Metis Platform
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At Axelera AI, we're working with @Fogsphere to enhance workplace safety. Our new case study shows how Fogsphere’s solutions, powered by the Metis AI platform, support real-time monitoring and efficiency. Read more: https://
axelera.ai/showcases/acce
lerating-fogsphere-ai-for-workplace-safety
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GPU Autoscaling for Fine-tuned SLMs: Webinar Recap
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Missed our webinar? Let’s talk #GPU autoscaling for #finetuned #slms! Learn how to: Handle traffic surges effortlessly Optimize GPU costs Hit throughput SLAs https://
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User expects visual feedback from Siri interface interaction
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I kept waiting for the edges to light up when I talked to Siri. pic.twitter.com/8gqgepHyFS
— Paul Roetzer (@paulroetzer) 29 octobre 2024I kept waiting for the edges to light up when I talked to Siri.
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Meta FAIR releases Layer Skip for accelerating LLM inference
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We previously shared our research on Layer Skip, an end-to-end solution for accelerating LLMs from researchers at Meta FAIR. It achieves this by executing a subset of an LLM’s layers and utilizing subsequent layers for verification and correction. We’re now releasing inference… pic.twitter.com/gag29HSf6e
— AI at Meta (@AIatMeta) 29 octobre 2024We previously shared our research on Layer Skip, an end-to-end solution for accelerating LLMs from researchers at Meta FAIR. It achieves this by executing a subset of an LLM’s layers and utilizing subsequent layers for verification and correction. We’re now releasing inference