BTW, I have an entire thread with all of my educational content on Local AI Covers everything from Software to Hardware to Infrastructure & Systems Design for running AI locally You absolutely should check it out as well for the bigger picture
HARDWARE
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Free online bible for running LLMs locally on any hardware
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DROP EVERYTHING The bible for running LLMs locally is now available online to read for free Covers what to use on – Laptop / edge / odd hardware
– Mac-first workflows
– Single RTX GPUs
– 2-4+ NVIDIA / CUDA GPUs
– General production serving
– Long-context / MoE / routing
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Anthropic gets up to GB200 capacity at Colossus 2 with SpaceX
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Anthropic SpaceX Anthropic is getting up to GB200 of capacity in Colossus 2 in June as a part of the expanded agreement with SpaceX. Partnerships are huge unlocks
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University of Innsbruck uses AI to design better quantum circuits
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Designing better quantum circuits with #AI
by University of Innsbruck @TechXplore_com Learn more: https://
bit.ly/4965hQ4 #QuantumComputing #ArtificialIntelligence #MachineLearning #ML -
Hugging Face launches real-world open-source AI hardware stats
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What hardware actually powers open-source AI? Not benchmarks.
Not vendor marketing.
Real-world community usage. We’re launching @huggingface Hardware:
→ trending GPUs & CPUs
→ VRAM distribution
→ inference hardware trends
→ what the OSS AI ecosystem really runs on -
Who controls AI infrastructure and data? Dell AI Factory
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One of the biggest enterprise AI questions right now is simple: who controls the infrastructure and the data? That’s why this matters. Bringing @MistralAI models into the Dell AI Factory with NVIDIA gives enterprises more control over how they train, deploy, and scale AI without
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Scaling AI from 80s to 2000s: Compute Not Enough
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Danny Hillis was scaling up AI with a massively parallel supercomputer in the 80s. In the 90s we had the data mining explosion, a.k.a. scaling up ML. In the 2000s we had the "big data" boom. And each time we noticed that no, compute etc. is not enough – you really need better
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AI ambition easy, execution hard: Dell AI Factory with NVIDIA
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AI ambition is easy. AI execution is the hard part. A lot of companies have ideas, pilots, and demos. Far fewer have the infrastructure, governance, and operational readiness to deploy AI at scale. That’s why the Dell AI Factory with NVIDIA conversation matters: moving
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AI races: top contenders in models, data centers, chips
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The three AI races and their top contenders:
Models: OpenAI, Anthropic, Google
Data centers: Amazon, Microsoft, Google
Chips: Nvidia, AMD, Google
