Introducing the 20 Article "AI Supercluster" Series
What's This All About?
Buckle up for a 20-article journey from GPU to supercluster, exploring the world of training trillion-parameter AI models, grounded in NVIDIA hardware and software. Our mission? Decoding AI supercomputers at a systems level, using AI training as the primary HPC workload to comprehend.
Skip to the end if you just want to check out the titles of the 20 articles.
Why This Series Exists
Ever tried finding just-right technical info? It's usually either fluff or in the weeds. This series bridges that gap, catering to the tech-savvy & curious who aren't necessarily experts.
Who's This For?
Tech-savvy leaders - the essential overhead who need to speak AI engineer. We're looking at you:
Product managers
Program managers
Marketing managers
Sales and account managers
Engineers from other domains
Investors
Analysts
Nerds
Fair warning: while we won't assume you're an AI guru, a high-level grasp of computing and AI is your ticket to ride.
A Confession and Invitation
Full disclosure: I'm no AI guru or supercomputing wizard. Just a curious dude deep-diving into AI supercomputing.
I've wrangled my almost-AGI buddies (ChatGPT, Gemini, Claude), using my experience as an ex-product manager to guide my questioning, gut-check the answers, and format in a way that’s the most straight-forward to me.
Spot an error? Have insights? Let me know. Your input will help make this resource more valuable.
Why NVIDIA?
You'll see a lot of NVIDIA gear in this series. No, I'm not an NVIDIA fanboy. I'm a Jensen fanboy. Plus, talking specific products grounds us in hardware reality better than abstract concepts. Don't worry—the principles apply broadly. And in the future, I may devote articles to AMD, Amazon Trainium, and Google TPU architectures.
What You'll Learn
By journey's end, you'll be able to:
Understand what makes an AI supercomputer tick (like a clock, not a bomb)
See how hardware, software, and networking play together for training massive models
Grasp the challenges of scaling AI compute
The 20-Part Journey
Here’s our itinerary:
Section I: Node (Articles 1-7)
Section II: Cluster (Articles 8-12)
Section III: Supercluster (Articles 13-20)
Section I: Node
NVIDIA GPU Architecture & Evolution (Hopper H100, Blackwell B200, GH200, and GB200)
NVIDIA DGX Reliability, Availability, and Serviceability (RAS)
NVIDIA DGX Platform Evolution (From DGX H100 to DGX B200 to GB200 NVL72)
Section II: Cluster
Traversing the Network Fabric (NVLink, NVSwitch, ConnectX SmartNIC, InfiniBand, RDMA)
Multi-Node Computing (Advanced CUDA and NCCL)
Section III: Supercluster
Each article: a digestible 4-5 pages. We're talking system-level framework, not technician-level or MLOps minutiae. Think big picture, not nuts and bolts.
Eager to dive in? We'll be publishing at least once a week until we wrap this grand tour of AI supercomputing.
Are you ready? Let's kick off with Article 1, “AI Supercluster: Overview”
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