What 1,000 H100s mean for io.net: GPUs vs CPUs and training capacity
Quote471 words
Explains GPU versus CPU architecture and what the H100 is, then sizes the 1,000 H100 addition to io.net: about 18.4M CUDA cores and 80TB of memory, enough to train models in the hundreds of billions of parameters. Notes GPT-3 training on 256 A100s took weeks.
Key claims
Each H100 has 18,432 CUDA cores, 18.4M across 1,000 GPUs
80GB per GPU gives 80TB total
Cluster can train models with hundreds of billions of parameters
1'000 H100s coming to @ionet! 🤖✨
That's pretty big. But let's be honest, you have no clue why and what that actually means. So let's unpack this. ⤵️
🧠 What are GPUs?
GPUs are specialized hardware designed to accelerate rendering of images and complex calculations.
Initially used for rendering graphics in video games, their parallel processing capabilities make them highly effective for computational tasks in AI, deep learning, and scientific simulations.
🧠 Is this the same as CPUs?
No. CPUs (Central Processing Units) are built differently and used to handle distinct tasks:
✦ Architecture: GPUs have many small cores for parallel tasks; CPUs have fewer, powerful cores for sequential tasks.
✦ Performance: GPUs excel in AI and graphics rendering; CPUs are essential for general computing.
✦ Memory: GPUs use HBM for speed; CPUs use traditional system memory and cache.
✦ Use Cases: CPUs are better for single-threaded tasks and running software applications, while GPUs are ideal for large-scale computations like AI and deep learning.
🧠 What is a "H100"?
The NVIDIA H100 is a GPU and part of NVIDIA's Hopper architecture, designed for advanced AI and high-performance computing tasks.
The H100's powerful Tensor Cores and parallel processing capabilities accelerate the training of complex AI models, reducing the time required for model development.
The high throughput and memory efficiency also make it ideal for performing inference, where the already trained model is used to make predictions on new data.
🧠 What does this mean for IO Network?
Is 1000 H100s a lot? Can you even run anything with that compute power?
The answer is yes.
But before we put that into context, let's have a look at what adding 1000 H100 GPUs to the network actually means:
✦ CUDA Cores: Each H100 GPU has 18,432 CUDA cores.
✦ Total Cores: 18,432,000 CUDA cores (1,000 GPUs combined).
✦ Memory per GPU: Each H100 GPU has up to 80GB of HBM2e.
✦ Total Memory: 80TB of high-speed memory across 1,000 GPUs.
That sounds like a lot, right?
Well, it is.
A cluster of 1000 H100 GPUs is capable of training models with hundreds of billions of parameters.
For instance, GPT-3 has 175 billion parameters, and training such models typically requires clusters of high-end GPUs.
More specifically, training GPT-3 on a cluster of 256 A100 GPUs (Ampere architecture, pre-Hopper) took weeks.
With H100 GPUs, this time could be reduced considerably due to higher throughput and efficiency.
A cluster of 1000 H100 nodes can run inference for thousands of smaller models simultaneously.
For example, a single H100 GPU can handle inference for multiple instances of models with >100 million parameters in parallel.
Thanks to the concurrent task handling, this type of GPU infra supports diverse AI applications without significant performance bottlenecks.
You positioned on @ionet anon? Or what is your AI/GPU play for the cycle? 👀
QUOTING @ionet
Expanding verified, enterprise grade GPUs in partnership with @AethirCloud. 1,000 H100s are coming to @ionet t.co/Mp4pqNYmr0
Expanding verified, enterprise grade GPUs in partnership with @AethirCloud. 1,000 H100s are coming to @ionet t.co/Mp4pqNYmr0
What came next · since 15 May 2024
Since then
Zero has written 39 more pieces on io.net since this one.