A large model computing foundation must be designed full-stack, from chips to cloud platform
Why does machine learning place special demands on hardware? Why does a cluster of servers, once stacked together, still fail to run training? Starting from simple linear regression, this book moves layer by layer through chips, servers, networking, storage, virtualization, and cloud platforms, answering how each layer of an intelligent computing center should be designed — and why.
The Construction Path
Key Concepts
GPU architecture
The chip system and server design built on massively parallel cores to serve deep learning.
It is the physical origin of computing power; understanding the architecture is how you size capacity and find bottlenecks.
Compute network
A low-latency high-speed network, often RoCE-based, designed for horizontal scaling of GPU clusters.
Communication is the bottleneck of thousand-card collaboration; network design decides cluster linearity.
Tiered storage
Layered storage and network design for training data, checkpoints, and business data.
If storage throughput lags, all the GPUs idle waiting for data.
Virtualization
GPU pooling and compute partitioning that make heterogeneous computing schedulable and shareable.
It turns physical computing into operable resources — the precondition of a platform.
Cloud-native platform
A machine learning development and runtime platform built on microservices, middleware, and logging.
Computing power pays off only when developers can use it easily.
Map of the Book
Part One: Foundation and Core Components
Part Two: Cluster Construction and Virtualization
Part Three: Application Platform and Operations
Part Four: Practical Cases and Summary
After reading, you will understand
- Design from the essential needs of machine learning
- Networking is the first bottleneck of a thousand-card cluster
- Storage throughput must match compute scale
- Virtualization makes computing operable and shareable
- Test full-stack design against a real case