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Fast GPUs
Each chip can do a lot of work.
Learn
Why networking matters
Why fast interconnects turn individual chips into useful AI clusters.
Large AI workloads often require many accelerators to work together. Networking matters because those chips must exchange data quickly enough that the cluster behaves like one coordinated system instead of many expensive devices waiting on one another.
Chips need linksBasics
A cluster is useful only when its accelerators can communicate fast enough.
Slow links waste capacityConstraint
Poor networking can leave expensive GPUs underused while they wait on data.
Example
A simple way networking changes performance
Imagine a team of fast workers who must constantly hand papers to one another. If the handoff is slow, the whole team slows down even if each worker is individually fast.
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Shared workload
The chips must exchange data to act together.
3
Fast interconnect
The cluster reaches more of its real potential.
Infrastructure
What networking affects
- How efficiently many GPUs can train one large model together.
- How quickly data moves within and between systems.
- How much expensive accelerator time is spent computing versus waiting.
- Whether a site can support larger, more tightly coupled workloads.
Market context
Why networking becomes a market issue
As models and clusters grow, networking becomes part of the value of compute itself. Two providers can offer similar chips but deliver different effective capacity if the surrounding interconnect is not equally capable.
- Faster networking can improve realized performance from the same hardware base.
- Cluster design affects workload fit and buyer value.
- Networking hardware and topology can become deployment bottlenecks.
- Effective compute is not only how many GPUs exist, but how well they work together.
Common mistake
More GPUs do not automatically mean more useful compute
Adding accelerators helps only if the workload can scale across them and the network can keep them synchronized. A poorly connected cluster may deliver much less value than its chip count suggests.
Hardware
Chip count
How many accelerators are installed.
Network
Interconnect
How well they exchange data.
Output
Effective capacity
How much useful work the cluster can actually deliver.
Keep learning
Related lessons
Infrastructure
What is a data center?
The physical site where chips, power, cooling, networking, and operations come together.
Infrastructure
Why memory matters
Why high-bandwidth memory can constrain accelerator supply and model performance.
Compare
H100 vs H200 vs B200
How accelerator generations affect performance, supply, and cost.