More information
The following documents provide further explanations of the terminologies that are relevant to the corresponding topics:
Terminology
The following terms apply to AI Hypercomputer.
Accelerator
A specialized hardware device, such as a GPU or TPU, designed to perform
specific tasks, such as machine learning workloads, more efficiently than a
general-purpose processor.
Block
A collection of sub-blocks interconnected by a non-blocking, high-speed
network that provides high bandwidth for all host machines in the block.
Cluster A collection of blocks
interconnected by a high-speed network fabric. Each cluster is globally unique.
For A4X, A4, A3 Ultra, A3 Mega, and A3 High (8 GPUs) machines, a cluster provides a common, non-blocking network
fabric for your blocks of accelerator capacity. Within a cluster, inter-node
communication (east-west
traffic) is non-blocking for the entire collection of blocks.
Clustered GPU A
category of machine families for GPUs, including A4X, A4, A3 Ultra, A3 Mega, and
A3 High (8 GPUs). Clustered GPUs let teams deploy and
scale thousands of interconnected accelerators as a single, tightly coupled
system by using specialized networking fabrics and synchronized maintenance.
They are ideal for workloads that require extreme compute, memory, and
high-speed networking synchronization. Common use cases include pre-training
foundation models with trillions of parameters, frontier model serving, and
simulations for drug discovery.
Emergent maintenance
Sometimes referred to as emergency maintenance. An unplanned
maintenance event caused by a critical hardware, software, or security issue.
For supported accelerator machine types, emergent maintenance provides an
advance notification instead of an immediate disruption. This notification gives
you time to gracefully drain your workloads and optionally trigger the repair
before the system terminates the VMs.
General GPU A
category of GPU machine families, including G2, G4, A2, N1+T4, and A3 Edge.
General GPUs let teams deploy and scale NVIDIA
accelerators with complete operational autonomy, bypassing the architectural
complexity of coordinated supercomputing clusters. They are ideal for workloads
that prioritize high availability, self-service provisioning, and independent
scaling. Common use cases include real-time inference, RAG, prototyping, and
small-to-medium model training.
Network fabric High-speed network
infrastructure that provides high bandwidth and low latency across all blocks
and Google Cloud services in a cluster. The fabric uses the architecture of the
Jupiter
network. To optimize performance, the fabric uses software-defined network and
optical switches.
Node or host
A single physical server machine in the data center. Each host has
associated compute resources, such as accelerators. The number and
configuration of these compute resources depend on the machine family.
Compute Engine instances are provisioned on top of a physical
host.
NVLink domain
An NVLink domain, also referred to as a sub-block, is the core unit of
capacity for A4X Max and A4X machines. An NVLink domain consists of 18 A4X Max
or A4X instances (72 GPUs) that are connected by a multi-node NVLink
system.
Sub-block
A group of hosts and associated connectivity hardware that are on a single
physical block. In the context of A4X Max and A4X machines, a sub-block is also
referred to as an NVLink domain.
Superblock
A physical grouping of interconnected machines within data centers. By placing
machines inside a superblock, you achieve the extreme network throughput and
sub-millisecond latency required to execute tightly coupled training workloads.