Optimizing GPU Clusters for Generative AI Model Training: A Comprehensive Guide

Training
generative
AI
models
requires
clusters
of
expensive,
cutting-edge
hardware
such
as
H100
GPUs
and
fast
storage,
interconnected
through
multi-network
topologies
involving
Infiniband
links,
switches,
transceivers,
and
ethernet
connections.
While
high-performance
computing
(HPC)
and
AI
cloud
services
offer
these
specialized
clusters,
they
come
with
substantial
capital
commitments.
However,
not
all
clusters
are
created
equal,
according
to

together.ai.

Introduction
to
GPU
Cluster
Testing

Reliability
of
GPU
clusters
varies
significantly,
with
issues
ranging
from
minor
to
critical.
For
instance,
Meta
reported
that
during
their
54-day
training
run
of
the
Llama
3.1
model,
GPU
issues
accounted
for
58.7%
of
all
unexpected
problems.
Together
AI,
serving
many
AI
startups
and
Fortune
500
companies,
has
developed
a
robust
validation
framework
to
ensure
hardware
quality
before
deployment.

The
Process
of
Testing
Clusters
at
Together
AI

The
goal
of
acceptance
testing
is
to
ensure
that
hardware
infrastructure
meets
specified
requirements
and
delivers
the
reliability
and
performance
necessary
for
demanding
AI/ML
workloads.

1.
Preparation
and
Configuration

The
initial
phase
involves
configuring
new
hardware
in
a
GPU
cluster
environment,
mimicking
end-use
scenarios.
This
includes
installing
NVIDIA
drivers,
OFED
drivers
for
Infiniband,
CUDA,
NCCL,
HPCX,
and
configuring
SLURM
cluster
and
PCI
settings
for
performance.

2.
GPU
Validation

Validation
begins
with
ensuring
the
GPU
type
and
count
match
expectations.
Stress
testing
tools
like

DCGM
Diagnostics
and

gpu-burn
are
used
to
measure
power
consumption
and
temperature
under
load.
These
tests
help
identify
issues
like
NVML
driver
mismatches
or
“GPU
fell
off
the
bus”
errors.

3.
NVLink
and
NVSwitch
Validation

After
individual
GPU
validation,
tools
like
NCCL
tests
and
nvbandwidth
measure
GPU-to-GPU
communication
over
NVLink.
These
tests
help
diagnose
problems
like
a
bad
NVSwitch
or
down
NVLinks.

4.
Network
Validation

For
distributed
training,
network
configuration
is
validated
using
Infiniband
or
RoCE
networking
fabrics.
Tools
like
ibping,
ib_read_bw,
ib_write_bw,
and
NCCL
tests
are
used
to
ensure
optimal
performance.
A
good
result
in
these
tests
indicates
the
cluster
will
perform
well
for
distributed
training
workloads.

5.
Storage
Validation

Storage
performance
is
crucial
for
machine
learning
workloads.
Tools
like

fio
measure
different
storage
configurations’
performance
characteristics,
including
random
reads,
random
writes,
sustained
reads,
and
sustained
writes.

6.
Model
Build

The
final
phase
involves
running
reference
tasks
tailored
to
customer
use
cases.
This
ensures
the
cluster
can
achieve
expected
end-to-end
performance.
A
popular
task
is
building
a
model
with
frameworks
like
PyTorch’s
Fully
Sharded
Data
Parallel
(FSPD)
to
evaluate
training
throughput,
model
flops
utilization,
GPU
utilization,
and
network
communication
latencies.

7.
Observability

Continuous
monitoring
for
hardware
failures
is
essential.
Together
AI
uses

Telegraf
to
collect
system
metrics,
ensuring
maximum
uptime
and
reliability.
Monitoring
includes
cluster-level
and
host-level
metrics,
such
as
CPU/GPU
usage,
available
memory,
disk
space,
and
network
connectivity.

Conclusion

Acceptance
testing
is
indispensable
for
AI/ML
startups
delivering
top-tier
computational
resources.
A
comprehensive
and
structured
approach
ensures
stable
and
reliable
infrastructure,
supporting
the
intended
GPU
workloads.
Companies
are
encouraged
to
run
acceptance
testing
on
delivered
GPU
clusters
and
report
any
issues
for
troubleshooting.

Image
source:
Shutterstock