NVIDIA and Oracle Enhance AI Workloads with NIM and cuVS Integration

The
vast
majority
of
the
world’s
data
remains
untapped,
and
enterprises
are
looking
to
generate
value
from
this
data
by
creating
the
next
wave
of
generative
AI
applications.
According
to
the
NVIDIA
Technical
Blog,
retrieval-augmented
generation
(RAG)
pipelines
are
a
key
part
of
this,
enabling
users
to
interact
with
large
corpuses
of
data,
transforming
documents
into
interactive
AI
applications.

Challenges
in
Implementing
RAG
Pipelines

Enterprises
face
several
challenges
when
implementing
RAG
pipelines.
Handling
both
structured
and
unstructured
data
is
complex,
and
processing
and
retrieving
data
is
computationally
intensive.
Additionally,
privacy
and
security
must
be
integrated
into
these
pipelines.

To
address
these
issues,
NVIDIA
and
Oracle
have
collaborated
to
demonstrate
how
various
segments
of
the
RAG
pipeline
can
utilize
the
NVIDIA
accelerated
computing
platform
on
Oracle
Cloud
Infrastructure
(OCI).
This
integration
aims
to
help
enterprises
better
leverage
their
data,
enhancing
the
quality
and
reliability
of
generative
AI
outputs.

Embedding
Generation
with
NVIDIA
GPUs
and
Oracle
Autonomous
Database

In
data-rich
enterprise
environments,
harnessing
large
amounts
of
text
data
for
generative
AI
is
crucial
for
enhancing
efficiency
and
productivity.
NVIDIA
and
Oracle
have
demonstrated
how
customers
can
access
NVIDIA
GPUs
through
Oracle
Machine
Learning
(OML)
Notebooks
in
Autonomous
Database.
This
allows
users
to
load
data
directly
from
an
Oracle
Database
table
into
an
OCI
NVIDIA
GPU-accelerated
virtual
machine
(VM)
instance,
generate
vector
embeddings
using
the
GPU,
and
store
those
vectors
in
Oracle
Database
for
efficient
searching
using
AI
Vector
Search.

Accelerated
Vector
Search
Indexes
and
Oracle
Database
23ai

NVIDIA
cuVS
is
an
open-source
library
for
GPU-accelerated
vector
search
and
clustering.
A
key
capability
of
cuVS
is
its
ability
to
significantly
improve
index
build
time,
a
crucial
component
of
vector
search.
NVIDIA
and
Oracle
have
demonstrated
a
proof
of
concept
that
accelerates
vector
index
builds
for
the
Hierarchical
Navigable
Small
World
(HNSW)
algorithm.
This
approach
pairs
GPUs
with
CPUs,
resulting
in
faster
index
generation
and
improved
performance
for
AI
workloads.

Performant
LLM
Inference
with
NIM
on
OCI

NVIDIA
NIM
provides
containers
to
self-host
GPU-accelerated
inferencing
microservices
for
pretrained
and
customized
AI
models
across
various
environments.
NIM
microservices
are
designed
for
NVIDIA-accelerated
infrastructure,
enabling
smooth
integration
with
existing
tools
and
applications.
Developers
can
quickly
deploy
LLMs
with
minimal
code,
whether
on-premises
or
in
Kubernetes-managed
cloud
environments.

Deploying
NVIDIA
NIM
on
OCI
offers
several
benefits,
including
improved
total
cost
of
ownership
(TCO)
with
low-latency,
high-throughput
inference,
faster
time
to
market
with
prebuilt
microservices,
and
enhanced
security
and
control
of
applications
and
data.

For
the
Oracle
CloudWorld
demonstration,
NVIDIA
and
Oracle
showcased
how
using
NIM
for
LLMs
can
achieve
higher
throughput
compared
to
off-the-shelf
open-source
alternatives,
particularly
in
text
generation
and
translation
use
cases.

Get
Started

NVIDIA
has
partnered
with
the
OCI
and
Oracle
Database
teams
to
demonstrate
how
bulk
generation
of
vector
embeddings,
HNSW
index
creation,
and
inferencing
elements
can
be
accelerated
using
NVIDIA
GPUs
and
software.
This
approach
helps
organizations
leverage
the
performance
gains
available
from
the
NVIDIA
accelerated
computing
platform,
enabling
them
to
utilize
AI
to
manage
the
vast
amounts
of
data
stored
in
Oracle
databases.

Learn
more
about

cuVS.
To
try
NVIDIA
NIM,
visit

ai.nvidia.com
and
sign
up
for
the
NVIDIA
Developer
Program
to
gain
instant
access
to
the
microservices.
You
can
also
start
using
NVIDIA
GPU-enabled
notebooks
on
Autonomous
Database
and
Oracle
Database
23ai
AI
Vector
Search
with
Oracle
Database
23ai
Free.

Image
source:
Shutterstock