NVIDIA NIM Microservices Enhance LLM Inference Efficiency at Scale

As
large
language
models
(LLMs)
continue
to
evolve
at
an
unprecedented
pace,
enterprises
are
increasingly
focused
on
building
generative
AI-powered
applications
that
maximize
throughput
and
minimize
latency,
according
to
the

NVIDIA
Technical
Blog.
These
optimizations
are
crucial
for
lowering
operational
costs
and
delivering
superior
user
experiences.

Key
Metrics
for
Measuring
Cost
Efficiency

When
a
user
sends
a
request
to
an
LLM,
the
system
processes
this
request
and
generates
a
response
by
outputting
a
series
of
tokens.
Multiple
requests
are
often
handled
simultaneously
to
minimize
wait
times.

Throughput
measures
the
number
of
successful
operations
per
unit
of
time,
such
as
tokens
per
second,
which
is
critical
for
determining
how
well
enterprises
can
handle
user
requests
concurrently.

Latency,
measured
by
time
to
first
token
(TTFT)
and
inter-token
latency
(ITL),
indicates
the
delay
before
or
between
data
transfers.
Lower
latency
ensures
a
smooth
user
experience
and
efficient
system
performance.
TTFT
measures
the
time
it
takes
for
the
model
to
generate
the
first
token
after
receiving
a
request,
while
ITL
refers
to
the
interval
between
generating
consecutive
tokens.

Balancing
Throughput
and
Latency

Enterprises
must
balance
throughput
and
latency
based
on
the
number
of
concurrent
requests
and
the
latency
budget,
which
is
the
acceptable
amount
of
delay
for
an
end
user.
Increasing
the
number
of
concurrent
requests
can
enhance
throughput
but
may
also
raise
latency
for
individual
requests.
Conversely,
maintaining
a
set
latency
budget
can
maximize
throughput
by
optimizing
the
number
of
concurrent
requests.

As
the
number
of
concurrent
requests
rises,
enterprises
can
deploy
more
GPUs
to
sustain
throughput
and
user
experience.
For
instance,
a
chatbot
handling
a
surge
in
shopping
requests
during
peak
times
would
require
several
GPUs
to
maintain
optimal
performance.

How
NVIDIA
NIM
Optimizes
Throughput
and
Latency

NVIDIA
NIM
microservices
offer
a
solution
to
maintain
high
throughput
and
low
latency.
NIM
optimizes
performance
through
techniques
such
as
runtime
refinement,
intelligent
model
representation,
and
tailored
throughput
and
latency
profiles.
NVIDIA
TensorRT-LLM
further
enhances
model
performance
by
adjusting
parameters
like
GPU
count
and
batch
size.

NIM,
part
of
the
NVIDIA
AI
Enterprise
suite,
undergoes
extensive
tuning
to
ensure
high
performance
for
each
model.
Techniques
like
Tensor
Parallelism
and
in-flight
batching
process
multiple
requests
in
parallel,
maximizing
GPU
utilization
and
boosting
throughput
while
reducing
latency.

NVIDIA
NIM
Performance

Using
NIM,
enterprises
have
reported
significant
improvements
in
throughput
and
latency.
For
example,
the
NVIDIA
Llama
3.1
8B
Instruct
NIM
achieved
a
2.5x
increase
in
throughput,
a
4x
faster
TTFT,
and
a
2.2x
faster
ITL
compared
to
the
best
open-source
alternatives.
A
live
demo
showed
that
NIM
On
produced
outputs
2.4x
faster
than
NIM
Off,
demonstrating
the
efficiency
gains
provided
by
NIM’s
optimized
techniques.

NVIDIA
NIM
sets
a
new
standard
in
enterprise
AI,
offering
unmatched
performance,
ease
of
use,
and
cost
efficiency.
Enterprises
looking
to
enhance
customer
service,
streamline
operations,
or
innovate
within
their
industries
can
benefit
from
NIM’s
robust,
scalable,
and
secure
solutions.

Image
source:
Shutterstock