Is the NVIDIA top in as Etched launches ASIC for LLMs 20x faster than H100 GPUs?


Etched
is

making
waves
in
the
artificial
intelligence
hardware
space
with
its
revolutionary
new
AI
accelerator
chip.
The
Silicon
Valley
startup,
founded
in
2022
by
Harvard
dropouts
Gavin
Uberti
and
Chris
Zhu,
has
developed
a
custom
application-specific
integrated
circuit
(ASIC)
called
Sohu
that
is
purpose-built
to
run
transformer
models

the
architecture
behind
today’s
most
advanced
AI
systems.

Etched
transformer
ASICS
for
LLMs

Etched
claims
its
Sohu
chip
can
process
AI
workloads
up
to
20
times
faster
than
Nvidia’s
top-of-the-line
GPUs
while
using
significantly
less
power.
With

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$120
million
in
fresh
funding
and
partnerships
with
major
cloud
providers,
Etched
is
positioning
itself
as
a
formidable
challenger
to
Nvidia’s
dominance
in
AI
chips.

Performance
of
Sohu
vs
top
GPUs
(Etched)

Primary
Venture
Partners
and
Positive
Sum
Ventures
led
the
funding
round,
which
included
participation
from
high-profile
investors
like
Peter
Thiel,
Github
CEO
Thomas
Dohmke,
and
former
Coinbase
CTO
Balaji
Srinivasan.
As
transformer
models
continue
to
drive
breakthroughs
in
generative
AI,
Etched’s
specialized
hardware
could
reshape
the
landscape
of
AI
computing.

Etched’s
approach
targets
the
complexities
of
GPUs
and
TPUs,
particularly
the
need
to
handle
arbitrary
CUDA
and
PyTorch
code,
which
demands
sophisticated
compilers.
While
other
AI
chip
developers
like
AMD,
Intel,
and
AWS
have
invested
billions
into
software
development
with
limited
success,
Etched
is
narrowing
its
focus.
By
exclusively
running
transformers,
Etched
can
streamline
software
development
for
these
models.

Most
AI
companies
use
transformer-specific
inference
libraries
such
as
TensorRT-LLM,
vLLM,
or
HuggingFace’s
TGI.
Although
somewhat
inflexible,
these
frameworks
suffice
for
most
needs
because
transformer
models
across
different
applications—text,
image,
or
video—are
fundamentally
similar.
This
allows
users
to
adjust
model
hyperparameters
without
altering
the
core
model
code.
However,
the
most
prominent
AI
labs
often
require
custom
solutions,
employing
engineers
to
optimize
GPU
kernels
meticulously.

Coinbase Banner  

Etched
aims
to
eliminate
the
need
for
reverse
engineering
by
making
its
entire
software
stack
open
source,
from
drivers
to
kernels.
This
openness
allows
engineers
to
implement
custom
transformer
layers
as
needed,
enhancing
flexibility
and
innovation.

Etched’s
approach
to
AI
hardware
is
comparable
to
the
advancements
seen
with
Groq’s
LPU
Inference
Engine.
Groq’s
LPU,
a
dedicated

Language
Processing
Unit,
has
set
new
benchmarks
in
processing
efficiency
for
large
language
models,
surpassing
traditional
GPUs
in
specific
tasks.
According
to
ArtificialAnalysis.ai,
Groq’s
LPU
achieved
a
throughput
of
241
tokens
per
second
with
Meta
AI’s
Llama
2-70b
model,
demonstrating
its
capability
to
process
large
volumes
of
more
straightforward
data
more
efficiently
than
other
solutions.

This
level
of
performance
spotlights
the
potential
for
specialized
AI
hardware
to
revolutionize
the
field
by
offering
faster
and
more
efficient
processing
capabilities
tailored
to
specific
AI
workloads.
Etched
claims
its
ASIC
achieves
as
many
as
500,000
tokens
per
token
with
its
hardware,
dwarfing
Groq’s
performance.

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ASICs
changed
the
game
for
Bitcoin;
will
they
do
the
same
for
AI?

The
introduction
of
ASICs
for

Bitcoin
mining
marked
a
revolutionary
shift
in
the
landscape,
fundamentally
altering
the
network
dynamics.
When
ASICs
were
first
introduced
in
2013,
they
represented
a
quantum
leap
in
mining
efficiency
compared
to
the
CPUs
and
GPUs
that
had
previously
dominated
the
field.
This
transition
profoundly
impacted
Bitcoin’s
ecosystem,
dramatically
increasing
the
network’s
overall
hash
rate
and,
consequently,
its
security.

ASICs,
being
purpose-built
for
Bitcoin
mining,
offered
unprecedented
computational
power
and
energy
efficiency,
quickly
rendering
CPU
and
GPU
mining
obsolete
for
Bitcoin.
This
shift
led
to
a
rapid
centralization
of
mining
power,
as
only
those
with
access
to
ASIC
hardware
could
profitably
mine
Bitcoin.
The
ASIC
era
ushered
in

industrial-scale
mining
operations,
transforming
Bitcoin
mining
from
a
hobby
accessible
to
individual
enthusiasts
into
a
highly
competitive,
capital-intensive
industry.

Etched
history
and
development

Etched’s
vision
began
in
2022
when
AI
technologies
like
ChatGPT
were
not
yet
prevalent,
and
image
and
video
generation
models
primarily
relied
on
U-Nets
and
CNNs.
Since
then,
transformers
have
become
the
dominant
architecture
across
various
AI
domains,
validating
Etched’s
strategic
focus.

The
company
is
rapidly
advancing
toward
one
of
the
quickest
chip
launches
in
history.
It
has
attracted
top
talent
from
major
AI
chip
projects,
partnered
with
TSMC
for
their
advanced
4nm
process,
and
secured
essential
resources
such
as
HBM
and
server
supply
to
support
initial
production.
Early
customers
have
already
committed
tens
of
millions
of
dollars
to
Etched’s
hardware.

This
rapid
progress
could
dramatically
accelerate
AI
capabilities.
For
instance,
AI
models
could
become
20
times
faster
and
cheaper
overnight.
Current
limitations
could
be
drastically
reduced,
such
as
the
slow
response
times
of
models
like
Gemini
or
the
high
costs
and
long
processing
times
of
coding
agents.
Real-time
applications,
from
video
generation
to
AI-driven
conversations,
could
become
feasible,
addressing
the
current

bottlenecks
faced
even
by
leading
AI
firms
like
OpenAI
during
peak
usage
periods.

Etched’s
advancements
promise
to
make
real-time
video,
calls,
agents,
and
search
a
reality,
fundamentally
transforming
AI
capabilities
and
their
integration
into
everyday
applications.

Mentioned
in
this
article

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Author: Liam ‘Akiba’ Wright


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