Hpc7g instances featuring new AWS Graviton3E
chips deliver the best price performance for HPC workloads on
Amazon EC2
C7gn instances featuring new AWS Nitro Cards
with enhanced networking offer the highest network bandwidth and
packet rate performance across Amazon EC2 network-optimized
instances
Inf2 instances powered by new AWS Inferentia2
chips deliver the lowest latency at the lowest cost on Amazon EC2
for running the largest deep learning models at scale
At AWS re:Invent, Amazon Web Services, Inc. (AWS), an
Amazon.com, Inc. company (NASDAQ: AMZN), today announced three new
Amazon Elastic Compute Cloud (Amazon EC2) instances powered by
three new AWS-designed chips that offer customers even greater
compute performance at a lower cost for a broad range of workloads.
Hpc7g instances, powered by new AWS Graviton3E chips, offer up to
2x better floating-point performance compared to current generation
C6gn instances and up to 20% higher performance compared to current
generation Hpc6a instances, delivering the best price performance
for high performance computing (HPC) workloads on AWS. C7gn
instances, featuring new AWS Nitro Cards, offer up to 2x the
network bandwidth and up to 50% higher packet-processing-per-second
performance compared to current generation networking-optimized
instances, delivering the highest network bandwidth, the highest
packet rate performance, and the best price performance for
network-intensive workloads. Inf2 instances, powered by new AWS
Inferentia2 chips, are purpose built to run the largest deep
learning models with up to 175 billion parameters and offer up to
4x the throughput and up to 10x lower latency compared to
current-generation Inf1 instances, delivering the lowest latency at
the lowest cost for machine learning (ML) inference on Amazon
EC2.
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AWS has a decade of experience designing chips developed for
performance and scalability in the cloud at a lower cost. In that
time, AWS has introduced specialized chip designs, which make it
possible for customers to run even more demanding workloads with
varying characteristics that require faster processing, higher
memory capacity, faster storage input/output (I/O) and increased
networking bandwidth. Since the introduction of the AWS Nitro
System in 2013, AWS has developed multiple AWS-designed silicon
innovations, including five generations of the Nitro System, three
generations of Graviton chips optimized for performance and cost
for a wide range of workloads, two generations of Inferentia chips
for ML inference, and Trainium chips for ML training. AWS uses
cloud-based electronic design automation as part of an agile
development cycle for the design and verification of AWS-designed
silicon, enabling teams to innovate faster and make chips available
to customers more quickly. AWS has demonstrated that it can deliver
a new chip based on a more modern, power-efficient silicon process
at a predictable and rapid pace. With each successive chip, AWS
delivers a step function improvement in performance, cost, and
efficiency to the Amazon EC2 instances hosting them, giving
customers even more choice of chip and instance combinations
optimized for their unique workload requirements.
“Each generation of AWS-designed silicon—from Graviton to
Trainium and Inferentia chips to Nitro Cards—offers increasing
levels of performance, lower cost, and power efficiency for a
diverse range of customer workloads,” said David Brown, vice
president of Amazon EC2 at AWS. “That consistent delivery, combined
with our customers’ abilities to achieve superior price performance
using AWS silicon, drives our continued innovation. The Amazon EC2
instances we’re introducing today offer significant improvements
for HPC, network-intensive, and ML inference workloads, giving
customers even more instances to choose from to meet their specific
needs.”
Hpc7g instances are purpose built to offer the best price
performance for running HPC workloads at scale on Amazon
EC2
Organizations across numerous sectors rely on HPC to solve their
most complex academic, scientific, and business problems. Today,
customers like AstraZeneca, Formula 1, and Maxar Technologies run
conventional HPC workloads like genomics processing, computational
fluid dynamics (CFD), and weather forecasting simulations on AWS to
take advantage of the superior security, scalability, and
elasticity it offers. Engineers, researchers, and scientists run
their HPC workloads on Amazon EC2 network-optimized instances
(e.g., C5n, R5n, M5n, and C6gn) that deliver virtually unlimited
compute capacity and high levels of network bandwidth between
servers that process and exchange data across thousands of cores.
While the performance of these instances is sufficient for most HPC
use cases today, emerging applications such as artificial
intelligence (AI) and autonomous vehicles require HPC-optimized
instances that can further scale to solve increasingly difficult
problems and reduce the cost of HPC workloads, which can scale to
tens of thousands of cores or more.
Hpc7g instances powered by new AWS Graviton3E processors offer
the best price performance for customers’ HPC workloads (e.g., CFD,
weather simulations, genomics, and molecular dynamics) on Amazon
EC2. Hpc7g instances provide up to 2x better floating-point
performance compared to current generation C6gn instances powered
by Graviton2 processors and up to 20% higher performance compared
to current generation Hpc6a instances, enabling customers to carry
out complex calculations across HPC clusters up to tens of
thousands of cores. Hpc7g instances also provide high-memory
bandwidth and 200 Gbps of Elastic Fabric Adapter (EFA) network
bandwidth to achieve faster time to results for HPC applications.
Customers can use Hpc7g instances with AWS ParallelCluster, an
open-source cluster management tool, to provision Hpc7g instances
alongside other instance types, giving customers the flexibility to
run different workload types within the same HPC cluster. For more
information on HPC on AWS, visit aws.amazon.com/hpc.
C7gn instances offer the best performance for
network-intensive workloads with higher networking bandwidth,
greater packet rate performance, and lower latency
Customers use Amazon EC2 network-optimized instances to run
their most demanding network-intensive workloads like network
virtual appliances (e.g., firewalls, virtual routers, and load
balancers) and data encryption. Customers need to scale the
performance of these workloads to handle increasing network traffic
in response to spikes in activity, or to decrease processing time
to deliver a better experience to their end users. Today, customers
use larger instance sizes to get more network throughput, deploying
more compute resources than required, which increases costs. These
customers need increased packet-per-second performance, higher
network bandwidth, and faster cryptographic performance to reduce
data processing times.
C7gn instances, featuring new AWS Nitro Cards powered by new,
fifth generation Nitro chips with network acceleration, offer the
highest network bandwidth and packet-processing performance across
Amazon EC2 network-optimized instances, while using less power.
Nitro Cards offload and accelerate I/Ofor functions from the host
CPU to specialized hardware to deliver practically all of an Amazon
EC2 instance’s resources to customer workloads for more consistent
performance with lower CPU utilization. New AWS Nitro Cards enable
C7gn instances to offer up to 2x the network bandwidth and up to
50% higher packet-processing-per-second performance, and reduced
Elastic Fabric Adapter (EFA) network latency compared to current
generation networking-optimized Amazon EC2 instances. C7gn
instances deliver up to 25% better compute performance and up to 2x
faster performance for cryptographic workloads compared to C6gn
instances. Fifth generation Nitro Cards also offer 40% better
performance per watt compared to fourth generation Nitro Cards,
lowering power consumption for customer workloads. C7gn instances
let customers scale for both performance and throughput and reduced
network latency to optimize the cost of their most demanding,
network-intensive workloads on Amazon EC2. C7gn instances are
available today in preview. To learn more about C7gn instances,
visit aws.amazon.com/ec2/instance-types/c7g.
Inf2 instances are purpose-built to serve today’s most
demanding deep learning model deployments, with support for
distributed inference and stochastic rounding
In response to demand for better applications and even more
tailored personalized experiences, data scientists and ML engineers
are building larger, more complex deep learning models. For
example, large language models (LLMs) with more than 100 billion
parameters are increasingly prevalent, but they train on enormous
amounts of data, driving unprecedented growth in compute
requirements. While training receives a lot of attention, inference
accounts for the majority of complexity and cost (i.e., for every
$1 spent on training, up to $9 is spent on inference) of running
machine learning in production, which can limit its use and stall
customer innovation. Customers want to use state-of-the-art deep
learning models in their applications at scale, but they are
constrained by high compute costs. When AWS launched Inf1 instances
in 2019, deep learning models were millions of parameters. Since
then, the size and complexity of deep learning models have grown
exponentially with some deep learning models exceeding hundreds of
billions of parameters—a 500x increase. Customers working on
next-generation applications using the latest advancements in deep
learning want cost-effective, energy-efficient hardware that
supports low latency, high throughput inference, with flexible
software that enables engineering teams to quickly deploy their
latest innovations at scale.
Inf2 instances, powered by new Inferentia2 chips, support large
deep learning models (e.g., LLMs, image generation, and automated
speech detection) with up to 175 billion parameters, while
delivering the lowest cost per inference on Amazon EC2. Inf2 is the
first inference-optimized Amazon EC2 instance that supports
distributed inference, a technique that spreads large models across
several chips to deliver the best performance for deep learning
models with more than 100 billion parameters. Inf2 instances
support stochastic rounding, a way of rounding probabilistically
that enables high performance and higher accuracy as compared to
legacy rounding modes. Inf2 instances support a wide range of data
types including CFP8, which improves throughput and reduces power
per inference, and FP32, which boosts performance of modules that
have not yet taken advantage of lower precision data types.
Customers can get started with Inf2 instances using AWS Neuron, the
unified software development kit (SDK) for ML inference. AWS Neuron
is integrated with popular ML frameworks like PyTorch and
TensorFlow to help customers deploy their existing models to Inf2
instances with minimal code changes. Since splitting large models
across several chips requires fast inter-chip communication, Inf2
instances support AWS’s high-speed, intra-instance interconnect,
NeuronLink, offering 192 GB/s of ring connectivity. Inf2 instances
offer up to 4x the throughput and up to 10x lower latency compared
to current-generation Inf1 instances, and they also offer up to 45%
better performance per watt compared to GPU-based instances. Inf2
instances are available today in preview. To learn more about Inf2
instances, visit aws.amazon.com/ec2/instance-types/inf2.
The Water Institute is an independent, non-profit applied
research organization that works across disciplines to advance
science and develop integrated methods used to solve complex
environmental and societal challenges. “The ability to make
accurate, near-real-time numerical weather predictions to aid
decision making is important to our clients. We’re excited to see
Amazon EC2’s high performance computing offerings continue to
evolve with the launch of Amazon EC2 Hpc7g instances,” said Zach
Cobell, research engineer at The Water Institute. “With increased
floating-point performance, higher efficiency using AWS Graviton3E
processors, based on Arm architecture, and decreased inter-node
latency using Elastic Fabric Adapter, we expect to continue to be
able to deliver innovative and sustainable solutions across our
computational portfolio.”
Arup is a global collective of designers, engineering and
sustainability consultants, advisors and experts dedicated to
sustainable development and to using imagination, technology and
rigour to shape a better world. “We use AWS to run highly complex
simulations to help our customers to build the next generation of
high-rise buildings, stadiums, data-centres, and crucial
infrastructure, along with assessing and providing insight into
urban microclimates, global warming, and climate change that
impacts the lives of so many people around the world,” said Dr.
Sina Hassanli, senior engineer at Arup. “Our customers are
constantly demanding faster, more accurate simulations at a lower
cost to inform their designs at the early stages of development,
and we are already anticipating how the introduction of Amazon EC2
Hpc7g instances with higher performance will help our customers
innovate faster and more efficiently.”
HAProxy Technologies is the company behind HAProxy, the world’s
fastest and most widely-used software load balancer. "HAProxy
powers modern application delivery at any scale and in any
environment, providing the utmost performance, observability, and
security for some of the most popular websites in the world,” said
Willy Tarreau, lead developer at HAProxy. “When HAProxy tested
Amazon EC2 C6gn instances, we found unprecedented performance for a
software load balancer. We are excited about the new C7gn instances
with Graviton3E and fifth generation AWS Nitro Cards and the
networking performance improvements they will bring to our
customers.”
Aerospike Inc.'s real-time data platform is designed for
organizations to build applications that fight fraud, enable global
digital payments, deliver hyper-personalized user experiences to
tens of millions of customers, and more. “The Aerospike Real-time
Data Platform is a shared-nothing, multithreaded, multimodal data
platform designed to operate efficiently on a cluster of server
nodes, exploiting modern hardware and network technologies to drive
reliably fast performance at sub-millisecond speeds across
petabytes of data,” said Lenley Hensarling, chief product officer
at Aerospike. “In our recent real-time database read tests, we were
pleased to see a significant improvement in transactions per second
on Amazon EC2 C7gn instances featuring new AWS Nitro Cards compared
to C6gn instances. We look forward to taking advantage of C7gn
instances and future AWS infrastructure improvements as they become
available.”
Qualtrics designs and develops experience management software.
“At Qualtrics, our focus is building technology that closes
experience gaps for customers, employees, brands, and products. To
achieve that, we are developing complex multi-task, multi-modal
deep learning models to launch new features, such as text
classification, sequence tagging, discourse analysis, key-phrase
extraction, topic extraction, clustering, and end-to-end
conversation understanding,” said Aaron Colak, head of Core Machine
Learning at Qualtrics. “As we utilize these more complex models in
more applications, the volume of unstructured data grows, and we
need more performant inference-optimized solutions that can meet
these demands, such as Inf2 instances, to deliver the best
experiences to our customers. We are excited about the new Inf2
instances, because it will not only allow us to achieve higher
throughputs, while dramatically cutting latency, but also
introduces features like distributed inference and enhanced dynamic
input shape support, which will help us scale to meet the
deployment needs as we push towards larger, more complex large
models.”
Finch Computing is a natural language technology company
providing artificial intelligence applications for government,
financial services, and data integrator clients. “To meet our
customers’ needs for real-time natural language processing, we
develop state-of-the-art deep learning models that scale to large
production workloads. We have to provide low-latency transactions
and achieve high throughputs to process global data feeds. We
already migrated many production workloads to Inf1 instances and
achieved an 80% reduction in cost over GPUs,” said Franz Weckesser,
chief architect at Finch Computing. “Now, we are developing larger,
more complex models that enable deeper, more insightful meaning
from written text. A lot of our customers need access to these
insights in real-time and the performance on Inf2 instances will
help us deliver lower latency and higher throughput over Inf1. With
the Inf2 performance improvements and new Inf2 features, such as
support for dynamic input sizes, we are improving our
cost-efficiency, elevating the real-time customer experience, and
helping our customers glean new insights from their data.”
About Amazon Web Services
For over 15 years, Amazon Web Services has been the world’s most
comprehensive and broadly adopted cloud offering. AWS has been
continually expanding its services to support virtually any cloud
workload, and it now has more than 200 fully featured services for
compute, storage, databases, networking, analytics, machine
learning and artificial intelligence (AI), Internet of Things
(IoT), mobile, security, hybrid, virtual and augmented reality (VR
and AR), media, and application development, deployment, and
management from 96 Availability Zones within 30 geographic regions,
with announced plans for 15 more Availability Zones and five more
AWS Regions in Australia, Canada, Israel, New Zealand, and
Thailand. Millions of customers—including the fastest-growing
startups, largest enterprises, and leading government
agencies—trust AWS to power their infrastructure, become more
agile, and lower costs. To learn more about AWS, visit
aws.amazon.com.
About Amazon
Amazon is guided by four principles: customer obsession rather
than competitor focus, passion for invention, commitment to
operational excellence, and long-term thinking. Amazon strives to
be Earth’s Most Customer-Centric Company, Earth’s Best Employer,
and Earth’s Safest Place to Work. Customer reviews, 1-Click
shopping, personalized recommendations, Prime, Fulfillment by
Amazon, AWS, Kindle Direct Publishing, Kindle, Career Choice, Fire
tablets, Fire TV, Amazon Echo, Alexa, Just Walk Out technology,
Amazon Studios, and The Climate Pledge are some of the things
pioneered by Amazon. For more information, visit amazon.com/about
and follow @AmazonNews.
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