Taiwan’s National Security Bureau (NSB) has warned that China-developed applications like RedNote (aka Xiaohongshu), Weibo, TikTok, WeChat, and Baidu Cloud pose security risks due to excessive data collection and data transfer to China.
The alert comes following an inspection of these apps carried out in coordination with the Ministry of Justice Investigation Bureau (MJIB) and the Criminal
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In May 2025, the U.S. government sanctioned a Chinese national for operating a cloud provider linked to the majority of virtual currency investment scam websites reported to the FBI. But a new report finds the accused continues to operate a slew of established accounts at American tech companies — including Facebook, Github, PayPal and Twitter/X.
On May 29, the U.S. Department of the Treasury announced economic sanctions against Funnull Technology Inc., a Philippines-based company alleged to provide infrastructure for hundreds of thousands of websites involved in virtual currency investment scams known as “pig butchering.” In January 2025, KrebsOnSecurity detailed how Funnull was designed as a content delivery network that catered to foreign cybercriminals seeking to route their traffic through U.S.-based cloud providers.
The Treasury also sanctioned Funnull’s alleged operator, a 40-year-old Chinese national named Liu “Steve” Lizhi. The government says Funnull directly facilitated financial schemes resulting in more than $200 million in financial losses by Americans, and that the company’s operations were linked to the majority of pig butchering scams reported to the FBI.
It is generally illegal for U.S. companies or individuals to transact with people sanctioned by the Treasury. However, as Mr. Lizhi’s case makes clear, just because someone is sanctioned doesn’t necessarily mean big tech companies are going to suspend their online accounts.
The government says Lizhi was born November 13, 1984, and used the nicknames “XXL4” and “Nice Lizhi.” Nevertheless, Steve Liu’s 17-year-old account on LinkedIn (in the name “Liulizhi”) had hundreds of followers (Lizhi’s LinkedIn profile helpfully confirms his birthday) until quite recently: The account was deleted this morning, just hours after KrebsOnSecurity sought comment from LinkedIn.
Mr. Lizhi’s LinkedIn account was suspended sometime in the last 24 hours, after KrebsOnSecurity sought comment from LinkedIn.
In an emailed response, a LinkedIn spokesperson said the company’s “Prohibited countries policy” states that LinkedIn “does not sell, license, support or otherwise make available its Premium accounts or other paid products and services to individuals and companies sanctioned by the U.S. government.” LinkedIn declined to say whether the profile in question was a premium or free account.
Mr. Lizhi also maintains a working PayPal account under the name Liu Lizhi and username “@nicelizhi,” another nickname listed in the Treasury sanctions. PayPal did not respond to a request for comment. A 15-year-old Twitter/X account named “Lizhi” that links to Mr. Lizhi’s personal domain remains active, although it has few followers and hasn’t posted in years.
These accounts and many others were flagged by the security firm Silent Push, which has been tracking Funnull’s operations for the past year and calling out U.S. cloud providers like Amazon and Microsoft for failing to more quickly sever ties with the company.
Liu Lizhi’s PayPal account.
In a report released today, Silent Push found Lizhi still operates numerous Facebook accounts and groups, including a private Facebook account under the name Liu Lizhi. Another active Facebook account clearly connected to Lizhi is a tourism page for Ganzhou, China called “EnjoyGanzhou” that was named in the Treasury Department sanctions.
“This guy is the technical administrator for the infrastructure that is hosting a majority of scams targeting people in the United States, and hundreds of millions have been lost based on the websites he’s been hosting,” said Zach Edwards, senior threat researcher at Silent Push. “It’s crazy that the vast majority of big tech companies haven’t done anything to cut ties with this guy.”
The FBI says it received nearly 150,000 complaints last year involving digital assets and $9.3 billion in losses — a 66 percent increase from the previous year. Investment scams were the top crypto-related crimes reported, with $5.8 billion in losses.
In a statement, a Meta spokesperson said the company continuously takes steps to meet its legal obligations, but that sanctions laws are complex and varied.
“Sanctions are often targeted in nature and don’t always prohibit people from having a presence on our platform,” the statement reads. “Whether specific activity is restricted by sanctions or Meta’s Terms and Policies depends on the specific facts.”
Attempts to reach Mr. Lizhi via his primary email addresses at Hotmail and Gmail bounced as undeliverable. Likewise, his 14-year-old YouTube channel appears to have been taken down recently.
However, anyone interested in viewing or using Mr. Lizhi’s 146 computer code repositories will have no problem finding active GitHub accounts for him, including one registered under the NiceLizhi and XXL4 nicknames mentioned in the Treasury sanctions.
One of multiple active GitHub profiles used by Liu “Steve” Lizhi, who uses the nickname XXL4 (a moniker listed in the Treasury sanctions for Mr. Lizhi).
Mr. Lizhi also operates a GitHub page for an open source e-commerce platform called NexaMerchant, which advertises itself as a payment gateway working with numerous American financial institutions. Interestingly, this profile’s “followers” page shows several other accounts that appear to be Mr. Lizhi’s. All of the account’s followers are tagged as “suspended,” even though that suspended message does not display when one visits those individual profiles.
In response to questions, GitHub said it has a process in place to identify when users and customers are Specially Designated Nationals or other denied or blocked parties, but that it locks those accounts instead of removing them. According to its policy, GitHub takes care that users and customers aren’t impacted beyond what is required by law.
All of the follower accounts for the XXL4 GitHub account appear to be Mr. Lizhi’s, and have been suspended by GitHub, but their code is still accessible.
“This includes keeping public repositories, including those for open source projects, available and accessible to support personal communications involving developers in sanctioned regions,” the policy states. “This also means GitHub will advocate for developers in sanctioned regions to enjoy greater access to the platform and full access to the global open source community.”
Edwards said it’s great that GitHub has a process for handling sanctioned accounts, but that the process doesn’t seem to communicate risk in a transparent way, noting that the only indicator on the locked accounts is the message, “This repository has been archived by the owner. It is not read-only.”
“It’s an odd message that doesn’t communicate, ‘This is a sanctioned entity, don’t fork this code or use it in a production environment’,” Edwards said.
Mark Rasch is a former federal cybercrime prosecutor who now serves as counsel for the New York City based security consulting firm Unit 221B. Rasch said when Treasury’s Office of Foreign Assets Control (OFAC) sanctions a person or entity, it then becomes illegal for businesses or organizations to transact with the sanctioned party.
Rasch said financial institutions have very mature systems for severing accounts tied to people who become subject to OFAC sanctions, but that tech companies may be far less proactive — particularly with free accounts.
“Banks have established ways of checking [U.S. government sanctions lists] for sanctioned entities, but tech companies don’t necessarily do a good job with that, especially for services that you can just click and sign up for,” Rasch said. “It’s potentially a risk and liability for the tech companies involved, but only to the extent OFAC is willing to enforce it.”
Liu Lizhi operates numerous active Facebook accounts and groups, including this one for an entity specified in the OFAC sanctions: The “Enjoy Ganzhou” tourism page for Ganzhou, China. Image: Silent Push.
In July 2024, Funnull purchased the domain polyfill[.]io, the longtime home of a legitimate open source project that allowed websites to ensure that devices using legacy browsers could still render content in newer formats. After the Polyfill domain changed hands, at least 384,000 websites were caught in a supply-chain attack that redirected visitors to malicious sites. According to the Treasury, Funnull used the code to redirect people to scam websites and online gambling sites, some of which were linked to Chinese criminal money laundering operations.
The U.S. government says Funnull provides domain names for websites on its purchased IP addresses, using domain generation algorithms (DGAs) — programs that generate large numbers of similar but unique names for websites — and that it sells web design templates to cybercriminals.
“These services not only make it easier for cybercriminals to impersonate trusted brands when creating scam websites, but also allow them to quickly change to different domain names and IP addresses when legitimate providers attempt to take the websites down,” reads a Treasury statement.
Meanwhile, Funnull appears to be morphing nearly all aspects of its business in the wake of the sanctions, Edwards said.
“Whereas before they might have used 60 DGA domains to hide and bounce their traffic, we’re seeing far more now,” he said. “They’re trying to make their infrastructure harder to track and more complicated, so for now they’re not going away but more just changing what they’re doing. And a lot more organizations should be holding their feet to the fire.”
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Deloitte’s new blueprint looks to bridge the gap between the massive push for AI adoption and a lack of preparedness among leaders and employees.
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Multiple critical infrastructure sectors were hit last year during an attack spree in France via a trio of zero-day vulnerabilities affecting Ivanti Cloud Service Appliance devices, the country’s cybersecurity agency said in a report released Tuesday.
Government agencies and organizations in the telecommunications, media, finance and transportation industries were impacted by widespread zero-day exploits of CVE-2024-8190, CVE-2024-8963 and CVE-2024-9380 from early September to late November 2024, according to the French National Agency for the Security of Information Systems.
French authorities attribute the attacks to UNC5174, a former member of Chinese hacktivist collectives likely working as a contractor for China’s Ministry of State Security, according to Mandiant. The attacker, believed to use the persona “Uteus,” previously exploited edge device vulnerabilities in ConnectWise ScreenConnect, F5 BIG-IP, Atlassian Confluence, the Linus kernel and Zyxel firewalls.
Authorities in France concluded UNC5174 used a unique intrusion set it dubbed “Houken,” which used zero-day vulnerabilities, a sophisticated rootkit, various open-source tools, commercial VPNs and dedicated servers. Officials said Houken and UNC5174 are likely operated by the same threat actor, an initial access broker that also steals credentials and deploys mechanisms to achieve persistent access to victim networks.
“Though already documented for its opportunistic exploitation of vulnerabilities on edge devices, the use of zero-days by a threat actor linked to UNC5174 is new,” France’s cybersecurity agency said in the report. “The operators behind the UNC5174 and Houken intrusion sets are likely primarily looking for valuable initial accesses to sell to a state-linked actor seeking insightful intelligence.”
The Cybersecurity and Infrastructure Security Agency issued an advisory in January warning that threat actors chained the three Ivanti zero-days to gain initial access, conduct remote code execution, obtain credentials and implant webshells on victim networks.
Sysdig researchers in April said they observed the China state-sponsored hacking group, UNC5174, using open-source offensive security tools, such as VShell and WebSockets, to blend in with more common cybercriminal activity.
Multiple attackers, including China-linked espionage groups, have repeatedly exploited a long run of vulnerabilities in Ivanti products. Ivanti is a repeat offender, shipping software with a high number of vulnerabilities — more than any other vendor in this space since the start of last year — across at least 10 different product lines since 2021.
CISA’s known exploited vulnerabilities catalog contains 30 Ivanti defects in the past four years, and attackers have exploited seven vulnerabilities in Ivanti products so far this year, according to cyber authorities.
Ivanti wasn’t immediately available to comment on the French authorities’ report.
The post China-linked attacker hit France’s critical infrastructure via trio of Ivanti zero-days last year appeared first on CyberScoop.
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Analyzing binary code helps vendors and organizations detect security threats and zero-day vulnerabilities in the software supply chain, but it doesn’t come without challenges. It looks like AI has come to the rescue.
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The French cybersecurity agency on Tuesday revealed that a number of entities spanning governmental, telecommunications, media, finance, and transport sectors in the country were impacted by a malicious campaign undertaken by a Chinese hacking group by weaponizing several zero-day vulnerabilities in Ivanti Cloud Services Appliance (CSA) devices.
The campaign, detected at the beginning of
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Have you ever wished you could quickly visualize how a new outfit might look on you before making a purchase? Or how a piece of furniture would look in your living room? Today, we’re excited to introduce a new virtual try-on capability in Amazon Nova Canvas that makes this possible. In addition, we are adding eight new style options for improved style consistency for text-to-image based style prompting. These features expand Nova Canvas AI-powered image generation capabilities making it easier than ever to create realistic product visualizations and stylized images that can enhance the experience of your customers.
Let’s take a quick look at how you can start using these today.
Getting started
The first thing is to make sure that you have access to the Nova Canvas model through the usual means. Head to the Amazon Bedrock console, choose Model access and enable Amazon Nova Canvas for your account making sure that you select the appropriate regions for your workloads. If you already have access and have been using Nova Canvas, you can start using the new features immediately as they’re automatically available to you.
Virtual try-on
The first exciting new feature is virtual try-on. With this, you can upload two pictures and ask Amazon Nova Canvas to put them together with realistic results. These could be pictures of apparel, accessories, home furnishings, and any other products including clothing. For example, you can provide the picture of a human as the source image and the picture of a garment as the reference image, and Amazon Nova Canvas will create a new image with that same person wearing the garment. Let’s try this out!
My starting point is to select two images. I picked one of myself in a pose that I think would work well for a clothes swap and a picture of an AWS-branded hoodie.
Note that Nova Canvas accepts images containing a maximum of 4.1M pixels – the equivalent of 2,048 x 2,048 – so be sure to scale your images to fit these constraints if necessary. Also, if you’d like to run the Python code featured in this article, ensure you have Python 3.9 or later installed as well as the Python packages boto3 and pillow.
To apply the hoodie to my photo, I use the Amazon Bedrock Runtime invoke API. You can find full details on the request and response structures for this API in the Amazon Nova User Guide. The code is straightforward, requiring only a few inference parameters. I use the new taskType of "VIRTUAL_TRY_ON". I then specify the desired settings, including both the source image and reference image, using the virtualTryOnParams object to set a few required parameters. Note that both images must be converted to Base64 strings.
import base64
def load_image_as_base64(image_path):
"""Helper function for preparing image data."""
with open(image_path, "rb") as image_file:
return base64.b64encode(image_file.read()).decode("utf-8")
inference_params = {
"taskType": "VIRTUAL_TRY_ON",
"virtualTryOnParams": {
"sourceImage": load_image_as_base64("person.png"),
"referenceImage": load_image_as_base64("aws-hoodie.jpg"),
"maskType": "GARMENT",
"garmentBasedMask": {"garmentClass": "UPPER_BODY"}
}
}Nova Canvas uses masking to manipulate images. This is a technique that allows AI image generation to focus on specific areas or regions of an image while preserving others, similar to using painter’s tape to protect areas you don’t want to paint.
You can use three different masking modes, which you can choose by setting maskType to the correct value. In this case, I’m using "GARMENT", which requires me to specify which part of the body I want to be masked. I’m using "UPPER_BODY" , but you can use others such as "LOWER_BODY", "FULL_BODY", or "FOOTWEAR" if you want to specifically target the feet. Refer to the documentation for a full list of options.
I then call the invoke API, passing in these inference arguments and saving the generated image to disk.
# Note: The inference_params variable from above is referenced below.
import base64
import io
import json
import boto3
from PIL import Image
# Create the Bedrock Runtime client.
bedrock = boto3.client(service_name="bedrock-runtime", region_name="us-east-1")
# Prepare the invocation payload.
body_json = json.dumps(inference_params, indent=2)
# Invoke Nova Canvas.
response = bedrock.invoke_model(
body=body_json,
modelId="amazon.nova-canvas-v1:0",
accept="application/json",
contentType="application/json"
)
# Extract the images from the response.
response_body_json = json.loads(response.get("body").read())
images = response_body_json.get("images", [])
# Check for errors.
if response_body_json.get("error"):
print(response_body_json.get("error"))
# Decode each image from Base64 and save as a PNG file.
for index, image_base64 in enumerate(images):
image_bytes = base64.b64decode(image_base64)
image_buffer = io.BytesIO(image_bytes)
image = Image.open(image_buffer)
image.save(f"image_{index}.png")
I get a very exciting result!
And just like that, I’m the proud wearer of an AWS-branded hoodie!
In addition to the "GARMENT" mask type, you can also use the "PROMPT" or "IMAGE" masks. With "PROMPT", you also provide the source and reference images, however, you provide a natural language prompt to specify which part of the source image you’d like to be replaced. This is similar to how the "INPAINTING" and "OUTPAINTING" tasks work in Nova Canvas. If you want to use your own image mask, then you choose the "IMAGE" mask type and provide a black-and-white image to be used as mask, where black indicates the pixels that you want to be replaced on the source image, and white the ones you want to preserve.
This capability is specifically useful for retailers. They can use it to help their customers make better purchasing decisions by seeing how products look before buying.
Using style options
I’ve always wondered what I would look like as an anime superhero. Previously, I could use Nova Canvas to manipulate an image of myself, but I would have to rely on my good prompt engineering skills to get it right. Now, Nova Canvas comes with pre-trained styles that you can apply to your images to get high-quality results that follow the artistic style of your choice. There are eight available styles including 3D animated family film, design sketch, flat vector illustration, graphic novel, maximalism, midcentury retro, photorealism, and soft digital painting.
Applying them is as straightforward as passing in an extra parameter to the Nova Canvas API. Let’s try an example.
I want to generate an image of an AWS superhero using the 3D animated family film style. To do this, I specify a taskType of "TEXT_IMAGE" and a textToImageParams object containing two parameters: text and style. The text parameter contains the prompt describing the image I want to create which in this case is “a superhero in a yellow outfit with a big AWS logo and a cape.” The style parameter specifies one of the predefined style values. I’m using "3D_ANIMATED_FAMILY_FILM" here, but you can find the full list in the Nova Canvas User Guide.
inference_params = {
"taskType": "TEXT_IMAGE",
"textToImageParams": {
"text": "a superhero in a yellow outfit with a big AWS logo and a cape.",
"style": "3D_ANIMATED_FAMILY_FILM",
},
"imageGenerationConfig": {
"width": 1280,
"height": 720,
"seed": 321
}
}Then, I call the invoke API just as I did in the previous example. (The code has been omitted here for brevity.) And the result? Well, I’ll let you judge for yourself, but I have to say I’m quite pleased with the AWS superhero wearing my favorite color following the 3D animated family film style exactly as I envisioned.
What’s really cool is that I can keep my code and prompt exactly the same and only change the value of the style attribute to generate an image in a completely different style. Let’s try this out. I set style to PHOTOREALISM.
inference_params = {
"taskType": "TEXT_IMAGE",
"textToImageParams": {
"text": "a superhero in a yellow outfit with a big AWS logo and a cape.",
"style": "PHOTOREALISM",
},
"imageGenerationConfig": {
"width": 1280,
"height": 720,
"seed": 7
}
}And the result is impressive! A photorealistic superhero exactly as I described, which is a far departure from the previous generated cartoon and all it took was changing one line of code.
Things to know
Availability – Virtual try-on and style options are available in Amazon Nova Canvas in the US East (N. Virginia), Asia Pacific (Tokyo), and Europe (Ireland). Current users of Amazon Nova Canvas can immediately use these capabilities without migrating to a new model.
Pricing – See the Amazon Bedrock pricing page for details on costs.
For a preview of virtual try-on of garments, you can visit nova.amazon.com where you can upload an image of a person and a garment to visualize different clothing combinations.
If you are ready to get started, please check out the Nova Canvas User Guide or visit the AWS Console.
Matheus Guimaraes | @codingmatheus
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Federal authorities levied sanctions Tuesday on Aeza Group, a bulletproof hosting service provider based in Russia, for allegedly supporting a broad swath of ransomware, malware and infostealer operators.
Aeza Group has provided servers and specialized infrastructure to the Meduza, RedLine and Lumma infostealer operators, BianLian ransomware and BlackSprut, a Russian marketplace for illicit drugs, according to the Treasury Department’s Office of Foreign Assets Control. Lumma infected about 10 million systems before it was dismantled through a coordinated global takedown in May.
The Treasury Department’s action against Aeza Group follows a wave of cybercrime crackdowns across the globe. Prolific cybercriminals have been arrested, and infostealers, malware loaders, counter antivirus and crypting services, cybercrime marketplaces, ransomware infrastructure and DDoS-for-hire operations have all been seized, taken offline or severely disrupted by global coordinated campaigns since May.
Officials accused Aeza Group of helping cybercriminals target U.S. defense companies and technology vendors.
“Cybercriminals continue to rely heavily on bulletproof hosting service providers like Aeza Group to facilitate disruptive ransomware attacks, steal U.S. technology and sell black-market drugs,” Bradley T. Smith, the Treasury Department’s acting under secretary for terrorism and financial intelligence, said in a statement.
The Treasury Department sanctioned four people for their involvement in Aeza Group, including two part owners — Asenii Aleksandrovich Penzev and Yurii Meruzhanovich Bozoyan — who were previously arrested by Russian law enforcement for their alleged involvement in BlackSprut, authorities said. Igor Anatolyevich Knyazev, another part owner of Aeza Group, and Vladimir Vyacheslavovich Gast were also sanctioned for their leadership positions in the criminal enterprise.
Authorities also imposed sanctions on Aeza Group-affiliated companies, including United Kingdom-based Aeza International and Russia-based subsidiaries Aeza Logistic and Cloud Solutions.
The sanctions imposed on Aeza Group and its leaders were a follow-on effort, marking a continuation of February’s globally coordinated sanctions against Zservers, a Russia-based bulletproof hosting provider that allegedly supported the LockBit ransomware-as-a-service group.
“Treasury, in close coordination with the U.K. and our other international partners, remains resolved to expose the critical nodes, infrastructure, and individuals that underpin this criminal ecosystem,” Smith said.
The post US sanctions bulletproof hosting provider for supporting ransomware, infostealer operations appeared first on CyberScoop.
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While tens of thousands of customers are successfully using Amazon DynamoDB global tables with eventual consistency, we’re seeing emerging needs for even stronger resilience. Many organizations find that the DynamoDB multi-Availability Zone architecture and eventually consistent global tables meet their requirements, but critical applications like payment processing systems and financial services demand more.
For these applications, customers require a zero Recovery Point Objective (RPO) during rare Region-wide events, meaning you can direct your app to read the latest data from any Region. Your multi-Region applications always need to access the same data regardless of location.
Starting today, you can use a new Amazon DynamoDB global tables capability that provides multi-Region strong consistency (MRSC), enabling zero RPO. This capability, first announced as a preview at AWS re:Invent 2024, simplifies building highly resilient global applications.
Here’s how you can enable MRSC starting with an existing empty DynamoDB table:
With MRSC, DynamoDB now offers you the highest level of resilience for your applications. If application processing is interrupted in a Region, you can redirect traffic to another Region containing a MRSC replica and know that you’ll be processing the latest data.
Getting started
Let me walk you through how you can use this new capability.
To start using MRSC, I need to create a global table from an existing DynamoDB table that doesn’t contain any data. I navigate to my existing table, select the Global tables tab and select Create replica.
The MRSC availability architecture requires three AWS Regions. I can configure MRSC with either three full replicas or with two replicas and a witness. A witness contains only replicated change data to provide the required availability without maintaining a full copy of my table data.
The following screenshot shows how I configure MRSC with two replicas and a witness. To configure MRSC with three full replicas instead, I can clear Configure Region 2 as Witness.
If I need to update my existing table programmatically, I can use the Amazon Q CLI to generate the required command with the following prompt:
> Hey Q! Update my existing DynamoDB table called "demo-mrsc" in us-east-1 with multi-Region strong consistency across us-east-2 with witness in us-west-2 Regions
Shortly after, Q CLI will respond back with following command:
> To update your DynamoDB table with multi-Region strong consistency, you'll need to use the update-table command with the appropriate parameters. Here's how you would do it:
aws dynamodb update-table \
--table-name demo-mrsc \
--replica-updates '[{"Create": {"RegionName": "us-east-2"}}]' \
--global-table-witness-updates '[{"Create": {"RegionName": "us-west-2"}}]' \
--multi-region-consistency STRONG \
--region us-east-1
After it’s finished processing, I can check the status of my MRSC global table. I can see I have a witness configured for my DynamoDB global table. A witness reduces costs while still providing the resilience benefits of multi-Region strong consistency.
Then, in my application, I can use ConsistentRead to read data with strong consistency. Here’s a Python example:
import boto3
# Configure the DynamoDB client for your region
dynamodb = boto3.resource('dynamodb', region_name='us-east-2')
table = dynamodb.Table('demo-mrsc')
pk_id = "demo#test123"
# Read with strong consistency across regions
response = table.get_item(
Key={
'PK': pk_id
},
ConsistentRead=True
)
print(response)
For operations that require the strongest resilience, I can use ConsistentRead=True. For less critical operations where eventual consistency is acceptable, I can omit this parameter to improve performance and reduce costs.
Additional things to know
Here are a couple of things to note:
Learn more about how you can achieve the highest level of application resilience, enable your applications to be always available and always read the latest data regardless of the Region by visiting Amazon DynamoDB global tables.
Happy building!
— Donnie
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Today, we’re announcing the general availability of Amazon Elastic Compute Cloud (Amazon EC2) C8gn network optimized instances powered by AWS Graviton4 processors and the latest 6th generation AWS Nitro Card. EC2 C8gn instances deliver up to 600Gbps network bandwidth, the highest bandwidth among EC2 network optimized instances.
You can use C8gn instances to run the most demanding network intensive workloads, such as security and network virtual appliances (virtual firewalls, routers, load balancers, proxy servers, DDoS appliances), data analytics, and tightly-coupled cluster computing jobs.
EC2 C8gn instances specifications
C8gn instances provide up to 192 vCPUs and 384 GiB memory, and offer up to 30 percent higher compute performance compared Graviton3-based EC2 C7gn instances.
Here are the specs for C8gn instances:
| Instance Name | vCPUs | Memory (GiB) | Network Bandwidth (Gbps) | EBS Bandwidth (Gbps) |
|---|---|---|---|---|
| c8gn.medium | 1 | 2 | Up to 25 | Up to 10 |
| c8gn.large | 2 | 4 | Up to 30 | Up to 10 |
| c8gn.xlarge | 4 | 8 | Up to 40 | Up to 10 |
| c8gn.2xlarge | 8 | 16 | Up to 50 | Up to 10 |
| c8gn.4xlarge | 16 | 32 | 50 | 10 |
| c8gn.8xlarge | 32 | 64 | 100 | 20 |
| c8gn.12xlarge | 48 | 96 | 150 | 30 |
| c8gn.16xlarge | 64 | 128 | 200 | 40 |
| c8gn.24xlarge | 96 | 192 | 300 | 60 |
| c8gn.metal-24xl | 96 | 192 | 300 | 60 |
| c8gn.48xlarge | 192 | 384 | 600 | 60 |
| c8gn.metal-48xl | 192 | 384 | 600 | 60 |
You can launch C8gn instances through the AWS Management Console, AWS Command Line Interface (AWS CLI), or AWS SDKs.
If you’re using C7gn instances now, you will have straightforward experience migrating network intensive workloads to C8gn instances because the new instances offer similar vCPU and memory ratios. To learn more, check out the collection of Graviton resources to help you start migrating your applications to Graviton instance types.
You can also visit the Level up your compute with AWS Graviton page to begin your Graviton adoption journey.
Now available
Amazon EC2 C8gn instances are available today in US East (N. Virginia) and US West (Oregon) Regions. Two metal instance sizes are only available in US East (N. Virginia) Region. These instances can be purchased as On-Demand, Savings Plan, Spot instances, or as Dedicated instances and Dedicated hosts.
Give C8gn instances a try in the Amazon EC2 console. To learn more, refer to the Amazon EC2 C8g instance page and send feedback to AWS re:Post for EC2 or through your usual AWS Support contacts.
— Channy
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