ARM . COM {
}
Detected CMS Systems:
- Blogger (2 occurrences)
Title:
Arcee AI and Arm | Enterprise AI With Arcee SLMs on Arm – Arm®
Description:
Arcee AI delivers high-performance small language models (SLMs) on Arm CPUs—enabling cost-effective, scalable AI for enterprise and agentic AI workloads.
Website Age:
30 years and 4 months (reg. 1995-02-07).
Matching Content Categories {📚}
- Technology & Computing
- Education
- Telecommunications
Content Management System {📝}
What CMS is arm.com built with?
Arm.com is based on BLOGGER.
Traffic Estimate {📈}
What is the average monthly size of arm.com audience?
💥 Very Strong Traffic: 200k - 500k visitors per month
Based on our best estimate, this website will receive around 418,128 visitors per month in the current month.
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How Does Arm.com Make Money? {💸}
We're unsure how the site profits.
While many websites aim to make money, others are created to share knowledge or showcase creativity. People build websites for various reasons. This could be one of them. Arm.com could have a money-making trick up its sleeve, but it's undetectable for now.
Keywords {🔍}
arm, arcee, slms, models, partners, support, stories, cpus, cloud, architecture, agentic, training, today, performance, scalability, run, armbased, model, center, enterprises, system, skip, products, company, success, overview, impact, technologies, related, costefficient, tasks, data, significantly, costeffective, deliver, reducing, hardware, enterprise, workflows, edge, efficiency, leveraging, expensive, instances, acceleration, enables, multiple, parallel, running, cpu,
Topics {✒️}
customer support automation maintaining model quality data center hardware arm-based cpus arm-based cloud instances arm cpus real-time decision-making process large-scale workloads industry-leading models combined significantly fewer parameters—making arcee ai technologies arcee ai specializes system revolutionizing ai today cost-efficient ai agentic ai systems agentic ai platform arm kleidi arm platforms running distributed slms significantly reducing hardware cpu platforms increasingly scarce gpus arcee ai arcee ai 4x performance improvements agentic ai cost-effective inference obvious choice today arm expensive gpu instances deliver comparable performance reducing cloud expenses demonstrating 3-4x acceleration main content skip models run efficiently parallel—maximizing efficiency enterprise workflows large model slms optimized run slms cost-effective ai cost-efficiency cost savings 4x acceleration larger models quantized models means running maximize efficiency
Schema {🗺️}
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duration:PT3M42S
name:Arm Arcee AI Success Story - Built On Arm
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embedUrl:https://fast.wistia.net/embed/iframe/qoh35uyiss
uploadDate:2025-04-11T14:37:03.000Z
description:an Arm Folder video
contentUrl:https://embed-ssl.wistia.com/deliveries/f2a87a0425d73dbfae1440c0a79e91dc3c1e6eb1.m3u8
transcript:My name is Julian. I'm the chief evangelist for RCAI.
RCAI is a US Armv9, and we're the small language model champions.
Small language models get smaller and smaller, and yet they get better and better. So I think we're at a tipping point where we need to run those models on the most cost efficient platform to deliver the best ROI for enterprise use cases. And that means running on CPU platforms and looking at them, our obvious choice today is to use on platforms in the cloud, outside of the cloud. Instead of using, GPU platforms, which even now are probably getting too large for those small models, you can look at running inference on CPUs and particularly Armv9 CPUs.
So it may sound surprising. I I would encourage everybody to try for themselves. Look at the numbers you're gonna get. Look at the price point you're gonna get and decide for yourself. But we believe in it and we think it's, it's gonna rise massively in the future.
The reason why we can efficiently run our models on Armv9 CPUs today is because, optimization routines and acceleration routines are available.
They're known as Cline AI, and they've been built into open source tools like PyTorch or LAMA CPP, which make it extremely simple to take, one of our models, from Hugging Face or one of our commercial models and optimize it in a very, very efficient way for CPU inference on ARM. Generally, when I run benchmarks, on Armv9 based instances in the cloud, on AWS or Google Cloud, I see anywhere from three to four x acceleration from the sixteen bit model, so the model in the original precision, to the four bit quantized model. So, I mostly work with LAMA CPP, which embeds the the Clyde AI, routines.
And, and so that's what I'm getting. So for example, with a ten billion parameter model, which is the the last one we released, I go from maybe sixteen tokens per second to forty five, forty seven tokens per second just by quantizing with minimal degradation.
Couple of years ago, we're all using large language models hosted, beyond APIs, and I think we've come a long way. So now, a lot of enterprise customers realize the value of small language models in terms of cost efficiency, in terms of, the ability to tailor them, etcetera, etcetera. I think the next step is to move to agency workflows where we're not trying to replace, an LLM with an open source equivalent.
We're really looking at using ten, twenty, thirty models, some off the shelf, some specialized, to complete all kinds of enterprise workflows.
And and so as you deploy those workflows, you'll have thousands and thousands of, parallel executions, which is a very different scaling pattern. And we think we can scale best with, small cost efficient compute units. And, and that would be Armv9 based, CPU instances.
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Social Networks {👍}(4)
External Links {🔗}(2)
Analytics and Tracking {📊}
- Facebook Pixel
- Hotjar
Libraries {📚}
- Boomerang
- Foundation
- Video.js
Emails and Hosting {✉️}
Mail Servers:
- arm-com.mail.protection.outlook.com
Name Servers:
- ns10.arm.com
- ns9.arm.com
CDN Services {📦}
- Com/9
- Cookielaw
- Designsystem