Box culture dominates life. Humans are comfortable with boxed-up products, services and things if they can be compartmentalized and created or delivered in a form that offers a clearly defined shape. We buy shoes in box, we often buy fruit in a box and we life to talk about so-called Commercial-Off-The-Shelf (COTS) products and services that may not always come in a physical box, but are nonetheless defined as a boxed-up offering.
We’ve even taken to living, working, shopping and eating in boxes - just look at the rise of ISO intermodal freight containers in the creation of what are now being called ‘containerpark’ sites everywhere from Las Vegas to London.
This process happens in technology too. We’ve seen COTS software suites offered many times over the years, particularly if we think back to pre-web CD-ROM era throughout the 1980s and 1990s. When we bought Microsoft Office, Adobe Creative Suite or Norton Anti-Virus (other software packages are/were also available), we bought them in a box.
Not to be confused with the commoditization of software - the point at which any given IT service becomes indistinguishable from others over and above its price - the compartmentalization of software and IT services into a boxed format today is often meant to express its pre-engineered, pre-architected and pre-provisioned (possibly part-baked oven-ready) status, all of which is meant to make it easier to deploy.
With so much discussion surrounding the use of generative Artificial Intelligence (gen-AI) and the use of ChatGPT-derived (the GPT part denoting Generative Pre-trained Transformer, a type of AI that gets its intelligence from Large Language Models of human words) intelligence, it is perhaps no surprise to see this technology being compartmentalized in line with the wider trend proposed here so far. Among the firms now looking to bring this efficiency to market is hybrid multi-cloud platform company Nutanix.
By bringing forward what it calls Nutanix GPT-in-a-Box, the company says it has created a technology for organizations who want to jump-start their work with AI and Machine Learning (ML), but still retain full control over their data. This is a software-defined AI-ready platform, along with services to help organizations size and configure hardware and software infrastructure suitable to deploy a curated set of Large Language Models (LLMs) using the open source AI and MLOps frameworks on the Nutanix Cloud Platform.
In simple terms, it is built to enable organizations to purchase, position, prepare and push forward AI-ready infrastructure to fine-tune and run generative pre-trained transformers (GPTs), including LLMs. In other words, this is boxed-up cloud foundations for new-age AI.
“Helping customers tackle the biggest challenges they face in IT is at the core of what we do, from managing increasing multi-cloud complexity, to data protection challenges, and now adoption of generative AI solutions while keeping control over data privacy and compliance,” said Thomas Cornely, SVP of product management at Nutanix. “Nutanix GPT-in-a-Box is an opinionated AI-ready stack [i.e. one that is prealigned, provisioned and prepared] that aims to solve the key challenges with generative AI adoption and help jumpstart AI innovation.”
Cornely and team point to the reality of AI implementation in real world businesses. He suggests that many firms find it tough to know how to quickly, efficiently and securely take advantage of generative AI and AI/ML applications. This challenge is logically made even tougher when there is a question of data sovereignty and governance in the implementation.
“New use cases emerge every day as organisations look to leverage generative AI to Boost customer service, developer productivity, operational efficiency and more. From the automated transcription of internal documents, to high-speed search of multimedia content and automated analysis, many organizations see the opportunity with AI but are struggling with growing concerns regarding intellectual property leakage, compliance and privacy concerns,” details Nutanix, in a technical product statement.
If we accept the technology proposition being put forward here by Nutanix (i.e. that the GPT end of generative AI is rarely a point-and-click plug-and-play solution and that a degree of ‘boxing’ as evidenced here can make adoption of this technology more manageable), then we can perhaps see how tough it is to deploy complex new AI & ML systems in real world environments. Not least of the challenges on the road to building an AI-ready stack is the need to support ML administrators and data scientists in the workplace. In some firms, this core human factor may be the reason why the prospect of large AI investment costs has enterprises stalled a company’s AI and ML strategy.
The Nutanix GPT-in-a-Box offering is ready-to-use customer-controlled AI infrastructure for the Internet of Things (IoT) edge, or the core datacenter. It allows our poor ML administrators and data scientists to run and fine-tune AI and GPT models with a full complement of security and data protection offerings ideal for AI data protection
According to the company, the Nutanix GPT-In-a-Box solution builds on the full stack scalability, performance, resilience and ease of use that the Nutanix Cloud Platform is known for. The team behind this new technology package claim to have expertize with scalable infrastructure across public cloud, datacenter and edge use cases. All of which combined efforts are said to deliver an environment to fine-tune and run AI applications.
Is box-based delivery logic the way forward for all complex technologies? Yes and no. There will be organizations that have dedicated teams well-versed in AI implementations at all levels. Indeed, some organizations most adept at working with the still-nascent world of gen-AI, Large Language Models (LLMs) and GPT technologies will be active innovators and leaders in this space and may also be involved with contributing to industry standards, major scale open source projects and other initiatives in this space.
But for many - and this is presumably where Nutanix eyes the low-hanging fruit - a specialism in this zone of technology will be a big ask and more than any one IT department can quickly shoulder as it works to just ‘keep the lights on’ and run the business.
Not all technology comes in a box, but where it does, it’s not like a box of chocolates, you pretty much always know what you are going to get.
The stock of Nutanix Inc. (NTNX) has seen a 4.78% increase in the past week, with a 5.72% gain in the past month, and a 22.98% flourish in the past quarter. The volatility ratio for the week is 2.71%, and the volatility levels for the past 30 days are at 2.65% for NTNX. The simple moving average for the last 20 days is 5.84% for NTNX stock, with a simple moving average of 14.66% for the last 200 days.
, and the 36-month beta value for NTNX is at 1.28. Analysts have varying views on the stock, with 8 analysts rating it as a “buy,” 1 rating it as “overweight,” 6 as “hold,” and 0 as “sell.”
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The average price suggested by analysts for NTNX is $35.25, which is $2.71 above the current market price. The public float for NTNX is 230.95M, and currently, shorts hold a 2.28% of that float. The average trading volume for NTNX on August 22, 2023 was 1.54M shares.
Nutanix Inc. (NASDAQ: NTNX)’s stock price has increased by 2.54 compared to its previous closing price of 30.92. However, the company has seen a 4.78% increase in its stock price over the last five trading sessions. Zacks Investment Research reported 2023-07-18 that NTNX, REPYY, ITRI, ING and SSYS have been added to the Zacks Rank #1 (Strong Buy) List on July 18, 2023.
After a stumble in the market that brought NTNX to its low price for the period of the last 52 weeks, the company was unable to rebound, for now settling with -6.01% of loss for the given period.
Volatility was left at 2.65%, however, over the last 30 days, the volatility rate increased by 2.71%, as shares surge +3.71% for the moving average over the last 20 days. Over the last 50 days, in opposition, the stock is trading +5.66% upper at present.
During the last 5 trading sessions, NTNX rose by +3.98%, which changed the moving average for the period of 200-days by +14.67% in comparison to the 20-day moving average, which settled at $30.01. In addition, Nutanix Inc. saw 21.71% in overturn over a single year, with a tendency to cut further gains.
Reports are indicating that there were more than several insider trading activities at NTNX starting from Wall Tyler, who sale 90,000 shares at the price of $26.19 back on Jan 03. After this action, Wall Tyler now owns 48,256 shares of Nutanix Inc., valued at $2,357,073 using the latest closing price.
RAMASWAMI RAJIV, the President and CEO of Nutanix Inc., sale 5,000 shares at $28.34 during a trade that took place back on Dec 21, which means that RAMASWAMI RAJIV is holding 310,348 shares at $141,715 based on the most exact closing price.
Current profitability levels for the company are sitting at:
The net margin for Nutanix Inc. stands at -50.45. The total capital return value is set at -107.03, while invested capital returns managed to touch -265.67. Equity return is now at value 49.80, with -16.50 for asset returns.
When we switch over and look at the enterprise to sales, we see a ratio of 4.22, with the company’s debt to enterprise value settled at 0.42. The receivables turnover for the company is 9.84 and the total asset turnover is 0.68. The liquidity ratio also appears to be rather interesting for investors as it stands at 1.34.
In conclusion, Nutanix Inc. (NTNX) has had a better performance lately. Opinion on the stock among analysts is bullish, with some giving it a “buy” rating and others a “hold”. It is worth mentioning that the stock is currently trading in close proximity to its 50-day moving average and its 52-week high.
Nutanix announces the new AI solution Nutantix GPT-in-a-box. The tool should make it easier for companies to introduce generative AI into the workplace.
Nutanix GPT-in-a-box is a new tool from Nutanix to help companies get started implementing AI and machine learning (ML). Notably, the tool allows companies to maintain full control over their data.
The new platform’s essential goal is to help define hardware and software infrastructure suitable for running AI and ML on. To do this, the platform can rely on the capabilities of open-source AI and MLOps frameworks from the Nutanix Cloud Platform.
The tool focuses on implementing generative AI in the workplace, but many enterprises are likely to ignore the tool. Indeed, generative AI was found to be banned in three-quarters of organizations, according to an earlier survey. There are two concerns about generative AI tools that trigger the ban. Many focus on the risk of using unsecured apps, while others fear that company secrets will leak.
Nutanix recognizes these fears. The tool, therefore, puts data management in the company’s hands but also adds (data)security capabilities to the platform.
According to the company, many organizations additionally struggle with questions about how best to support ML administrators and data scientists. To that end, Nutanix GPT-in-a-Box provides turnkey AI infrastructure for the edge or core data center.
Also read: Nutanix wants to be infrastructure for all workloads, including Kubernetes
Nutanix has launched GPT-in-a-Box, a bundled service that adds artificial intelligence (AI) software stack elements – such as foundation models and AI frameworks – to scale-out hyper-converged infrastructure (HCI).
GPT-in-a-Box also offers consulting so that customers can specify the right infrastructure configuration, in terms of hardware – for example, GPU spec – and software, such as AI components.
Nutanix will aim the initial launch squarely at customer on-premise use cases, but including edge workloads, with expansion to the cloud coming later.
Essentially, Nutanix believes customers need help to specify an infrastructure for AI because it can involve a complex mix of software elements plus hardware add-ons, and that concerns are commonplace over privacy and governance in AI applications.
“It’s activity that consumes, creates and generates a lot of data,” said Nutanix senior vice-president for product management Thomas Cornely. “And discussion about what you can do on-premise often resolves around privacy and governance.”
Nutanix will offer what it calls a “full-stack AI-ready platform”, in which it expects customers to deploy hardware and software to train and retrain models and be able to expose results to application developers.
GPT-in-a-Box bundles will comprise Nutanix HCI, Nvidia GPU hardware or recommendations, the Nutanix AHV hypervisor, a Kubernetes container layer, AI foundation models, open-source AI frameworks that could include KubeFlow, Jupiter and PyTorch, and a curated set of large language models including Llama2, Falcom GPT and MosaicML, all of which will provide outputs exposed for application development.
Nutanix’s offering is the latest effort from storage array makers to target AI/ML use cases, and clearly aims to hook on the surge in interest in chat-format AI. All of the big storage makers have addressed the rise in prominence of unstructured data as a source of analytics processing, but not all have been so explicit in targeting product bundles. An exception is Vast Data, which wants to build its recently launched Vast Data Platform as a global brain-like network of AI learning nodes.
Meanwhile, Nutanix GPT-in-a-Box is not just a self-service deploy-and-run offer. “It’s a bundled offer and it can scale down and out,” said Cornely. “But there’s a consulting phase, on GPUs, for example, and the software elements needed to support customer requirements.”
It’s an offer primarily launched at greenfield deployments in core datacentre or edge locations. Existing Nutanix customers can, in theory, build AI-ready infrastructures but would still need to consult over, for example, GPU sizing. “They do need different components,” said Cornely.
“They could upgrade their own infrastructure, but many customers lack the time to get started,” he said. “And there are different components for different parts of the [machine learning] process. There’s quite a lot of consulting up-front, but Nutanix has people that are chairs and vice-chairs of organisations that are putting this stuff out so they can say, ‘This is what’s needed for this deployment’.”
According to Cornely, many customers lack policies for the data that’s going into models and where it goes after it comes out, so for the time being, this offer is aimed at deployments on-premise to simplify matters of privacy, copyright and governance.
“It’s clearly targeted at on-premise and edge, and allowing customers to be fully in control of what they’re paying for and what data is going into it,” he said. “The cloud element is limited to getting foundation models, registering for LLMs, etc.”
The new offering adds an Opinionated AI stack and related services to Nutanix’s infrastructure and storage stacks, and the company believes it will find a good reception from the SMB to the enterprise.
Today, Nutanix is announcing Nutanix GPT-in-a-Box, a full-stack software-defined AI-ready platform designed for customers who are looking to jump-start artificial intelligence [AI] and machine learning [ML] innovation, but who don’t know quite where to start.
“Nutanix is primarily an infrastructure player, with a parallel storage stack,” said Manosiz Bhattacharyya, Nutanix’s CTO. “The infrastructure stack and the storage stack are both the same as we use here. What is new is the addition of an Opinionated AI stack on top, and services that in turn go on top of it.”
This makes the solution a stack of Nutanix Cloud Infrastructure, Nutanix Files and Objects storage, and Nutanix AHV hypervisor and Kubernetes platform with NVIDIA GPU acceleration, all on the Nutanix Cloud Platform. 78% of Nutanix customers indicated that they were likely to run their AI and ML workloads on the Nutanix Cloud Platform.
“The AI stack lets you deploy, fine tune and do inference,” Bhattacharyya said. There are foundational base models for LLM, which include all the standard open source models and a curated set of large language models like Llama2, Falcon GPT and MosaicML.
“Customers want to use AI but don’t know where to start,” Bhattacharyya indicated. “There are tons of tools today, although some, like PyTorch, have become more popular than others, like TensorFlow. And we also bundle in services, so it’s not just the infrastructure stack or the AI stack, but the services as well. What we are doing is making sure we give you the right tools with the services, and this combination of services and the right tools makes it easy and lets the customer hit the ground running.”
Nutanix sees the potential market for their GPT-in-a-Box solution as fairly large.
“We are trying to target the higher end of SMB through to larger customers,” Bhattacharyya stated. “We want to take it downstream as well. This kind of AI has been typically seen in larger markets, but there are areas like fraud detection where the use can be much broader because it makes a lot of sense in smaller markets.”
Bhattacharyya said that many prospects for this typically have both data scentists and IT, but they are siloed separately.
“The data scientists don’t understand the infrastructure side of things and IT doesn’t know what the data scientists want,” he commented. “We give them a solution where the data scientists can do their models without bothering IT.”
At the same time, Bhattacharyya cautioned that Nutanix is well aware that Generative AI today can’t have the final decision on anything important.
“In terms of where we think Generative AI is going, we see it as more of a helping tool for certified right now,” he said. “For example, in pharmacy manufacturer, you can use it to check regulations, but a person is charge has to have the authority to implement it. What has improved is the fewer number of steps that a human has to do. But there is no way at this juncture that AI can be given the authority on its own to make decisions that affect things that matter.”
There are no immediate plans to follow up on GPT-in-a-Box with near-term roadmap solutions.
“As of now, there is no extension planned to what we are offering here,” Bhattacharyya said. “We think that will suffice for a majority of customers – those who want a simple infrastructure and a stack. We may announce additional things later on, but not now.”
Service integrator partners have been particularly excited about the new solution.
“I see a lot of traction with them because services are a big part of this,” Bhattacharyya indicated. “Most companies and most industries do not have people who can create a fine-tuned AI model by themselves. We believe that service integrators can play a big part with this, and they are excited.”
In this Aug 25, 2019 photo, a Southern Vietnamese box turtle (Cuora picturata) walks in its pen at a turtle sanctuary in Cuc Phuong national park in Ninh Binh province, Vietnam. Hau Dinh/AP hide caption