Dell Partners with AMD for Enhanced AI Server Portfolio, Boosting Generative AI Capabilities

Dell has expanded its high-performance computing portfolio for AI workloads by adding an AMD-powered server, specifically designed to support large language models (LLMs). This new addition complements Dell’s existing Nvidia-powered options, offering customers more variety in their AI infrastructure choices.


The latest server has 8 AMD M1300X acceleratorsThe latest server, the Dell PowerEdge XE9680, is equipped with eight AMD Instinct MI300X accelerators. These accelerators are notable for their 1.5GB of high-bandwidth memory (HBM3) and their impressive performance capacity, exceeding 21 petaFLOPS. This makes them particularly suitable for businesses looking to train and operate their own in-house LLMs.


DellOne of the key features of the new Dell offering is its scalability. Customers can scale the systems they deploy using the global memory interconnect (xGMI) standard. Additionally, AMD’s GPUs can be connected over an Ethernet-based AI fabric with a Dell PowerSwitch Z9664F -ON. This release follows Dell’s earlier launch of a unit fitted with Nvidia H100 GPUs.


Another significant aspect of Dell’s strategy is the introduction of a new standard called Dell Validated Design for Generative AI with AMD. This standard provides a framework for organizations to run their own hardware and networking architecture for LLMs. It emphasizes the use of AMD ROCm powered AI frameworks, an open-source package including drivers, development toolkits, and APIs compatible with AMD Instinct accelerators. These frameworks support popular AI software like PyTorch, TensorFlow, and OpenAI Triton, all of which have native support on the PowerEdge XE9680 fitted with AMD accelerators .


Dell’s commitment to standards-based networking, as demonstrated by its membership in the Ultra Ethernet Consortium (UEC), signifies a departure from Nvidia’s approach. Unlike Nvidia, AMD and Dell advocate for an open Ethernet for AI, enabling interoperability of switches from different vendors within the same system.

Dell’s approach encourages businesses to adopt an open strategy encompassing computing, fabric, and storage components essential for powering in-house generative AI models.
The new hardware and services that constitute Dell’s latest push in AI are expected to be available in the first half of the next year. This development is part of Dell’s broader effort to provide versatile and powerful solutions for businesses engaging in generative AI and other advanced computing tasks.


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Year ender 2023: Elon Musk’s Grok AI, Google Gemini, GPT-4; Checkout the top 5 AI launches of 2023

The Grok logo on a smartphone arranged in New York, US, on Wednesday, Nov. 8, 2023. Elon Musk revealed his own artificial intelligence bot, dubbed Grok, claiming the prototype is already superior to ChatGPT 3.5 across several benchmarks

2023 was the year when generative AI really made its mark, leading to people reacting with a mixture of shock and amazement at the potential of the new technology, while a wave of panic also hit many industries about how AI could eventually take jobs away from humans .

Nevertheless, 2023 has seen a number of AI-related announcements, including OpenAI’s GPT-4 language model, Elon Musk’s Grok AI, Microsoft’s Bing Chat and Google’s Gemini language model. We take a look at the top 5 AI launches of 2023.Top 5 AI launches of 2023: 1) Bird AI: 
Probably a little taken aback by the sudden rise of generative AI technology, Google rushed to launch its own chatbot, named Bard, as an ‘early experiment’. Later in the year, Google continued to make its AI chatbot more feature-rich, including the addition of a more powerful language model called Gemini.

The company has also said that it will make the Bard AI chatbot even more powerful with the addition of the Gemini Ultra language model.


2) Bing Chat: Shortly after the launch of the Bard AI chatbot, Microsoft also launched its own generative AI-based offering, integrating it tightly into its Bing search engine and calling the new tool Bing Chat. Although the initial reception of Bing Chat wasn’t as great as the company had hoped, Microsoft has continued to add features to the chatbot, including support for the GPT-4 language model, the creation of images using OpenAI’s DALL-E, and now the chatbot is even said to have the ability to create songs. 
3) GPT-4: Perhaps one of the biggest AI launches of the year was the GPT-4 language model, the successor to the earlier GPT-3.5 model that powered ChatGPT. OpenAI claimed at the time that the GPT-4-powered ChatGPT demonstrated human-level performance on various professional and academic benchmarks. The company also said that GPT-4 was found to be more reliable, creative and capable of handling much more nuanced instructions than its predecessor.

However, OpenAI only allowed access to its latest language model via a $20/month ChatGPT Plus subscription.


Later in the year, OpenAI introduced another upgrade to its language model in the form of GPT-4 Turbo, which had a much larger context window than its predecessors. According to OpenAI, GPT-4 Turbo can fit over 300 pages of text into a single prompt.
4) DALL-E 3: In September, OpenAI decided to improve its already popular text-to-image generating software, DALL-E. The company claimed that DALL-E 3 which now came with integration with ChatGPT could allow users to ‘craft amazing prompts’ and bring their ideas to life.
However, much like GPT-4, DALL-E 3 was available only to the company’s premium customers with a ChatGPT Plus subscription. However, the new technology was soon available for free via Bing Chat.5) Grok AI: Continuing his complicated relationship with artificial intelligence, Elon Musk finally decided to launch his own AI company, xAI, to compete with the likes of Google and OpenAI.


xAI’s first product is a chatbot called Grok, inspired by The Hitchhiker’s Guide to the Galaxy and designed to answer questions with a bit of wit and humour.
What makes Grok different from other chatbots on the market is that Elon Musk’s Gen AI offering has real-time knowledge of the world thanks to the data from X platform.

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Back BackFrom GPT-5 to AGI; Sam Altman reveals the most commonly requested features from ChatGPT maker in 2024OpenAI CEO Sam Altman has listed the most requested features from the ChatGPT maker in 2024. The list of requested features includes many notable mentions, including artificial general intelligence, GPT-5 language model, more personalization, better GPTs and more.


The suggestions were in response to a question posed by Altman on X (formerly Twitter), where he asked his followers what they would like OpenAI to build or fix in 2024.“will keep reading, and we will deliver on as much as we can (and plenty of other stuff we are excited about and not mentioned here)”, the OpenAI CEO promised in an ensuing post on X.


While listing the most requested features of Open AI , Altman added a caveat about AGI, noting that users will have to be patient and implying that an AI model from the company that reaches the level of AGI in 2024 remains highly unlikely.
Speaking to Time magazine earlier this month, Altman had shed light on the limitless potential of the new technology. He said: “I think AGI will be the most powerful technology humanity has yet invented…If you think about the cost of intelligence  and the equality of intelligence, the cost falling, the quality increasing by a lot, and what people can do with that,”“It’s a very different world. It’s the world that sci-fi has promised us for a long time—and for the first time , I think we could start to see what that’s gonna look like.” the 38 year old added.


Open AI announced its GPT-4 Turbo language model at the company’s first developer conference in November. The new language model has knowledge of world events up to April 2023 and was seen as a major upgrade over GPT-4, which was released in May.
Meanwhile, at the same event, OpenAI also announced that it would allow users to create their own Generative Pre-trained Transformers (GPTs) and share them publicly. The AI ​​​​startup had said it would also launch a GPT store to help verified developers monetise their offerings. However, the drama surrounding Sam Altman’s sacking and subsequent re-hiring at the AI ​​firm has reportedly led to the GPT store’s release being pushed back to 2024.

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Powering Inventory Availability and Order Management With AI

For retailers selling online and offline, artificial intelligence is transforming inventory availability management and order fulfillment.


This article was written and paid for by Fluent Commerce. This content is not WIRED editorial content. The content does not necessarily reflect the views of WIRED, its affiliates or owners, nor does it reflect any direct or indirect endorsement by Fluent Commerce or Business Reporter , its affiliates or other customers.Artificial intelligence is a major trend in business right now. The value of AI in the retail industry is expected to grow from $3.7 billion in 2021 to $16.8 billion in 2030, a healthy compound annual growth rate (CAGR) of 15.7%.


The use of artificial intelligence in customer-centric areas such as advertising and communications is well known. One only has to think of today’s automated chatbots to see the important role AI is already playing in retail customer service.
One area that is less widely known is the use of artificial intelligence in inventory levels. But in fact, inventory management ranks second only to customer service as a use case for AI< /span>, with nearly half of retailers (47%) saying AI can greatly enhance inventory management by tracking inventory online and at physical locations, facilitating true Provide customers with an omni-channel experience.
Transform inventory and order management


Clearly, there are significant opportunities in inventory availability and order management, where AI can help businesses become more efficient and maximize profits. Let’s look at a few.
Inventory availability optimization


Overstocking and understocking are two problems any retailer wants to avoid. Artificial intelligence helps retailers optimize inventory levels, improve efficiency and profitability. Demand forecasting is a particularly powerful tool here: AI predicts future demand based on historical data and other factors, allowing orders to be routed to the best locations to maintain optimal inventory levels.


Demand sensing is another important use case. This involves short-term demand forecasting, which can alert you if the stock status of a SKU is at risk of being out of stock, or if order sourcing rules are inappropriate based on current stock.


AI can also help manage safety stock. Safety stock is additional inventory kept to reduce the risk of stockouts. However, in some cases, inventory buffers can lead to sell-offs. Artificial intelligence enables dynamic safety stock by constantly checking inventory status, current demand and forecast sales, then automatically updating safety stock levels. The technology can improve inventory turns across the entire retail point network (online and offline).
Another advantage of AI is that it can improve procurement logic. Order sourcing involves sending orders to the best locations based on the retailer’ ;s business goals. These may include fast delivery; reducing partial shipments; shipping costs; or shipping from locations with the slowest inventory movement to avoid markdowns. By optimizing purchasing logic, retailers can enhance profitability, improve inventory turns, reduce markdowns and inventory waste , and enhance sustainability.


Typically, order sourcing is managed through simple purchasing rules, such as shipping from the location closest to the customer. Artificial intelligence provides valuable opportunities to quickly use richer data sets. This can include location attributes such as labor capacity, the maximum number of open orders the location can handle, shipment damage rates, and average order processing speed. or product attributes, such as whether the item is fragile, bulky, or must be shipped separately. or location-specific inventory attributes, such as inventory duration, sell-out rates, or likelihood of price reductions. By uncovering complex data patterns, order procurement can be transformed into a strategic process that drives sales and improves customer satisfaction.


Logistics optimization
To achieve the most efficient fulfillment operations, logistics processes must be optimized. This may include consolidating orders using existing trunk routes, minimizing cross-docking or holding times.


Order management and tracking are at the heart of this. AI can track multiple orders in real time and can identify inventory issues, production delays and delivery bottlenecks. AI models can also incorporate other data, such as telematics, into the analysis to optimize shipping routes and ensure timely delivery of orders.
Achieve success with artificial intelligence
These are huge opportunities for retail. However, being successful with AI requires knowing the right questions to ask the AI ​​​model. You need to your business metrics and the improvements understand you want to make.


For example, do you want to increase inventory turns, reduce multiple inventory moves, or improve cross-selling by ensuring that new and popular product mixes are readily available? Once youknow the questions you want to ask, you will be able to identify the data you need to provide the answers.
Data quality
Often the questions that you can answer with traditional systems are limited by the data that is available. Using AI-powered systems uncovers new opportunities to use data sets that were not available before.But even AI models need data of sufficient quality. And unfortunately, when it comes to order and inventory processes, data of sufficient quality can be hard to find.
Inventory data is often poor quality. It may sit in multiple systems, stored in different formats, much of it poorly structured, and some of it is incomplete or polluted by inaccurate or out-of-date information. In addition, it may contain irrelevant data that will cause AI systems to deliver biased outputs.
Providing quality data to train and operate AI models is a challenge. For most AI projects, approximately 80 percent of the cost is getting the data right. And in many cases, even after a lot of effort, organizations find they don’t have the right data. So, the project fails before it is launched.


Finding Good Data
The data you need will depend on the questions you want to ask. This means being able to capture the right data and make it available to the AI ​​​model.
A modern order-management system such as Fluent Order Management can provide a continuous stream of sales data points on demand, together with related contextual data, including location (capacity or opening hours, for example), order (order date, delivery date), product (weight, fragility), and customer (credit status, return rate). This contextual data can be extremely valuable and should be stored, not purged or condensed, so it’s available for future analysis.
A Seamless Experience
The role of inventory availability and order management is to deliver a seamless omnichannel experience for consumers while bolstering retailer profitability. Modern, event-based systems such as Fluent Order Management capture all the data signals that enable retailers to take full advantage of AI models.
What’s more, it provides short-term value as well. This enables retailers to get an accurate real-time view of their inventory so they can increase fill rates and reduce the number of orders that are canceled because of delays.
With Fluent Order Management, inventory availability and fulfillment processes can be managed by region or channel to enable growth and support local needs. And order management processes can be integrated with other business systems that need to be aware of inventory levels. For example, advertising platforms can be managed so that investment is not wasted on advertisements for out-of-stock items.


Alongside advances in customer experience, such as chatbots and personalized shopping, today’s AI-enabling inventory availability and order management systems are enhancing retail profitability by maximizing sales, increasing fulfillment speed, and minimizing waste.
To find out more about how AI can transform your inventory availability and order management, visit fluentcommerce.com

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