Nvidia Q1 shines, splits stock 10-for-1 amid data center boom

Larry Dignan

Editor in Chief of Constellation Insights
Constellation Research

Larry Dignan is Editor in Chief of Constellation Insights at Constellation Research, where he leads editorial coverage focused on enterprise technology, digital transformation, and emerging trends shaping the future of business. He oversees research-driven news, analysis, interviews, and event coverage designed to help technology buyers and vendors navigate complex markets with clarity and context. ...

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Nvidia reported first quarter sales growth of 262% from a year ago, reported record quarterly data center revenue and split its stock 10-for-1 effective June 7.

The company reported first quarter earnings of $5.98 a share on revenue of $26 billion. Non-GAAP earnings were $6.12 a share. Wall Street was expecting Nvidia to report first quarter earnings of $5.54 a share on revenue of $24.6 billion.

CEO Jensen Huang said the AI factory upgrade cycle has begun. He said:

"Our data center growth was fueled by strong and accelerating demand for generative AI training and inference on the Hopper platform. Beyond cloud service providers, generative AI has expanded to consumer internet companies, and enterprise, sovereign AI, automotive and healthcare customers, creating multiple multibillion-dollar vertical markets."

During the quarter, Nvidia lined up a bevy of partnerships including one with Dell Technologies that advances the AI factory concept. Nvidia CEO Jensen Huang said Dell Technologies AI factory effort will be the largest go-to-market partnership the GPU maker has. "We have to go modernize a trillion dollars of the world's data centers," said Huang.

• Nvidia acquires Run.ai for GPU workload orchestration
• Nvidia Huang lays out big picture: Blackwell GPU platform, NVLink Switch Chip, software, genAI, simulation, ecosystem
• Nvidia today all about bigger GPUs; tomorrow it's software, NIM, AI Enterprise

As for the outlook, Nvidia projected second quarter revenue of $28 billion with non-GAAP gross margins of 75.5%, an outlook that indicates Nvidia has pricing power.

By the unit, data center revenue surged 23% from the first quarter and 427% from a year ago. Gaming and AI PC revenue in the first quarter was $2.6 billion, up 18% from a year ago. Professional visualization revenue in the first quarter was up 45% from a year ago and automotive and robotics sales were up 11%.

In prepared remarks, CFO Colette Kress said:

"Data Center compute revenue was $19.4 billion, up 478% from a year ago and up 29% sequentially. These increases reflect higher shipments of the NVIDIA Hopper GPU computing platform used for training and inferencing with large language models, recommendation engines, and generative AI applications. Networking revenue was $3.2 billion, up 242% from a year ago on strong growth of InfiniBand end-to-end solutions, and down 5% sequentially due to the timing of supply. Strong sequential Data Center growth was driven by all customer types, led by Enterprise and Consumer Internet companies. Large cloud providers continued to drive strong growth as they deploy and ramp NVIDIA AI infrastructure at scale, representing mid-40% of our Data Center revenue."

Constellation Research's take and conference call takeaways

Constellation Research analyst Holger Mueller said:

"Nvidia had another blow out quarter with surreal YoY comparisons. If you want to see the AI boom in a financial statement – look up the Nvidia earnings.  But all things come to an end – Nvidia is only guiding to <10% QoQ growth, which half of this quarter's QoQ growth. The question is what is slowing Nvida down – demand or supply? It could also be cloud vendors holding their CAPEX spend in anticipation of Blackwell. One thing is clear for Nvidia to be Nviida – it needs Blackwell to be a success."

Key items from the conference call include:

• Inferencing is in the mid-40s as percent of data center revenue. 
• Nvidia is speaking to total cost of ownership, which AMD is actively discussing. Kress said: "Training and inferencing AI on NVIDIA CUDA is driving meaningful acceleration in cloud rental revenue growth, delivering an immediate and strong return on cloud provider's investment. For every $1 spent on NVIDIA AI infrastructure, cloud providers have an opportunity to earn $5 in GPU instant hosting revenue over four years."
• Those cloud returns also pay off for end customers. Kress said cloud rentals remain the lowest cost way to train models as well as inferencing workloads. 
• Enterprises are showing strong demand with Meta and Tesla cited as key customers for Nvidia infrastructure. 
• Sovereign AI is expected to drive revenue in the "high single-digit billions this year."
• H200 was sampled in the first quarter with shipments in the second. Huang said production shipments will ramp in the third quarter with data centers stood up in the foruth quarter. OpenAI got the first system. "We will see a lot of Blackwell revenue this year," said Huang. 
• Kress again cited cost benefits with Nvidia HGX H200 servers: "For example, using Llama 3 with 700 billion parameters, a single NVIDIA HGX H200 server can deliver 24,000 tokens per second, supporting more than 2,400 users at the same time. That means for every $1 spent on NVIDIA HGX H200 servers at current prices per token, an API provider serving Llama 3 tokens can generate $7 in revenue over four years."
• Grace Hopper Superchip is shipping in volume. 
• Blackwell systems are reverse compatible so the transition from H100 to H200 will be seamless. That said supply will remain an issue. Huang said: "We expect demand to outstrip supply for some time as we now transition to H200, as we transition to Blackwell. Everybody is anxious to get their infrastructure online. And the reason for that is because they're saving money and making money, and they would like to do that as soon as possible."
• We're 5% into the AI data center buildout. Huang said: "We're in a one-year rhythm. And we want our customers to see our roadmap for as far as they like, but they're early in their build-out anyways and so they had to just keep on building. There's going to be a whole bunch of chips coming at them, and they just got to keep on building and just, if you will, performance average your way into it. So that's the smart thing to do. They need to make money today. They want to save money today. And time is really, really valuable to them."
• Ethernet networking a growth market for Nvidia. Huang said: "For companies that want the ultimate performance, we have InfiniBand computing fabric. InfiniBand is a computing fabric, Ethernet is a network. And InfiniBand, over the years, started out as a computing fabric, became a better and better network. Ethernet is a network and with Spectrum-X, we're going to make it a much better computing fabric. And we're committed -- fully committed to all three links, NVLink computing fabric for single computing domain to InfiniBand computing fabric, to Ethernet networking computing fabric. And so we're going to take all three of them forward at a very fast clip." 

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Snowflake Q1 mixed, acquires assets to evaluate, monitor LLMs

Larry Dignan

Editor in Chief of Constellation Insights
Constellation Research

Larry Dignan is Editor in Chief of Constellation Insights at Constellation Research, where he leads editorial coverage focused on enterprise technology, digital transformation, and emerging trends shaping the future of business. He oversees research-driven news, analysis, interviews, and event coverage designed to help technology buyers and vendors navigate complex markets with clarity and context. ...

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Snowflake reported a mixed first quarter and said it will acquire technology assets and employees from TruEra, an AI observability platform that can manage evaluate large language models (LLMs).

The company reported a net loss of $317 million, or 95 cents a share, on revenue of $828.7 billion. Non-GAAP earnings for the first quarter were 14 cents a share.

Wall Street was expecting Snowflake to report first quarter non-GAAP earnings of 17 cents a share on revenue of $785.9 million.

Snowflake’s big move this quarter was the launch of its Arctic large language model (LLM).

• Snowflake CEO Ramaswamy on product cycles, AI, scaling
• Snowflake CFO Scarpelli: Don't pigeonhole new CEO Ramaswamy as technologist
• Snowflake's Slootman steps down as CEO, technologist Ramaswamy takes over

Ramaswamy said the company saw "strong customer interest" for its AI products. Product revenue was up 34% from a year ago and Ramaswamy said its core business was strong.

As for the outlook, Snowflake projected second quarter product revenue of $805 million to $810 million, up 26% to 27% from a year ago. Product revenue in the first quarter was $780.6 million, up 34% from a year ago.

Constellation Research's take and conference call takeaways

Constellation Research analyst Holger Mueller said:

"Snowflake had another very strong quarter. But growth came at a price and Snowflake went backwards when it comes to costs, deepening its loss. It became more expensive to run its offerings, as cost of revenue rose linear to overall revenue growth. Snowflake keeps managing on the best practice of startups before interest rate hikes, with its cost structure of last fiscal year leading to a nice profit with the revenue of this fiscal year. We will see if Snowflake can keep it up. Its competitor Databricks is the all encompassing lakehouse of the cloud vendors, the foundation of analytics, and the  foundation for all forms of AI. Investors will have to watch how much Snowflake can become the AI data platform of enterprises in the coming GenAI years – and the verdict is still out."

Key items from the Snowflake conference call include:

• Ramaswamy acknowledged that Snowflake needs to become a data platform. He said: "We're still in the early innings of our plan to bring our world class data platform to customers around the globe. And in the first quarter alone, we saw some of our largest customers meaningfully increase their usage of our core offering. The combination of our incredibly strong data cloud, now powerfully boosted by AI, is the strength and story of Snowflake."
• Snowflake said data sharing and collaboration with customers can drive growth. Ramaswamy said: "Nearly a third of our customers are sharing data products as of Q1 2025, up from 24% one year ago. Collaboration already serves as a vehicle for new customer acquisition. Through a strategic collaboration with Fiserv, Snowflake was chosen by more than 20 Fiserv financial institutions and merchant clients to enable secure direct access to their financial data and insights. We announced support for unstructured data over two years ago. Now about 40% of our customers are processing unstructured data on Snowflake. And we've added more than 1,000 customers in this category over the last six months."
• Arctic model training was quick and cost effective. Ramaswamy said: "I'm comfortable with the amount of investments that we are making. Part of what we gain as Snowflake is the ability to fast follow on a number of fronts, is the ability to optimize against metrics that we care about, not producing like the latest, greatest, biggest model, let's say, for image generation. And so having that kind of focus lets us operate on a relatively modest budget pretty efficiently."

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Cognizant CEO Kumar: GenAI hyperproductivity will need to be self-funded

Larry Dignan

Editor in Chief of Constellation Insights
Constellation Research

Larry Dignan is Editor in Chief of Constellation Insights at Constellation Research, where he leads editorial coverage focused on enterprise technology, digital transformation, and emerging trends shaping the future of business. He oversees research-driven news, analysis, interviews, and event coverage designed to help technology buyers and vendors navigate complex markets with clarity and context. ...

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Cognizant CEO Ravi Kumar said the technology services firm's customers are preparing for generative AI, but need to do work in quantifying productivity gains to justify costs. Nevertheless, Kumar said generative AI will usher in a hyperproductivity phase that will affect every worker, every process and every enterprise.

Speaking at Cognizant's Analyst Day in New York, Kumar (right) covered how his company is planning for the future, scaling AI and solving long-running industry issues such as technical debt.

Kumar's big take is that generative AI will lower the cost of technology deployment and cut technology debt. "AI will drive a new productivity wave and the biggest industry to embrace AI will be technology. We will have to apply AI on ourselves and as we apply it the cost of technology deployment will change ," said Kumar. "Discretionary spending in tech over the last 25 years has happened in the low interest rate regime where there was no cost of capital. Today you need a business use case for new projects."

In other words, generative AI needs to fund itself if it's really going to transform businesses.

That theme set the agenda for Cognizant's Analyst Day. Constellation Research analyst Doug Henschen said:

"Kumar, CEO of Cognizant since January 2023, took the reins of the company at a challenging time for the systems integration market. As he acknowledged during his opening keynote, deals are mostly focused on cost take out, vendor consolidation, and cost optimization these days rather than transformation or big bets on innovation. With that said, Kumar was optimistic about a next wave of growth triggered by hyper-productivity projects driven by AI. Kumar and Cognizant's CTO, Babak Hodjat, pointed to a next wave focused on carefully curated, multi-agent AI that will be focused, robust and credible. The focus is spot on, as GenAI and LLMs alone will not deliver what Kumar described as "responsible AI" that meets the acid test of addressing safety, trust, and equity simultaneously."

Constellation ShortList™ Public Cloud Transformation Services: Global | Constellation ShortList™ Custom Software Development Services | Constellation ShortList™ Customer Experience (CX) Operations Services: Global

The push and pull of generative AI adoption

Kumar spoke of a push and pull for generative AI demand that may take years to play out. Companies are going to have to step back with a wider perspective and do the productivity studies and think through the orchestration behind generative AI.

When the cost of deployment changes you'll have a new productivity paradigm, said Kumar. "Take that technology cost out and there's an extraordinary opportunity to create hyperproductivity," he said.

"If productivity is driving generative AI, you need the studies to quantify the big use cases," said Kumar. He noted that Cognizant has funded studies and using the framework internally. "We have an AI roadmap internally and have 200 plus use cases we have identified. We have a unique opportunity to be the reference stack," said Kumar. "You have to build the last mile. The last mile is the heavy lift."

Cognizant is using its own platforms such as Neuro AI, Skygrade and Flowsource and using a "tech for tech" approach for development. The idea is to use Cognizant's platforms to drive innovation that's self-funded with productivity savings.

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• Secrets to a Successful AI Strategy
• Return on Transformation Investments (RTI)
• Financial services firms see genAI use cases leading to efficiency boom
• Foundation model debate: Choices, small vs. large, commoditization

Kumar added that Cognizant has more than 500 prototypes in the pipeline for generative AI as well as another 500 that have graduated to production. "To have the license to talk to a client you have to apply generative AI on yourself. Our biggest use case for AI is deploying it in the technology development cycle," said Kumar. "We will use AI to disrupt the operating model for the company."

So, what's the holdup for this generative AI boom?

Kumar said enterprises are reluctant to go all in without quantifiable returns. He said:

"Why is it that companies are not jumping the gun on this? The business case doesn't stack up in most places. Let's take the example of copilots. Every company has done small cohorts of copilots but companies are not going all out because the productivity studies are not complete and comprehensive. Productivity will be the first lever and it is related to business transformation and process transformation."

To help break this log jam, Kumar said Cognizant is mapping enterprise operations, tasks and roles for enterprises and itself. "We are client zero on this," he said.

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AMD sees AI inference, training workloads increasing in enterprise

Larry Dignan

Editor in Chief of Constellation Insights
Constellation Research

Larry Dignan is Editor in Chief of Constellation Insights at Constellation Research, where he leads editorial coverage focused on enterprise technology, digital transformation, and emerging trends shaping the future of business. He oversees research-driven news, analysis, interviews, and event coverage designed to help technology buyers and vendors navigate complex markets with clarity and context. ...

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AMD CFO Jean Hu said cloud providers and enterprises are starting to look toward total cost of ownership when it comes to inference and training workloads for artificial intelligence workloads.

Speaking at J.P. Morgan’s annual technology conference, Hu said data center demand for the company’s GPUs, accelerators and server processors was strong. She added that in the second half demand will be stronger than in the first. One big reason for that surge may be the news coming out of Microsoft Build 2024.

Microsoft announced the general availability of the ND MI300X VM series, which features AMD's Instinct MI300X Accelerator. The ND MI300X VM combines eight AMD MI300X Instinct accelerators. AMD is looking to give Nvidia's GPU franchise competition. On a briefing with analysts, Scott Guthrie, Executive Vice President of Cloud and AI at Microsoft, said AMD's accelerators were the most cost-effective GPUs available based on what the company is seeing with its Azure AI Service.

AMD Q1 delivers data center, AI sales surge of 80%

Hu said total cost of ownership is becoming critical as companies scale generative AI use cases.

Here are the key takeaways from Hu's talk.

The Microsoft Azure deal. actually,MI300 side, MI300X and ROCm software together actually power the Microsoft's virtual machine both for the internal workload, the ChatGPT, what open source to use, and also external workload, third-party workload," said Hu. "It's the best price performance to power the ChatGPT for inference. So that's really a proof point for not only MI300X from hardware, how competitive we are, but also from ROCm software, the maturity, how we have worked with our customers to come up with the best price performance."

She added that software investments have also been critical for TCO as AMD leverages open-source frameworks.

GPU workloads will also pull demand for CPUs. Cloud providers have more than 900 public AM instances driving adoption. Hu said that enterprises are also adopting AMD server processes because they need to make room for GPUs. Hu said:

"All the CIOs in enterprise are facing a couple of challenges. The first is more workloads. The data is more, application is more, so they do need to have a more general compute. At the same time, they need to start think about how they can accommodate AI adoption in enterprise. They are facing the challenges of running out of power and space. If you look at our Gen 4 family of processors, we literally can provide the same compute with 45% less servers."

AMD's Gen 5 server processors, Turin, will also launch with revenue ramping in 2025.

MI300 demand. "We have more than 100 customer engagements ongoing right now," said Hu. "The customer list includes, of course, Microsoft, Meta, Oracle, and those hyperscale customers, but we also have a broad set of enterprise customers we are working with." Dell Technologies AI factory roadmap has two tracks--one solely Nvidia and one that will include AMD infrastructure as well as others. 

Roadmaps. Hu said AMD has talked with customers about roadmaps for GPUs and collecting feedback. She added that AMD tends to be conservative about announcing roadmaps, but you can expect it to be competitive. "We will have a preview of our roadmap in the coming weeks," she said.

On-premise AI workloads. Hu noted that AMD is working with a bevy of hyperscalers, but enterprises are a critical customer base.

"When we talk to our enterprise customers, they do start to think about that question. Do I do it on premise? Do I send it to cloud? That is a strategic approach they have to think through. We are uniquely positioned because on the server side, we're working with our customers. We're helping them with how they deploy servers. It has become significant leverage for us. AI PC, the server side and the GPU side, that's a part of our go-to-market model right now."

Hu said inferencing workloads are strong and training is scaling. "Both training and inference are important to us," said Hu.

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Sustainability 50 interview: Tech Mahindra's Sandeep Chandna on sustainability, AI and ROI

Larry Dignan

Editor in Chief of Constellation Insights
Constellation Research

Larry Dignan is Editor in Chief of Constellation Insights at Constellation Research, where he leads editorial coverage focused on enterprise technology, digital transformation, and emerging trends shaping the future of business. He oversees research-driven news, analysis, interviews, and event coverage designed to help technology buyers and vendors navigate complex markets with clarity and context. ...

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Tech Mahindra Chief Sustainability Officer Sandeep Chandna said sustainability has become a CFO issue as the returns on investment are obvious. "Today profitability is linked to the ESG strategy and that's been a shift over the last three to four years," said Chandna.

Chandna is a member of the 2024 class of Constellation Research's Sustainability 50. Here are some takeaways from my conversation with Chandna.

The sustainability journey. Chandna has been a Chief Sustainability Officer for more than a decade and perhaps the biggest change has been that people are more educated about the role. "When I started, everybody had their own definitions of sustainability. When I got into details, somebody said you'll have to plant trees. I said that was a good thing to do. Another said donate to charity work. Once again, that's good for society. We had to build out what sustainability meant with a purpose and a vision," explained Chandna. "We charted out environmental, social and governance structures and processes that were clear. It has to be top driven but grassroots at the same time. Today every part of our strategy has sustainability in it with stakeholders, data and champions."

Small steps. Chandna said awareness of sustainability and small actions make a big difference. Tech Mahindra has Green Marshals, who drive environmental awareness. Simple things like PCs being turned off on Friday evening in an organization of 150,000 people save a lot of emissions.

Sustainability and CFOs align. "Previously, we used to go to a CFO saying what we wanted to do. The world has changed now. The CFOs are now coming to the CSO saying, 'When will you implement the net zero strategy?' or 'What's the impact of renewable energy on the bottom line?' Today profitability is linked to the ESG strategy and that's been a shift over the last three to four years." He added that ESG boosts engagement among employees and reduces attrition.

Data matters. Chandna said the data of sustainability is the hardest to put in place. Just defining what supply chain data has the biggest impact on sustainability can be challenging. Once data like carbon pricing data is in place along with water and power usage is in place the argument for sustainability efforts is much easier to make. Tech Mahindra now has the data pool in place where sustainability has its own budget line. He added that data tracking an entire procurement process through multiple partners will remain challenging, but Tech Mahindra and third-party data can get you far.

Scope 3 emissions data. Scope 3 emissions are carbon emissions that are indirectly generated by a business outside of its physical footprint. Tracking that data is a big challenge. Chandna said:

"The world is a bit confused about Scope 3 and has a lot of simple questions. If you lease a building where does that carbon data reside? We looked at our supply chain and prepared a document saying how we would build the capabilities of our suppliers first, best practices we follow and business impact. We do a workshop for all suppliers every year and reward the ones that have implemented best practices and hit goals on their scope 1, 2, 3 goals."

Chandna added that incentivizing electric vehicles for employees would help as would offices closer to homes. Employee commuting can have a big impact.  Business travel can also move the needle.

Generative AI, a sustainability blessing and curse. Today, generative AI workloads are sucking up power and stretching resources. Chandna, however, is hopeful that generative AI can analyze and optimize electricity generation and distribution, optimize trade and come up with more sustainable material design. Transportation optimization via generative AI can also improve sustainability. Those options have more long-term impacts, noted Chandna. "Today the data centers are consuming a lot of energy," he said.

In the short-term, renewable energy for data centers makes the most sense for AI workloads, but over time AI can optimize a lot to improve carbon emissions. 

 

 

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Nutanix product additions, partnerships designed to capitalize on VMware customer angst

Larry Dignan

Editor in Chief of Constellation Insights
Constellation Research

Larry Dignan is Editor in Chief of Constellation Insights at Constellation Research, where he leads editorial coverage focused on enterprise technology, digital transformation, and emerging trends shaping the future of business. He oversees research-driven news, analysis, interviews, and event coverage designed to help technology buyers and vendors navigate complex markets with clarity and context. ...

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Of course, Nutanix didn't mention VMware directly, but there are a few veiled references to its rival indicating that it smells opportunity. The Nutanix news lands a day after Rimini Street said it would offer third party support to VMware customers as they plan next steps.

The timeline since Broadcom closed its VMware purchase features a good bit of turmoil.

• May 6. Broadcom addresses concerns about VMware Cloud on AWS.
• April 15. Broadcom CEO Tan says VMware customers can get support extensions amid subscription transition.
• March 14. Tan recaps first 100 days of VMware purchase, noted that "this level of change has understandably created some unease among our customers and partners."
• Feb. 14. VMware acknowledges that changes have been rough on customers, but a long blog post could be classified as mansplaining.
• Dec. 11: VMware outlined product rationalization and subscription plan.

Here's a look at what Nutanix announced at its .Next Conference in Barcelona.

1. Nutanix added new deployment options for its Nutanix AHV hypervisor that preserves existing server investments and gives customers flexibility. Nutanix's release made a veiled reference to wooing VMware migrations after price increases. Nutanix said new capabilities in AHV will smooth migrations by repurposing the most popular vSAN ReadyNode configurations. Nutanix also added features for cybersecurity resilience, disaster recovery and virtual machine clusters.
2. Nutanix said it is working with Cisco to certify Cisco UCS blade servers so enterprises can redeploy existing deployed servers to run on Nutanix AHV hypervisor with compute only nodes or storage only nodes.
3. Dell Technologies andNutanix outlined a series of deployment options, partnerships and platform enhancements at its .Next Conference in Barcelona. The gist of the news: VMware we have you surrounded. Nutanix will launch hyperconverged appliances combining Nutanix Cloud Platform and Dell servers. The combination will cover a broad range of PowerEdge servers. The companies also said Nutanix Cloud Platform for Dell PowerFlex will combine Nutanix's platform with its AHV hypervisor with Dell PowerFlex storage. Dell and Nutanix also said they will collaborate on engineering and go-to-market efforts. Keep in mind that Dell said it was exiting its VMware hyperconverged partnership.
4. Nutanix added new integrations for Nutanix-GPT-in-a Box that includes Nvidia NIM inference microservices and Hugging Face large language models (LLM). The company also launched an AI partner program that'll enable companies to build generative AI apps on top of Nutanix Cloud Platform.
5. Red Hat and Nutanix said they will collaborate to use Red Hat Enterprise Linux as an element of Nutanix Cloud Platform. Nutanix AOS, which is part traditional operating system with additional packages, will build on Red Hat Enterprise Linux for operating system capabilities.
6. Nutanix launched the Nutanix Kubernetes Platform (NKP) to simplify the management of container-based applications. Enterprises can manage clusters running Nutanix and third parties on one dashboard. NKP integrates with data services, simplifies management with automation, offers multi-cluster fleet management and is cloud native.

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Microsoft Build 2024: Azure gets AMD MI300X Accelerators, Cobalt preview, OpenAI GPT-4o

Larry Dignan

Editor in Chief of Constellation Insights
Constellation Research

Larry Dignan is Editor in Chief of Constellation Insights at Constellation Research, where he leads editorial coverage focused on enterprise technology, digital transformation, and emerging trends shaping the future of business. He oversees research-driven news, analysis, interviews, and event coverage designed to help technology buyers and vendors navigate complex markets with clarity and context. ...

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Microsoft announced a bevy of additions to Azure including AMD Instinct MI300X instances, Cobalt 100 instances in preview and the latest OpenAI model, GPT-4o, in Azure OpenAI Service.

The announcements at Microsoft's Build 2024 conference land as both Amazon Web Services and Google Cloud are busy launching custom silicon and access to multiple model choices.

All of the hyperscalers are looking to supply supercomputers that have diversity of custom silicon and processors from AMD and Nvidia as well as networking and architecture choices for AI workloads. Constellation Research analyst Holger Mueller said:

"It is clear that the path to AI is custom algorithms on custom silicon and Microsoft is on the jouney, with both the Cobalt and the AMD Mi300 preview. A key aspect of faster CPUs is faster networking, but Microsoft is quiet on that. Amongst all the cloud vendors, Microsoft has its traditional partner connections - so the AMD chip uptake comes as no surprise. When viable we will likely see Intel in Azure as well."

• Microsoft Azure revenue in Q3 up 31% | Microsoft Build 2024: Microsoft Fabric opens up, adds real-time intelligence, Snowflake, Databricks connections

Here's a look at the three headliners for Azure at Build along with other key additions.

• Microsoft announced the general availability of the ND MI300X VM series, which features AMD's Instinct MI300X Accelerator. The ND MI300X VM combines eight AMD MI300X Instinct accelerators. AMD is looking to give Nvidia's GPU franchise competition. On a briefing with analysts, Scott Guthrie, Executive Vice President of Cloud and AI at Microsoft, said AMD's accelerators were the most cost-effective GPUs available based on what the company is seeing with its Azure AI Service. AMD Q1 delivers data center, AI sales surge of 80%
• Azure virtual machines built to run on Cobalt 100 processors are available in preview. Microsoft announced Cobalt in November and claimed that it could deliver 40% better performance than Azure's previous Arm-based VMs. Guthrie also noted that Cobalt performs better than the current version of AWS' Trainium processor. "It's going to enable even better infrastructure and better cost advantage and performance on Azure," said Guthrie, who was referencing Cobalt on Azure. Guthrie added that Snowflake was developing on Cobalt instances. 

• OpenAI's GPT-4o will be available exclusively on Azure. Microsoft said OpenAI's latest flagship model will be available in preview on Azure AI Studio and as an API. OpenAI just launched GPT-4o last week, a day ahead of Google's latest Gemini models.
• To round out the Azure OpenAI Service upgrades, Microsoft enhanced fine-tuning of GPT-4, added Assistants API to ease creation of agents and launched GPT-4 Turbo with vision capabilities. Microsoft also said it is adding its multimodal Phi-3 family of small language models to Microsoft Azure AI as a model as a service offering.
• Azure will also get new services for managing instances. Microsoft launched Azure Cloud Fleet, a service that provisions Azure compute capacity across virtual machine types, available zones and pricing models to mix and match performance and cost. The company also launched Microsoft Copilot in Azure, which is an assistant for managing cloud and edge operations.

Nadella: We're a GenAI platform company

Speaking at the Build 2024 keynote, Microsoft CEO Satya Nadella said the age of generative AI is just starting.

"We now have these things that are scaling every six months or doubling every six months. You know, what we have though, with the effect of the scaling laws is a new natural user interface that's multimodal that means support stack speech, images, video as input and output. We have memory that retains important context recalls both our personal knowledge and data across our apps and devices. We have new reasoning and planning capabilities that helps us understand very complex context and complete complex tasks while reducing the cognitive load on us."
 
Nadella said Microsoft has always been a platform company and Azure is "the most complete scalable AI infrastructure that meets your needs in this AI era."

"With building Azure as the world's computer, we have the most comprehensive global infrastructure with more than 60 plus data center regions," said Nadella, who noted that the company is optimizing power and efficiency across the stack. 

Nadella said Azure will be built out with Nvidia, AMD and its own silicon in clusters. He said Maya and Cobalt, Microsoft's custom processors, are already delivering customer workloads and responding to prompts.  

Mueller said:

"Microsoft needs to wean itself and OpenAI of Nvidia machines that are expansive and the hardest commodity to purchase in IT. Continuing the Cobalt strategy makes sense, adding AMD as well, but it will not help with the existing workloads. The question is – will Microsoft rebuild OpenAI models? – or support two different AI hardware platfoms and choose what to run where. Time will tell."

More:

• Microsoft names Suleyman head of consumer AI, Microsoft AI
• Microsoft expands at Coca-Cola as part of multi-cloud strategy that includes AWS
• Microsoft raises Dynamics 365 prices starting Oct. 1
• Microsoft's Copilot for Security will go for $4 an hour
• Quantinuum, Microsoft claim quantum reliability breakthrough
• Foundation model debate: Choices, small vs. large, commoditization

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IBM open sources Granite models, integrates watsonx.governance with AWS SageMaker

Larry Dignan

Editor in Chief of Constellation Insights
Constellation Research

Larry Dignan is Editor in Chief of Constellation Insights at Constellation Research, where he leads editorial coverage focused on enterprise technology, digital transformation, and emerging trends shaping the future of business. He oversees research-driven news, analysis, interviews, and event coverage designed to help technology buyers and vendors navigate complex markets with clarity and context. ...

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IBM has open sourced its portfolio of Granite large language models under Apache 2.0 licenses on Hugging Face and GitHub, outlined a bevy of AI ecosystem partnerships and launched a series of assistants and watsonx powered tools. The upshot is that IBM is looking to do for foundational models what open source did for software development.

The announcements, made at IBM's Think 2024 conference, land as the company has made a bevy of partnerships that put it in the middle of the artificial intelligence mix either as a technology provider or services provider. For instance, IBM and Palo Alto Networks outlined a broad partnership that combines Palo Alto Networks' security platform with IBM's models and consulting. IBM Consulting also partnered with SAP and ServiceNow on generative AI use cases and building copilots.

IBM also recently named Mohamad Ali Senior Vice President of IBM Consulting. Part of his remit is melding IBM's consulting and AI assets into repeatable enterprise services.

All these moves add up to IBM positioning itself as a partner to enterprises to scale AI across hybrid cloud and platforms. 

The backdrop of IBM's announcements, according to IBM CEO Arvind Krishna, is scaling AI. During his keynote he summed up the state of generative AI. 

"There's a lot of experimentation that's going on. That's important, but it's insufficient. If you watch every one of the previous technologies, the history has shown you kind of move from innovating a lot to deploying a lot. As you deploy it is where you will get the benefits but in order to deploy, you also need to start moving from experimenting to working at scale. Think about a small project, then you got to think about it in an enterprise scale in a systemic way. How do you begin to expand it? How do you begin to make it have impact across an enterprise or across a government? And that is what is really going to make it come alive."

Here's how IBM sees itself in the AI ecosystem.

And here's a look at what IBM announced at Think 2024:

IBM open-sourced its Granite models and made them available under Apache 2.0 licenses on Hugging Face and GitHub. The code models range from 3 billion to 34 billion parameters and are suitable for code generation, bug fixing, explaining and documenting code and maintaining repositories.

Krishna said that IBM's move to open source Granite and highlight smaller models has multiple benefits. "We also want to make sure that you can leverage smaller purpose LLM models. Can a small model do as much maybe using 1% of the energy and 1% of the total cost?" he said. "We believe that actually having a smaller model, but that is tuned for a purpose. Rather than one model that does all the things that we can actually make models that are fit for purpose."

IBM launched InstructLab, which aims to make smaller open models competitive with LLMs trained at scale. The general idea is that open-source developers can contribute skills and knowledge to any LLM, iterate and merge skills. Think of InstructLab as the open-source software equivalent of AI models.

Red Hat Enterprise Linux (RHEL) AI will include the open-source Granite models for deployment. RHEL AI will also feature a foundation model runtime inferencing engine to develop, test and deploy models. RHEL AI will integrate into Red Hat OpenShift AI, a hybrid MLOps platform used by Watson.ai. Granite LLMs and code models will be supported and indemnified by Red Hat and IBM. Red Hat will also support distribution of InstructLab as part of RHEL AI.

• IBM acquires HashiCorp for $6.5 billion, reports mixed Q1

IBM launched IBM Concert, a watsonx tool that has one interface for visibility across business applications, clouds, networks and assets. The company also launched three AI assistants built on Granite models including watsonx orchestrate Assistant Builder and watsonx Code Assistant for Enterprise Java Applications. For good measure, IBM launched watsonx Assistant for Z for mainframe data and knowledge transfer.

Via a partnership with AWS, IBM is integrating watsonx.governance with Amazon SageMaker for AI governance of models. Enterprises will be able to govern, monitor and manage models across platforms.

IBM will indemnify Llama 3 models, add Mistral Large to watsonx and bring Granite models to Salesforce and SAP. IBM watsonx.ai is also now certified to run on Microsoft Azure.

Watsonx is also becoming an engine for IT automation. IBM is building out its generative AI observability tools with Instana, Turbonomic, network automation tools and Apptio's Cloudability Premium. These various parts were mostly acquired.

Constellation Research's take

Constellation Research analyst Holger Mueller said:

"IBM is pushing innovation across its portfolio and of course it is all about AI. The most impactful is probably the open sourcing the IBM Granite models. With all the coding experience and exposure that IBM has, these are some of the best coding LLMs out there, and you can see from the partner momentum, that they are popular. The release of IBM Concert is going to be a major step forward for IBM customers running IBM systems and 3rd party systems. Of note is also the release of Qiskit, the most popular quantum software platform, that IBM has significantly increased in both capabilities and robustness. These three stick out for me as the most long term impact for enterprises."

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Zoom Q1 earnings strong, sees growth in employee experience

Larry Dignan

Editor in Chief of Constellation Insights
Constellation Research

Larry Dignan is Editor in Chief of Constellation Insights at Constellation Research, where he leads editorial coverage focused on enterprise technology, digital transformation, and emerging trends shaping the future of business. He oversees research-driven news, analysis, interviews, and event coverage designed to help technology buyers and vendors navigate complex markets with clarity and context. ...

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Zoom said its Zoom AI Companion is gaining traction and the company is betting that Zoom Workplace can drive demand for its portfolio.

Zoom reported first quarter earnings of $216.3 million, or 69 cents a share, on revenue of $1.14 billion, up 3.2% from a year ago. Non-GAAP earnings in the quarter were $1.35 a share. Wall Street was expecting Zoom to report first quarter non-GAAP earnings of $1.19 a share on revenue of $1.13 billion.

Last week, Zoom said its Workvivo employee engagement tool will be the sole migration partner for Meta's Workplace offering, which is being shut down. 

By the numbers:

• Zoom had 3,883 customers contributing more than $100,000 in trailing 12 months revenue.
• The company had 191,000 enterprise customers, but Zoom noted that it moved 26,800 enterprise customers to an online sales channel.
• Churn in the quarter was 3.2%.
• Zoom Phone has 5 customers with 100,000 seats.
• Zoom AI Companion has more than 700,000 customer accounts enabled.
• 90 Contact Center accounts had more than $100,000 ARR. 
• The company ended the quarter with $7.4 billion in cash, cash equivalents and marketable securities.

Speaking on an earnings call, Zoom CEO Eric Yuan said AI enhancements were boosting demand of Zoom Workplace, Zoom Phone, Team Chat, Events and Whiteboard. Yuan added that Workplace integration with Workvivo will also enhance employee engagement. Yuan said the deal with Meta to migrate Workspace customers to Workvivo will also be positive.

"Our success in employee experience represents an important beachhead for us in upselling customers on the full suite," said Yuan.

As for the outlook, Zoom projected second-quarter revenue of $1.145 billion to $1.15 billion with non-GAAP earnings of $1.20 a share and $1.21 a share. Sales were in line with estimates, but missed Wall Street earnings targets of $1.24 a share.  For fiscal 2025, Zoom projected revenue of $4.61 billion and $4.62 billion with non-GAAP earnings of $4.99 a share to $5.02 a share. Both projections were ahead of expectations.

"We still believe that Q2 will be the low point from a year-over-year growth perspective and to improve from there," said Yuan. 

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