ANETArista Networks, Inc.

“Just to remind everybody, we have raised now from about $10.5 billion last September to $11.5 billion. And yes, a high degree of that is AI, but we have aggressive commitments on the campus to go to a $1.25 billion year and continue to service and grow our data center and cloud just as well.”

“we are now pleased to raise our 2026 fiscal year outlook to 27.7% revenue growth, delivering approximately $11.5 billion. We maintain our 2026 campus revenue goal of $1.25 billion, and raise our AI fabrics goal from $3.25 billion to $3.5 billion.”

“Our purchase commitments at the end of the quarter were $8.9 billion, up from $6.8 billion at the end of Q4. As mentioned in prior quarters, this expected activity mostly represents purchases for chips related to new products and AI deployments.”

“We also increased our AI target now to $3.5 billion this year, thereby more than doubling our AI sales annually.”

“I think last year on scale across we were just beginning, so I think they were small numbers. The majority of the numbers were really scale out. That is our heritage, and that is where we excel. If I were to anticipate how it would be this year, again, scale up is virtually zero and nonexistent because it really only comes to play after the ESUN spec, so consider that more a 2027–2028 number. I think the number will be shared between scale across and scale out. I do not know if I can say it is 50/50 or 70/30 or 60/40, but scale across will definitely contribute at least a third of our AI number.”

“Many of these new products in the Arista EtherLink family, particularly for the AI, are brand new—brand new chips, brand new software. Familiarity with it, particularly in the back end for scale out and scale across, is new to them. So there is a level of testing and making sure it works for the rest of their ecosystem, including the front end, that is super important, and Arista Networks, Inc. bears a huge responsibility for that as well. So all this to tell you, the length of time to qualify this, which used to be two to four quarters, has extended more like six to even eight quarters.”

“Arista Networks, Inc. is a shining example here with greater than 100 cumulative customers to date in 800 gigabit Ethernet deployments, and we expect the addition of 1.6 terabit in 2027 at production scale.”

“The two of them have very interesting characteristics. They exhibit what I would call the three use cases I just alluded to—scale up, scale out, and scale across—where we really have a fabric notion of creating. So far we have been working with them a lot on the front end; now we get to complement that on the back end, definitely for scale out and scale across and maybe even a little bit of scale up in some of these use cases. The other thing we are seeing with a lot of these use cases is the lack of power in sites and the ability and demand to distribute and get a more multi-tenant scale across is very high in these two use cases.”

“while today we are in a training fever, a more distributed AI, generative AI paradigm with instances—which means you do not always need the GPU—you are going to have high-end CPUs and a smaller set of parameters and tokens to manage, and you are going to have specific agentic AI use cases and applications. We are seeing very early trials and stages—nothing super big yet. They are not in the hundreds of thousands of GPUs like you see with the AI titans. But we are frequently seeing our customers in certain high-tech sectors want to deploy clusters that are a thousand, a few thousand—definitely not 10,000—in the low thousands.”

“Our first highlighted win is a Neo Cloud AI network. The customer was constrained by an incumbent white box architecture that simply could not keep pace with the massive scale-out requirements of AI. Arista Networks, Inc. was selected as a commercially proven and reliable scale-out architecture, with unmatched stability of EOS and the ability to connect AMD MI series XPUs. Arista's AI leaf and spine EtherLink products were deployed at 800G to provide the incredible performance modern AI networks require.”

“we are now seeing way more back-end activity, particularly with our large AI titans and cloud titans, because there is just so much scale they need to prepare for the billions of parameters and tokens. So much so that I think the front end, they might come back and refresh, but they are almost ignoring right now in favor of the back end. Having said that, by virtue of the back-end deployments, I do not know if we see a two-to-one to the front end anymore, but we at least see a one-to-one.”

“I would like to take a moment to review our three AI fabric use cases. In scale up mode, we have familiar technologies such as NVLink and PCIe that have enabled vertical scaling of single compute nodes or racks. The advent of eSun, Ethernet for scale up networking specifications, allows for increasing or decreasing computing power in a flexible manner with Ethernet to automatically adapt to workload demands. Scale up will be a new entry for Arista Networks, Inc. in 2027 and beyond”

“While the industry has been talking a lot about co-packaged optics, these are still science experiments, and they are very proprietary with individual vendors doing their own thing. We embrace open CPO a few years from now, but we think XPO has a ten-year run, especially at 1.6T and 3.2T where you need liquid cooling and you need that kind of capacity. So all those scale-up racks we are talking about would not be possible without XPO or CTC or any one of those technologies.”

“In general, we are seeing diverse accelerators. Last time, I spoke about the AMD accelerators. This time, I will definitely give a nod to the TPUs, because in particularly scale-across use cases, we are seeing multi-tenants connecting to different AI accelerators, including GPUs as well. So I think the diversity of accelerators is creating tremendous multi-accelerator opportunity and multi-protocol features that we can provide for them in our network.”

“there is no doubt in our minds that we will have a number of racks and a number of scale-up use cases in 2027. Maybe some of them will be in early trials, but the majority of them are looking at really starting with 1.6T and 1.6T will really happen in 2027. There may be a few, a handful of them, some experimental stuff at 800G. But we continue to see at least five to seven rack opportunities.”

“This fourth customer from the group has officially moved from InfiniBand to Ethernet at production scale over the last two years. The high-speed Ethernet AI design with flexible air or liquid-cooled infrastructure overcomes the physical constraints of power and space for AI workloads. It results in a low-latency distributed AI supercomputer fabric across global regions.”

“the two big ones—we never take them for granted—Microsoft and Meta, they are all-time favorites. They have been 10% and greater customers for over a decade, and the partnership could never be stronger. And it continues to get better both in cloud and in AI. In terms of the new entrants, we still expect at least one, maybe two.”

“we have surging demand, especially on the newest platforms, which of course is driving our need for the most modern silicon from our providers, and it is driving a need for an expanded amount of memory—even more than we were expecting before the year began. That is driving us to be a buyer in the market.”

“At 400G and 800G you will be fine with OSFP. As we go to higher speeds in 2027–2028 or beyond, OSFP will run out of steam, and XPO will be the new connector of choice. So the migration to higher speeds equals the migration to XPO, particularly for scale out and scale across.”

“What XPO unlocks is a standard, interoperable way to get to four times the faceplate bandwidth with liquid cooling, which is absolutely critical for these AI use cases. Without that, you have this huge bottleneck at the front panel.”

“agentic AI is kind of a buzzword. Let me break it into how the biggest killer application we see in agentic AI right now is still training. And indeed, it is going to move to more distributed inference, and we would also like to see agentic AI move into a lot of enterprise use cases—all of which we are seeing, by the way. But I would say large, medium, small: the largest killer agentic AI application is training; the medium is inference; and the small is enterprise.”

“how good it feels to have the same set of products and the same common operating system, management suite, and operating model across the front end and back end. This lowers cost to the customer, simplifies their design process, and we are one of the few vendors who can do that.”

“We remain in a period of ramping our new products, winning new and expanding use cases, including AI. These trends have resulted in increased customer-specific acceptance clauses and an increase in the volatility of our product deferred revenue balances.”

“Arista's EtherLink portfolio addresses both the synchronous flows for massive training and the low latency for concurrent swarms of real-time inference in this era of trillions of tokens, terabits of performance, and terawatts of power.”

“Our cloud and AI networking strategy for diverse AI accelerators continues to gain traction. Unlike typical workloads, AI workflow patterns can be long-lived elephant flows, or short-lived and simply not predictable.”

“I would agree with you that scale across is by far the most significant and differentiated opportunity that really highlights Arista Networks, Inc.'s prowess in both platforms and software.”

“It is underappreciated, and the Neo Cloud is very strong this quarter, if I recall, Chantelle, for us in the specialty and cloud providers.”

“we certainly do not want to keep GPUs idle and AI infrastructure underutilized because Arista Networks, Inc. did not supply the network.”

“scale across is the common denominator in all our use cases, and scale up and scale out may be nice options in brand-new greenfields.”

“Growth was seen across the customer sectors, led by our AI and specialty provider customers within the quarter.”

“It enhances our ability to better cope with the many risks that AI is creating.”