Tag: Together AI

  • Equinix Bets Colocation Can Become an AI Inference Utility

    Equinix Bets Colocation Can Become an AI Inference Utility

    TL;DR · 30-second read

    The Short Version

    Equinix rents out the buildings where companies keep their computers and where the world’s networks plug into each other. Now it wants to sell the computing itself.

    With the chipmaker NVIDIA and a startup called Together AI, Equinix has launched a service that runs artificial-intelligence models inside those buildings, close to the businesses using them. The pitch: faster answers, and your data stays where you put it.

    What it costs, how much of it exists, and who has signed up have not been made public.

    Equinix has launched Inference Exchange, a distributed artificial-intelligence inference platform developed with NVIDIA and Together AI, W.Media and Telecompaper reported this week, alongside a new connectivity service. The offering is intended to let enterprises run trained AI models inside Equinix’s metro data centers rather than in a distant public cloud region.

    The companies have not published the platform’s scale. There is no disclosed figure for dedicated capacity, no list of launch metros, no pricing model and no named customers, and Equinix did not accompany the announcement with a material filing to the U.S. Securities and Exchange Commission.

    Executive Summary

    The announcement is a move up the value chain. Equinix’s core business is selling space, power and cross-connects — the physical patch cables that let a bank’s network meet a cloud provider’s network inside the same building. Inference Exchange proposes selling the workload instead: NVIDIA accelerators and Together AI’s serving software, operated in Equinix facilities and reachable over the interconnection fabric its customers already buy.

    The timing tracks the industry’s center of gravity. NVIDIA’s second-quarter fiscal 2027 results, filed with the SEC on August 26, reported revenue of $96.2 billion, up 106% from a year earlier, with data center revenue of $89.0 billion, up 117%. Founder and CEO Jensen Huang framed the quarter around consumption rather than construction: “AI has reached its inflection point. It’s doing useful work. Its tokens are productive and profitable. Now, compute is revenue.” Inference — running models in production — is where that revenue is generated, and unlike training it has to happen near users and near data.

    What matters for buyers and investors is that the strategic logic is clearer than the commercial substance. A partnership among three named companies is real; a contracted, revenue-generating utility is not the same thing, and neither Equinix nor its partners has yet put a number, a date or a customer against it.

    Why Metro Colocation Is the Natural Home for Inference

    Training and inference want different real estate. Training a frontier model is a batch job: it can run anywhere power is cheap and abundant, which is why the largest commitments in the market point at greenfield megasites. NVIDIA’s own CFO commentary describes credit support for roughly 4.25 gigawatts at SB Energy’s PORTS-Pike campus in Ohio — a gigawatt being roughly the output of a large power station — under twenty-year leases to OpenAI. Nobody puts that in a city center.

    Inference is the opposite. It is interactive, it runs continuously, and its latency budget is measured in the tens of milliseconds a user will tolerate before an assistant feels sluggish. It also carries regulatory weight, because the prompts and documents flowing into a model are frequently the customer data a bank or hospital is not permitted to move across a border. Equinix’s asset is precisely metro adjacency plus interconnection density: its facilities are where carriers, clouds and enterprises already meet, so an inference endpoint sited there is a private hop away from a customer’s own network rather than a trip across the public internet.

    The economics of the shift are less settled than the logic. Colocation is a rent business — long leases, predictable yields, capital spent on shells, power and cooling that depreciate over decades. Accelerated computing is not: graphics processing units are expensive, they depreciate fast, and their returns depend on high utilization. Whether Inference Exchange is a capital-intensity story for Equinix or a channel arrangement depends entirely on who buys and owns the hardware, and that has not been stated.

    What NVIDIA’s Record Quarter Says About the Timing

    NVIDIA’s August 26 results give the backdrop unusual clarity. Data center revenue of $89.0 billion grew 117% year over year, and the company guided to $108.0 billion in total revenue for the current quarter while assuming no data center compute revenue from China. The quarter’s own highlights lean hard toward inference: the company said its Groq 3 LPX interactive inference accelerator has entered full production, and disclosed that NVIDIA GPUs with confidential computing are used for confidential inference in Apple’s Private Cloud Compute — a design in which the operator cannot see the data being processed.

    Read together, those signals describe a distribution problem more than a silicon problem. Frontier labs already buy directly and at scale. The next tranche of demand is thousands of enterprises that will never operate a GPU cluster and want inference delivered as a service, in a location they can name, with a contract their compliance team recognizes. Equinix supplies the geography and the enterprise relationships; Together AI supplies the serving layer; NVIDIA supplies the silicon and, critically, keeps those workloads inside its software ecosystem rather than migrating to a rival accelerator.

    The quarter closed on July 26, before this launch, so its silence on Equinix is chronological rather than meaningful. It does, however, set the scale against which the partnership should be read: an arrangement neither party has sized is unlikely to be financially material to NVIDIA in the near term. Its value to NVIDIA is positional. Its value to Equinix, whose revenue base is far smaller than NVIDIA’s data center line, could be proportionally much larger — if it converts.

    Who Gains, Who Gets Squeezed

    Together AI is arguably the clearest beneficiary. An independent AI cloud can win developers on price and model breadth but struggles to reach regulated enterprises that require a named facility, an auditable network path and a counterparty with a balance sheet. Distribution through Equinix’s footprint addresses all three without Together AI building data centers.

    The pressure lands on operators selling undifferentiated GPU hours. If enterprise inference migrates toward venues chosen for latency, data residency and network adjacency, then raw capacity in a remote low-cost region competes on price alone — a difficult position when supply is expanding. Content delivery and edge networking vendors, which have marketed proximity as their advantage, now face a colocation incumbent making the same argument with more power per site. Hyperscale clouds are less exposed: their hold on enterprise inference rests on data gravity, identity systems and developer tooling, none of which a colocation platform replicates. But they are also Equinix tenants, which makes this a partner-adjacent product and raises a channel question Equinix has not addressed publicly.

    Wholesale developers building megawatt campuses are not the losers here. They serve a different workload. The genuine contest is over which layer captures enterprise inference margin — the landlord, the model-serving vendor, or the cloud — and this launch is Equinix’s claim that the landlord can move up rather than be disintermediated.

    What the Launch Substantiates, and What It Doesn’t

    Substantiated: three named companies, a named product, and a second connectivity service announced alongside it. Equinix has the metro sites, the interconnection fabric and the enterprise account base to make the concept credible, and NVIDIA’s disclosed financials confirm that inference demand is real and growing rather than aspirational.

    Not substantiated: everything a buyer would need to price the thing. No capacity figure, no availability date, no commercial model, no customer, no statement of who funds the hardware. That is a common shape for infrastructure platform announcements, where logos typically precede contracted revenue by several quarters, and it is a fair criticism of the disclosure rather than of the strategy.

    One further piece of context deserves attention without overreach. NVIDIA’s filings show an ecosystem in which vendor financial support has become structural: supply commitments rose from $119 billion in the prior quarter to $279 billion; the company disclosed partnerships with Apollo, BlackRock, Blackstone, Brookfield, Goldman Sachs and KKR intended to mobilize over $500 billion of third-party capital over time, subject to definitive agreements; and it reported a maximum gross exposure of $3.5 billion under guarantees for AI cloud partners’ lease obligations, plus the separately capped $105 billion Ohio guarantee. None of that is stated to apply to this partnership. But investors evaluating any NVIDIA-adjacent infrastructure announcement in 2026 are entitled to ask whether hardware is being sold, financed, consigned or revenue-shared — and here, no one has said.

    Background

    Equinix, founded in 1998 and structured as a real estate investment trust since 2015, built its business on carrier-neutral colocation: buildings where any network can connect to any other without favoring an owner’s own transit. That neutrality made its facilities natural meeting points for carriers, cloud providers and enterprises, and interconnection — the cross-connects and virtual links between tenants — became the high-margin core of the model rather than the floor space itself.

    The AI buildout initially looked like a threat to that positioning, because training clusters favor vast, remote, power-rich campuses of a kind Equinix does not principally build. The counter-argument, which this launch embodies, is that once models move into production the workload shifts toward inference, and inference rewards exactly what Equinix already owns: presence in dozens of business metros, dense network adjacency and existing enterprise contracts. NVIDIA’s most recent quarterly disclosures show both halves of that market running at once — gigawatt-scale campus commitments for training on one side, and production-grade inference accelerators and confidential inference deployments on the other.

    Sources

    Source: Equinix launches Inference Exchange in partnership with NVIDIA and Together AI (W.Media) — report of the platform launch by Equinix with NVIDIA and Together AI. Additional coverage: Equinix announces connectivity service and distributed AI inference platform (Telecompaper), Nvidia Taps Equinix, Together AI for AI Inference Push (techbuzz.ai) and How Equinix has found a niche in the multitrillion-dollar AI data center boom (CNBC).

    Primary sources: NVIDIA Form 8-K filed August 26, 2026, reporting results for the quarter ended July 26, 2026; NVIDIA press release, Exhibit 99.1 to the August 26, 2026 Form 8-K, with second-quarter fiscal 2027 revenue, data center results, outlook and product highlights; CFO Commentary on Second Quarter Fiscal 2027 Results, Exhibit 99.2 to the same Form 8-K, detailing supply commitments, data center leases and guarantees; and NVIDIA Form 10-Q for the quarter ended July 26, 2026, filed August 26, 2026.

  • IBM’s $240M Together AI Deal Treats AI Inference Like Reserved Capacity

    IBM’s $240M Together AI Deal Treats AI Inference Like Reserved Capacity

    TL;DR · 30-second read

    The Short Version

    IBM, the century-old computing company, has signed a $240 million, multi-year deal with Together AI, a younger firm that runs freely available artificial intelligence programs for other businesses.

    IBM’s cloud, meaning its rentable computers in data centers, will host a large set of powerful Nvidia chips set aside for one job: answering people’s questions to those programs, the everyday work of a chatbot.

    The surprising part is how it is bought. Instead of paying per use, the computing power is booked years ahead, like leasing a whole building instead of renting hotel rooms by the night.

    IBM and Together AI have signed a $240 million multi-year agreement for an Nvidia-powered AI inference cluster that will run on IBM Cloud, Reuters reported on August 11, 2026. Inference is the stage where a trained AI model answers requests, as opposed to training, where the model is built.

    Together AI operates a cloud platform that serves open-weight AI models, meaning models whose parameters are publicly released, to developers and enterprises. Under the deal, a dedicated cluster of Nvidia accelerators on IBM’s infrastructure is committed to that inference work for several years.

    Executive Summary

    The headline number is $240 million, but the structure matters more. This is not a customer swiping a card for GPU hours. It is a multi-year commitment to a specific, dedicated cluster, which is how data center space and power have long been contracted, and increasingly how AI compute is contracted too.

    For IBM, the deal places IBM Cloud in the business of hosting third-party AI inference at scale, a market where the large hyperscalers and specialist GPU clouds have been most visible. For Together AI, it secures a block of capacity for serving open models at a time when access to Nvidia hardware still depends heavily on who has committed to it in advance.

    The companies have not disclosed the hardware generation, the size of the cluster, where it sits, or how the $240 million is paid over time. Those details determine whether this is a modest capacity reservation or a meaningful new source of inference supply.

    Inference Is Being Booked Like a Lease

    Most people meet AI inference as a metered service: a developer sends a prompt, a provider returns an answer, and the bill is calculated per token, a token being a small chunk of text. That pricing looks elastic, but the hardware underneath it is not. Nvidia accelerator clusters are expensive to buy, take time to deliver and install, and lose value as each new chip generation arrives. Someone has to commit to them before the first token is served.

    A $240 million, multi-year contract for a dedicated cluster is that commitment made explicit. It shifts inference from the on-demand model of cloud computing toward the reserved-capacity model long familiar from colocation and wholesale data center leasing, where a tenant contracts a defined block of infrastructure for a defined term. The provider gets revenue visibility that justifies deploying hardware; the tenant gets guaranteed supply it can resell to its own customers with predictable performance.

    This is one deal, and it does not prove an industry-wide shift on its own. But it shows clearly that open-model inference, often described as a commodity service racing to the lowest per-token price, is being underwritten by the same kind of long-dated capacity contracts as training clusters. The per-token price a developer sees sits on top of a fixed, multi-year obligation somewhere in the stack.

    What IBM Gains by Hosting Someone Else’s Models

    IBM has spent recent years positioning around open and hybrid AI: its Granite models are openly released, and its Red Hat business is built on open-source software. Hosting a platform whose entire proposition is serving open-weight models fits that posture more naturally than competing head-on with the largest clouds for frontier-lab training contracts.

    It also gives IBM Cloud a substantial, recurring AI workload with an anchor tenant. For a cloud provider, a committed customer reduces the risk of buying accelerators that might otherwise sit underused. For enterprise buyers who already run on IBM infrastructure, the arrangement could, in principle, place open-model inference closer to their existing systems, though neither company has described how enterprise customers would access the cluster.

    The Risk Sits in the Utilisation Curve

    Reserved capacity moves risk; it does not remove it. Inference demand can shift quickly as new models arrive, and per-token prices for open models have been falling as providers compete. A multi-year commitment to a fixed pool of hardware exposes whichever party carries it to two pressures: whether the cluster stays busy, and whether the hardware stays competitive as Nvidia releases newer, more efficient generations.

    There is also a physical side. Dense Nvidia clusters draw significant power and increasingly require liquid cooling, which constrains which data halls can host them and how quickly. The value of a contract like this depends on the cluster actually being energised and in service on schedule, and none of those milestones have been published.

    Background

    IBM has operated a public cloud for more than a decade and in recent years has concentrated its AI strategy on enterprise and hybrid deployments, including its watsonx platform, its openly released Granite models and the open-source software business of Red Hat. IBM Cloud offers Nvidia GPU infrastructure alongside its traditional enterprise services.

    Together AI emerged during the generative AI boom as a platform for running open-weight models, giving developers an alternative to closed models from the largest AI labs. Like other inference providers, it depends on securing large volumes of Nvidia accelerators, whose supply has been shaped by long-term commitments from cloud providers and AI companies.

    Sources

    Source: IBM, Together AI ink $240 million deal for Nvidia-powered AI inference cluster (Reuters): report on a multi-year agreement for a dedicated Nvidia inference cluster on IBM Cloud.