SpaceX and Tesla on Thursday formally unveiled plans for the initial phase of their Terafab project. The first stage of the plan — which is expected to use Intel's 14A process technology — is expected to require $16.8 billion in investment, while the completed campus is planned to encompass more than 100 million square feet of manufacturing space. The massive semiconductor manufacturing complex will be built in Grimes County, Texas, at a location that belongs to SpaceX.

According to SpaceX and Tesla, their combined demand for semiconductors is projected to exceed 1 terawatt (TW) of compute per year, which significantly exceeds today's global supply. In fact, SpaceX, Tesla, and xAI already consume a significant portion of contract chipmaking services available today, and can potentially justify building a dedicated fab that will exclusively serve Elon Musk's companies. In particular, Terafab is envisioned to exclusively produce AI inference processors for Tesla Optimus humanoid robots and Cybercab autonomous vehicles, as well as 'high-power' processors intended for SpaceX's space-based data centers. Meanwhile, SpaceX and Tesla have not disclosed when their combined demand for compute per year will hit the 1 TW benchmark.

Unlike a conventional semiconductor fab, Terafab is envisioned as a vertically integrated manufacturing campus where advanced logic devices, memory chips, packaging, and testing operations are housed together. Normally, logic and memory are produced at different fabs using different process technologies, whereas packaging and testing services are performed at different facilities. However, the companies believe that consolidating logic, memory, packaging, and testing in one location will not only shorten the production cycle but will also shorten time-to-yield by enabling faster iterative improvements.

Given the description and the goal of the project, this will be a massive facility. Yet, SpaceX and Tesla have said little about its capabilities; The only thing they have disclosed is that the current facility in Grimes County will feature '100 million square feet of manufacturing space.' The 100 million square feet (9.3 million square meters) figure immediately stands out because it is far beyond anything ever announced for a semiconductor manufacturing facility. For example, the total area of Samsung's Pyeongtaek campus is approximately 2.89 million square meters, or 31.1 million square feet. A single Samsung fab occupies about 120,000 square meters, or 1.29 million square feet. However, the key wording is important: '100 million square feet of manufacturing space' does not mean 100 million square feet of cleanroom space.

Based on an image published by SpaceX, the Terafab facility will occupy four massive buildings. It is unclear whether these buildings will be four phases of the project (i.e., logic, memory, and packaging will be made under one roof) or will serve different purposes (i.e., one building makes logic, another produces memory, yet another does testing and packaging). In any case, when fully built, Terafab will be a massive semiconductor production campus that will require significantly more than $16.8 billion.

The announcement claims that the Terafab facility will employ at least 3,000 people, and that between 60% and 80% of them will be Grimes and nearby Brazos County residents.

Terafab is expected to use water from Gibbons Creek Reservoir instead of local groundwater and feature on-site wastewater treatment as well as water recycling and conservation measures.

The announcement follows Tesla's groundbreaking earlier this year on a research semiconductor facility at the North Campus of Tesla's Giga Texas campus, which the companies describe as a precursor to Terafab.

This week, Samsung presented its vision for next-generation memory and storage solutions both for AI and general-purpose applications at the Future of Memory and Storage (FMS) 2026 conference. The company introduced zHBM, zNAND-O, and BV-NAND technologies at the event, and while they are aimed at very different applications, they share one common ingredient: they all rely on wafer bonding.

Here's a breakdown of each type of technology that the company announced.

• zHBM: Samsung’s vision for custom HBM ultra-high-performance memory that will sit on top of logic dies.
• zNAND-O: NAND memory that can be bonded atop a logic device to maximize bandwidth, reduce latency, and minimize power consumption.
• BV-NAND (aka Samsung’s 10^th Generation V-NAND): Samsung’s take on bonding NAND arrays atop all the circuitry needed to operate them. This is essentially next-generation 3D NAND technology that is used today by almost everyone, including Kioxia/Sandisk, SK Hynix, and YMTC (which was the first company to adopt such a method).

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zHBM: Placing HBM stack on top of logic

Around the time we first heard about HBM4's 2,048-bit interface, we read a 'semi-official' report that SK hynix and its partners were considering vertical integration of HBM4 stacks on top of logic chips. To a large degree, companies were considering installing these memory stacks on top of their processors as they doubted that it would be possible and feasible to use interposers to connect HBM4 stacks to AI accelerators. Over time, it turned out that integration of memory on top of logic is complicated when it comes to cooling, power delivery, and developing interposers that can handle a 2,048-bit memory interface. Samsung's zHBM appears to be a continuation of that effort.

Samsung's zHBM concept places HBM directly on top of an AI accelerator instead of on its perimeter, which greatly reduces the distance data must travel between compute and memory. The company says this architecture can increase bandwidth and power efficiency and projects that it can provide 8x the 'performance of HBM5', while its advanced wafer bonding is expected to enable over 10x higher memory density, 3x better energy efficiency, and more than 50% lower thermal resistance than HBM5.

While Samsung's claims about the zHBM look incredibly impressive, they are vague. For example, when the company claims that a 'next-generation interface system incorporating zHBM' will deliver 'approximately eight times the performance of HBM5,' it never explicitly states whether this refers to memory bandwidth, actual application performance due to a combination of improvements over standard HBM, or something else. Had Samsung intended to compare raw bandwidth, it would likely have used more precise wording, such as '8x higher bandwidth.'

Instead, the general term 'performance' may have many meanings. The same applies to references of improved power efficiency, higher memory density, and lower thermal resistance compared to HBM5, a standard that has not been fully defined or ratified. To make matters even more imprecise, Samsung mentions thermal resistance, which depends on implementation rather than the standard. Finally, Samsung didn't reveal which die stacking/bonding technology it will use for zHBM.

In any case, Samsung says that zHBM will also support customer-specific designs and will enable custom IP to be integrated into the interconnect layer (which plays a role similar to the base die, though it is definitely not a base die per se) between the HBM stack and the AI processor, which essentially means that zHBM is not necessarily an industry-standard solution, so it might be implemented with additional perks.

For now, Samsung hasn't disclosed any timeframes for when it plans to offer its zHBM solutions. The only clue is that it compares it to HBM5, which is likely to see the light of day sometime in the late 2020s or early 2030s.

zNAND-O(on-device): Put storage close to compute

Samsung also unveiled zNAND-O, a high-performance NAND memory currently in development that will enable putting four or eight stacks of NAND devices on top of logic dies. zNAND-O(n) device is based on the company's V-NAND platform (or rather, BV-NAND) and is meant to combine high storage density with improved I/O performance and low latency.

Samsung claims that the tech is suitable for edge AI systems running real-time, data-intensive inference workloads, though, of course, there are many other applications that can benefit from high-performance on-package storage.

Since the technology is in development, Samsung hasn't shared much information about it, even keeping the schematics of how it plans to put zNAND-O layers on top of a logic under wraps. We can speculate that since Samsung mentions 'improved I/O performance and low-latency,' it may be planning to exploit both the inherently parallel architecture of NAND memory with a high-performance interface, though we will not try to dive into details for the sake of not making the discussion too speculative.

BV-NAND: Good old V-NAND gets a new treatment

With BV-NAND, Samsung is essentially introducing a new brand name for its next-generation 3D NAND that bonds the NAND array on top of the I/O and logic wafer. This, in turn, produces the layer itself using high-performance logic process technologies and enables higher I/O speeds.

Samsung formally announced its 400-layer-class V-NAND (aka 10^th Generation V-NAND) some time ago, and even disclosed that its areal density would be 28 Gb/mm² with a maximum I/O speed reaching 5600 MT/s. This likely stipulates the usage of a pinout that is different from the 3D NAND packages used today. While the areal density of Samsung's 10^th Gen TLC V-NAND is slightly lower when compared to that of Kioxia/Sandisk's BiCS10 TLC NAND, its maximum I/O speed is significantly higher, which perhaps justifies the new branding.

Considering the fact that Samsung rarely sells its memory to third parties and prefers to market client SSDs itself, a new brand for memory can be a sales driver, even if actual high-end drive performance is limited by a PCIe 5.0 x4 interface rather than by 3D NAND memory I/O. Then again, the performance of mid-range PCIe Gen5 SSDs that rely on a quad-channel NAND controller depends on 3D NAND I/O today, so marketing them under a new flash memory brand is a certain way to attract attention and then sell decent performance.

Samsung's new brand strategy

As the new name of the FMS tradeshow implies, the announcements touch upon very distinct industry needs. In the case of Samsung, we are talking about high-performance data center-grade AI accelerators; applications that need storage with latency that is lower than ~50 – 60 µs of modern high-end PCIe SSDs, though we will see how it compares to Z-NAND, and essentially a new generation of conventional 3D NAND with a new brand.

Except for the 400-layer-class Samsung BV-NAND that is set to hit the market in the foreseeable future, the company's new tech, like zHBM and zNAND-O, is years away.

Google was among the first hyperscalers to develop its own custom AI processors about a decade ago and has been steadily ramping their deployment since then. The company seems to be so confident about its TPU v9 due in 2028 that it intends to order 12 – 15 million of such processors, according to a Fubon Research note to clients published by Sean. If the information is correct, Google may not only produce more or a comparable number of AI accelerators than Nvidia, but may also need to use Intel Foundry to meet its goals.

"Based on our checks, Google plans to have 12 – 15 million TPUs in 2028," the paper reads. "Entering 2028, Google’s TPUs will enter the V9 generation with four compute dies, suggesting that their capacity consumption will more than double in 2028 versus 2027."

Fubon estimates that Nvidia supplied 8.2 million data center AI GPUs in 2026 and is on track to increase the number to 12.4 million in 2028. If Fubon is correct about Google's plans to produce 12 – 15 million 9th-generation TPUs in 2028, then the company may produce more, or at least a comparable number of AI accelerators, than Nvidia in 2028.

TSMC is not enough

How the performance of Google's v9 TPUs will stack against Nvidia's Rubin and Rubin Ultra is something that remains to be seen, but the fact that Google intends to use four compute chiplets on these AI accelerators clearly points to the fact that the company bets big on the performance of these processors. Meanwhile, building an AI accelerator with four large compute chiplets is a major engineering effort, which Google seems to have accomplished.

"Although we do not have the detailed allocation yet, we think it is difficult to reach Google’s target with TSMC alone, and Intel's supply is a must by 2028," the paper continues.

Researchers from Fubon are not sure whether Google's allocations at TSMC will be enough to meet the company's demand for 12 – 15 million 9^th Generation TPUs, so they think that Google will have to use Intel Foundry's capacity to meet its volume goals. Over the past few months, we have seen reports claiming that Intel had landed orders to make three million TPUs for Google following months of Google's testing of Intel's advanced packaging technologies. Indeed, if Google wants to make its silicon at Intel Foundry, usage of Intel's advanced packaging services makes great sense. It should be noted that when compute chiplets are developed, they must be developed with their packaging technology in mind, as Intel's EMIB/EMIB-T and TSMC's CoWoS-L are incompatible.

World's largest consumer of AI accelerators

If the information about Google's plans to produce 12 – 15 million TPUs in 2028 is correct (note that the difference between 12 and 15 is 20%, which is huge) and Google will indeed deploy more AI accelerators annually than Nvidia sells to the entire market, it would mark a dramatic shift in the AI hardware landscape. It will not only make Google the world's largest consumer of AI accelerators (as the company will unlikely cease buying Nvidia hardware), it will eventually make Google the owner of the world's most capable AI hardware fleet. Whether or not Google will use its overwhelming AI compute capacity primarily for its own services, or will lend the majority to other is something that remains to be seen.

Meanwhile, for Google's rivals, the milestone will underscore the growing importance of vertically integrated AI infrastructure, where cloud providers design chips tailored to their own software stacks, workloads, and data centers instead of purchasing off-the-shelf GPUs.

Yet, Google's surpassing Nvidia in unit shipments would not necessarily diminish Nvidia's dominant position. AI demand continues to expand so rapidly that both companies could increase deployments simultaneously, but Google will simply grow faster, at least till Nvidia ups production of its AI accelerators with Feynman and Feynman Ultra in 2029 – 2030. After all, Nvidia's AI GPUs are sold out. What Nvidia should worry about is not the volumes of TPUs that Google can deploy, but rather the fact that these processors do rely on a software stack that rivals Nvidia's CUDA, the company's main competitive advantage.