Faced with growing opposition to datacenters from residents who don't want the bit barns in their backyards, Amazon plans to invest more than $1 billion over the next five years to indoctrinate the public. The company announced the investment in a whataboutism-packed blog post aimed at convincing readers datacenters are not as bad as they've been led to believe, and that if the common folk don't get in line and support their development, the nation could be left in the dust. The program aims to invest in education, job training, local infrastructure improvements, and sustainability initiatives in communities home to Amazon datacenters. "This build out is so important geopolitically that there are widespread reports of various countries intentionally seeding misinformation in the US about datacenters to trick us into slowing down," the e-tailer turned cloud titan wrote. "Right now there are over 100 datacenter moratoriums being considered across the country. If these measures are enacted, the US could be writing its own losing ticket to this race, and the consequences would last generations." If Jeff Bezos's cash cow is to be believed, datacenters will transform the US economy much like the buildout of the highway system did in the '50s. But that can't happen, Amazon argues, if Americans block the development of these bit barns over misguided fears. The blog post highlights four such datacenter fears, which Amazon claims are "myths," despite plenty of reporting to suggest otherwise. Who needs water? The first "myth" is the fact that datacenters consume an excessive share of water. Bit barns commonly employ evaporative coolers which use water to reject heat generated by the facility into the atmosphere. These cooling towers use less energy than dry coolers or refrigerant-based systems but remove water from local watersheds as a consequence. According to Amazon, citizens should really be more worried about the water consumed by golf courses, which it claims use 200x more water than all of Amazon's datacenters. That argument might be more convincing if people didn't like golf courses. But they do and the whole lesser of two evils argument works a lot better when one of those evils isn't extremely popular. Amazon also attempted to dispel claims that a typical datacenter consumes five million gallons a day, arguing that its facilities are much more efficient using just 13,000 gallons a day, the equivalent of 42 US households. But this leaves out an important detail. It is possible to build datacenters that don't consume large quantities of water, but these facilities often consume large sums of power as a consequence. Most gas, nuclear, coal, and anything else that uses steam to turn a turbine also consumes water. In other words, nearly every large-scale power-generation method uses water either directly or indirectly. And despite Amazon's attempts to downplay the impact of datacenters, there's good reason for those living nearby to be worried. Earlier this year, it was reported that a datacenter, which we'll note was not operated by Amazon, has quietly sucked up nearly 30 million gallons of water and the only reason anyone discovered the issue was that local residents started complaining of unusually low water pressure. We totally won't let datacenters drive up your power bill or poison your air Amazon also attempted to dispel fears that datacenters could leave neighboring residents picking up the tab for their massive power consumption. The company argues that rates are going up because the US grid is old, and that responsible bit barn builders are working with utilities to ensure they're paying their fair share for the additional capacity and infrastructure upgrades required to support them. What wasn't addressed is why folks might be worried about rising rates. One of the reasons that utilities and plant operators are so conservative is that they've been burned by speculative markets before. If new power plants are built to meet demand that never materializes, ratepayers could get stuck with the bill. Amazon and others agreeing to pay higher rates might help, but only if they're consuming an appreciable amount of the capacity they convinced utilities to build. An empty datacenter doesn't pull a lot of juice. Speaking of power, Amazon also attempted to downplay the impact of datacenters on air quality due to their reliance on diesel backup generators. "The truth is the datacenter generators almost never run," the company wrote. Amazon also argued that these generators are subject to stringent permitting requirements which dictate how long they can run for. That might be more reassuring if datacenters in power constrained regions weren't getting permission after the fact to exceed those limits. And, as we saw with Elon Musk's Colossus supercomputer, temporary gas turbines can be deployed when the local grid can't support the load. That hasn't sat well with residents of Boxtown, Tennessee, while the NAACP is suing xAI over allegedly unpermitted turbines in Mississippi. Amazon can argue it's got a better track record than most, but that doesn't mean the community's concerns are misplaced. But think of the taxes Finally, Amazon argues that folks don't realize how big of an economic impact datacenters have on their community. "Datacenter operators are often the single largest taxpayer in a community, with upfront payments routinely exceeding entire annual municipal budgets in the first year of construction," the company claimed. "For example, in St. Joseph County, Indiana, the taxes Amazon will pay to the county are estimated to exceed $3 billion." The subtext is that, by enacting datacenter moratoriums, communities are walking away from billions in tax dollars. Then again, if the tax revenue from these facilities is so great, it raises the question why Amazon needs to spend more than $1 billion over five years sweetening the deal for communities hosting its bit barns. ® Updated Oct 3 at 1447 GMT to reflect the fact that Amazon actually said it would spend more than $1 billion.

In an effort to provide a more affordable AI system, Nvidia is introducing a cut down version of the DGX Spark with half the memory and storage. The GB10-based systems will be offered exclusively through hardware partners including Acer, Asus, Dell, Gigabyte, HP, and MSI, and are expected to retail for around $4,999. The less-powerful SKUs are necessary after Nvidia jacked the price of its 128 GB DGX Spark on Friday to $6,950 — an increase of nearly 75 percent from this time last year. As you might have already guessed, skyrocketing memory prices are to blame for the massive price adjustment. That makes the 64 GB model considerably less than Nvidia’s top-spec DGX Spark and GB10 systems given the ongoing memory shortage, but that’s still 25 percent more than the 128 GB version retailed for at launch. Nvidia’s GB10 platform has been plagued by pricing creep since the Projects Digits concept was unveiled at CES last year. Originally, the appliance was expected to retail for around $3,000, not the $4,000 it eventually ended up selling for. Even at that price, the DGX Spark offered something that just a few years ago would have set you back tens of thousands of dollars: large quantities of GPU memory. In fact, at the time it launched, the system was technically the highest capacity workstation GPU Nvidia sold. That’s not really the case with the 64 GB model, which due to its smaller capacity, isn’t as well suited to certain AI workloads like fine tuning. But if your main concern is local AI inference for powering private agents, Nvidia argues models in the 26-35 billion parameter range, Qwen 3.8 27B for example, are now good enough to get the job done. While the new systems may offer less memory, we’re told the memory bandwidth remains unchanged at 273 GB/s, which tells us they’re using lower capacity LPDDR5x memory modules rather than fewer of them. Nvidia also says that beyond the new memory config, the systems are otherwise unchanged and will feature the same 20-core Arm processor from MediaTek. The systems also retain their onboard ConnectX-7 networking, which enables clustering of up to 4x GB10-based devices at 200 Gbps a piece. To further facilitate this, Nvidia says later this month it’ll be rolling out new software tools to DGX OS to facilitate cluster configurations and inference deployments. Previously, this required some tinkering in the CLI to get set up along with some tweaks to the vLLM or TensorRT-LLM launch commands. While the 64 GB version’s lower MSRP should make the systems more accessible amid the ongoing memory crunch, it doesn’t bode well for Nvidia’s RTX Spark notebooks and mini PCs expected to make their debut this fall. These systems are based on the same GB10 silicon that powers the DGX Spark and other DGX OS-based systems, but are intended for more general purpose computing, like AAA gaming, web browsing, and content creation, not just AI inference and training workloads. Announced at Computex late this spring, RTX Spark-based systems were already expected to be quite expensive, particularly the 128 GB variants. With DGX Spark pricing nearing $7,000, similarly equipped RTX Spark notebook offerings based on the chip could end up selling for significantly more. But, as we understand it, Nvidia will be offering versions of the chip with much lower memory capacities for price conscious PC buyers. However, across the DGX and RTX Spark lines, Nvidia will face some competition from AMD’s newly launched Gorgon Halo SoCs, which offer memory capacities ranging from around 32 GB at the low end to 192 GB for the top-spec Ryzen AI Max+ 495 SKUs. What’s more, systems powered by that chip are currently retailing for less than Nvidia’s 128 GB DGX Spark. But while AMD wins on max capacity, it should be noted that our testing has repeatedly shown that the GB10’s GPU offers substantially higher performance for AI tasks. ®

Google’s first “Suncatcher” datacenter satellite has made it into space, but the company has cast doubt on whether its orbital ambitions can scale. As The Register reported last week, Project Suncatcher aims to test whether Google’s tensor processing units (TPUs) can survive a launch and the harsh environment of space. The Big G hopes to validate theories that the limitless supply of solar energy available in space makes the cost of putting servers into orbit sensible. In its announcement of a successful Suncatcher launch, Google linked to its own peer-reviewed research about the endeavor – which outlines many thorny challenges that must be met to make orbital datacenters fly. The paper, titled “Toward a future space-based, highly scalable AI infrastructure system design,” notes that SpaceX and others have suggested that orbital datacenters become viable when launch costs fall to $200/kg. Google’s researchers also note that SpaceX has managed to reduce the cost to launch a kilogram into orbit by 20 percent after every doubling of the cumulative mass it launched. If SpaceX can keep that up, which will mean launching 370,000 t additional cumulative mass, the equivalent of around 1,800 successful launches of its giant Starship rocket, and re-using components 100 times, Google thinks $200/kg is “plausible under reasonable assumptions.” Google doesn’t know the mass of its future datacenter satellites, but points to Starlink’s second-gen sats weighing 575kg and then compares the cost of sending a satellite into space with the price to power servers on Earth. “If launch costs reach ≲$200/kg, annualized cost per unit of power in space could be approximately comparable to terrestrial spend,” is Google’s conclusion. The paper’s calculations on future launch costs use data from SpaceX and other rocket operators. The other technologies needed to make datacenters in space remain largely theoretical. For example, the paper notes that existing network technologies probably aren’t suitable to link multiple satellites into functioning clusters. The need for rapid comms between satellites means Google’s plans require a design “significantly larger and entail much closer formation flight … than any previous or current satellite constellations.” Google also thinks that satellite design will need to evolve. “Our system design work to this point assumes a relatively conventional, discrete compute payload, satellite bus, thermal radiator, and solar panel design,” the paper states. “However, as has been seen in other industries (such as smartphones), massively scaled production motivates highly integrated designs (such as the system on chip, or SoC). Eventually, scaled space-based computing would similarly involve an integrated compute, radiator, and power design based on next-generation architectures, such as computational substrates based on neural cellular automata.” Google’s researchers also found “robust optical satellite-ground communications will also be critical for scaled operation but will necessitate overcoming challenges including atmospheric turbulence, high-speed relative motion errors, and precision beam tracking.” NASA’s TeraByte Infrared Delivery (TBIRD) mission, which has demonstrated 200 Gbps ground-low-earth-orbit comms, is cited as a promising approach. Google’s researchers therefore concluded that realizing its space datacenter ambitions “will require sustained research, iterative refinement of our design, and the achievement of several critical future milestones.” ®

Memory-maker Micron has warned that RAM shortages will persist into 2028, and perhaps beyond. Speaking on the company’s FY 2026 earnings call, CEO and company chair Sanjay Mehrotra reminded investors that Micron has already sold most of the memory it will make next year and said customers will pay “much higher prices” than they shelled out this year. “In calendar 2027 as well as 2028, we see demand exceeding supply,” he added. “In fact, we see greater tightness in the industry in 2027 and in 2028 versus 2026. Overall, supply-demand environment is only getting tighter.” “We do not have line of sight to when supply and demand will return to balance.” Micron plans to bring new factories online in 2028 – helped by planned capex of $25 billion in the first half of its new financial year – but execs warned those facilities won’t immediately help to improve availability or ease prices. The CEO said demand for the high-bandwidth memory (HBM) needed in AI hardware is growing faster than it is for the DRAM used in servers. Micron is also finding ways to expand its margins for HBM, which is currently not as profitable as DRAM. Both types of memory are, however, enormously profitable. Micron’s earnings announcement reveals that the gross margin for its cloud memory business unit, which focuses on HBM, reported a Q4 gross margin of 83 percent – a big jump from the 59 percent in last year’s Q4. The core datacenter business unit, which sells more DRAM, recorded Q4 gross margin of 90 percent, up from 41 percent last year. Sales of solid-state disks for use in datacenters reached almost $10 billion for the quarter, up more than 1,000 percent year over year. Q4 revenue came in at $54.25 billion, up from $11.3 billion last year. Annual revenue landed at $133.2 billion, vastly higher than last year’s $37.4 billion. Net income rose 895 percent to $85 billion. Mehrotra told investors Micron has “a strong roadmap for future HBM products and are proud to be working with NVIDIA on the industry’s first custom HBM4E implementation, NV-HBM, to be adopted on next generation of GPUs and NVLink fusion platforms. In NAND, AI context memory storage used for KV cache offload and HDD displacement opportunities are expanding the addressable market for SSDs.” Micron forecast Q1 FY27 revenue of $61.5 billion plus or minus $1.5 billion, and gross margin of 86.25 percent. The company’s share price spiked, then returned to its close-of-day price in after-hours trading, suggesting investors expected its colossal growth and profit. ®