By ckasprzak | TkOut | March 27, 2026 |
Hardware
The Hot Take: That's amazing! We need more manufacturers other than TSMC. I still hope they're looking to make a factory States side, so we don't have to rely on the one in S Korea.
Samsung Electronics has reportedly raised the yield of its 2nm wafer foundry process above 60%, a significant jump from around 20% in the second half of 2025. Industry analysts say this improvement not only cuts manufacturing costs but also boosts Samsung's chances of securing new orders.
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The Hot Take: Looks like we're going back to tech silos globally. We still have to address the unfair competition we have domestically more I think anyway.
Russia-based Tramplin Electronics obtains samples of Loongson's LS3C6000 processors with Cyrillic inscriptions, claims these are its own CPUs.
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The Hot Take: ARM wants a piece of that Ai cash pie for sure. I'm wondering how their licensing partners are going to take this.
The chip design firm says Meta, OpenAI, Cerebras, and Cloudflare are among the first customers of its new artificial intelligence hardware.
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The Hot Take: Uh oh, Ai king looks to be in trouble.
U.S. senators Elizabeth Warren (D-Mass.) and Jim Banks (R-Ind.) told Commerce Secretary Howard Lutnick that he should suspend all active export licenses to China for Nvidia AI chips, saying that Nvidia's most advanced AI GPUs are being diverted into the country despite Jensen Huang's assurances.
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The Hot Take: US domestic chip manufacturing appears to be exploding. That's an insane goal, but to bad it's just for his companies.
"Billionaire Elon Musk has announced plans to build a $20 billion chip plant in Austin, Texas" reports a local news station:
Musk announced on Saturday night during a livestream on his social media platform X that the plant, called "Terafab," will be built near Tesla's campus and gigafactory in eastern Travis County. The long-anticipated project is a joint venture between Musk-owned properties Tesla, SpaceX and xAI... The Terafab plant is expected to begin production in 2027.
Musk "has said the semiconductor industry is moving too slow to keep up with the supply of chips he expects to need," writes Bloomberg ā quoting Musk as saying "We either build the Terafab or we don't have the chips, and we need the chips, so we build the Terafab."
Musk detailed some specific plans, including producing chips that can support 100 to 200 gigawatts a year of computing power on Earth, and chips that can support a terawatt in space, but gave no timelines for the facility or its output... The facility is expected to make two types of chips, one of which will be optimized for edge and inference, primarily for his vehicle, robotaxi and Optimus humanoid robots. The other will be a high-power chip, designed for space that could be used by SpaceX and xAI... Musk said he expects xAI to use the vast majority of the chips.
During the presentation, Musk also unveiled a speculative rendering of a future "mini" AI data center satellite, one piece of a much larger satellite system that he wants SpaceX to build to do complex computing in space. In January, SpaceX requested a license from the Federal Communications Commission to launch one million data center satellites into orbit around Earth. Musk said that the mini satellite he revealed would have the capacity for 100 kilowatts of power. "We expect future satellites to probably go to the megawatt range," Musk said.
Raising money to build and launch AI data centers in space is one of the driving forces behind SpaceX's planned IPO later this year. SpaceX is expected to raise as much as $50 billion in a record-setting IPO this summer which could value it at more than $1.75 trillion, Bloomberg News reported earlier.
Read more of this story at Slashdot.
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The Hot Take: Green New agenda doesn't fit in with Ai replacement of the plebes for sure. So they push us to Solar & Wind while they get viable power options for a bot?
Microsoft and Nvidia are joining forces to accelerate the construction of nuclear power plants for power-hungry AI data centers. The partnership combines generative AI, digital twin simulation, and Nvidia's Omniverse platform to streamline the nuclear lifecycle from permitting through operations.
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The Hot Take: Nvidia starting to feel the heat of competition and see those $ evaporate as they try other vendors.
Nvidia is preparing to launch a new chip designed to speed up AI responses, breaking with its long-running habit of flogging the same processor for every job.
Nvidia chief executive Jensen Huang is expected to unveil a chip focused on āinferenceā, meaning running models rather than training them.
According to people familiar with the plans for GTC next week, the chip is the first new product to emerge from Decemberās $20bn deal to hire the founders of Groq, a start-up building ālanguage processing unitsā tuned for high-speed answers to complex AI queries.
Three months after that deal, Nvidia is expected to debut a Groq-based LPU to sit alongside its forthcoming flagship Vera Rubin graphics processing unit. It is part of a product family meant to head off challengers and meet new kinds of AI applications.
The move lands as the worldās most valuable company gets grief from start-ups and customers, such as Google, all busy cooking up their own AI chips. This week, Meta announced a new family of four inference-focused processors.
One Silicon Valley venture investor said: āWe are entering an interesting phase that is not āNvidia dominantā,ā
For the past three years, Nvidiaās $4.5tn market capitalisation has been built on its GPUs, which have become the backbone of generative AI. They train models such as the ones behind OpenAIās ChatGPT.
Huang has insisted that a single system can handle training and then run the chatbots and coding tools built on top. Big Tech has spent hundreds of billions deploying these boxes while funding their own specialised silicon.
But the growing sophistication of AI tools, including āagenticā coding systems, is pushing Huang to ditch the mantra that one GPU fits every workload.
The Groq deal was worth about $20bn, according to people familiar with the transaction, making it one of the biggest deals in Nvidiaās 33-year history. It includes licensing and the hiring of key talent, including Groq founder and former Google chip executive Jonathan Ross.
Groq, which had been working with Samsung to manufacture its products, previously bragged that its LPUs were faster and more efficient than Nvidiaās GPUs for inference. Nvidia clearly listened.
Nvidiaās flagship Blackwell and Rubin systems lean on high-bandwidth memory to cope with the massive data loads that AI models fling around. But HBM is expensive and in increasingly short supply as SK Hynix and Micron struggle to keep up with demand.
The Groq-style chip will use SRam rather than the dynamic Ram used for HBM, according to people familiar with Nvidiaās plans, because SRam is more available and better suited to speeding up AI āreasoningā tasks.
Bank of America reckons that by 2030, inference will account for 75 per cent of AI data centre spending, up from about 50 per cent last year, and it expects a ābroadened AI portfolioā at GTC.
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The Hot Take: Intel doing what it's great at with it's CPUs, software optimizations.
With the introduction of the new Binary Optimization Tool (BOT), Intel is taking a significantly different approach to boosting the performance of modern processors than in the past. While traditional optimizations rely heavily on developers and are determined during the software compilation process, Intel is now focusing on a post-compilation optimization layer based directly on [ā¦]
Source
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The Hot Take: If this proves true ASML better watch out. Their monopoly might come crashing down if they don't get something that competes.
Lace Lithography raised $40 million in Series A funding on Monday to develop a chipmaking tool that uses a helium atom beam instead of light to pattern silicon wafers.
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By ckasprzak | TkOut | March 26, 2026 |
Hardware
The Hot Take: US domestic job market appears to be expanding in tech.
The Trump administration is targeting $4 trillion Pax Silica investment for semiconductors. Itās not currently clear how the Trump administration arrived at the $4 trillion figure, or how it will ultimately materialize.
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