This week on Tom’s Hardware Premium: September 12, 2026 — Benchmarking Qwen 3.8, the splintered compute economy and AI breakthroughs

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To kick the week off, our resident GPU guru and all-round inference extraordinaire Jeff Kampman undertook the laborious task of running Qwen 3.8 27B across several devices you might actually be able to afford. The model promises near-enough Frontier levels of performance, without the downsides that come alongside paying for an API-based subscription or tokenized billing.

What Jeff finds out after a dizzyingly large number of benchmarks across devices like an RTX 5090, Mac Mini, DGX Spark, and Strix Halo systems is that configuration matters just as much as the final, raw, tokens-per-second outputs that many clamor over on social media. Benchmarking AI models is still a fairly nascent subject, and the level of detail on offer here isn’t something you’ll find published anywhere else.

• Benchmarking Qwen 3.8 27B on RTX 5090 and beyond — VRAM capacity alone can’t overcome severe software and inference engine bottlenecks

Andrew Freedman headed out to IFA last week, where we saw a slew of new announcements and devices from companies. But there’s only one problem. They’re all either super-light MacBook Neo competitors or Agentic AI PCs that may well cost more than a car. The original “golden” price point for many enthusiasts, around $1,000, looks a little lonely as a result.

The factors impacting the mid-range of regular old desktops and laptops have been adversely affected by the sheer scale of demand coming from the ongoing AI data center buildout. Meaning that if you’re after a system with just enough RAM and storage to get by, prices can skyrocket fast. Andrew explores everything he saw at the show and ruminates on the current state of the systems market, which, for now, appears to be fractured.

• At IFA 2026, computing chased the high and low ends — AI and budget-focused machines left little for the rest of us

Elsewhere in the industry, Ajinomoto, a company usually associated with food products based in Japan, is involved in making some of the most crucial materials in modern AI accelerators. We’ve taken a look at how the company’s ABF substrates sit within the supply chain of the biggest chipmakers like Nvidia, Intel and AMD. But, as with many components in the AI boom, it’s strained by more demand than anyone anticipated, which has led to an increase in prices of approximately 30%. We dive deep into the current state of the use of ABF substrates in the semiconductor industry and take a look at the supply chains underpinning it.

• The state of ABF substrates in data center silicon in 2026 — solving the supply crunch and material wall beneath every AI accelerator

Also in the news, TSMC, Intel, and Samsung have all thrown their support behind ASML. The industry is collectively aiming to move towards a shift in High-NA EUV, particularly in the usage of 6×12-inch photomasks. With current standards set at 6×6-inch photomasks, a larger mask would eliminate the need for stitching, which weaves together multiple High-NA exposures, at the cost of efficiency.

We analyze how the move to larger photomasks might shake out, and the trials and tribulations that might face chipmakers during the shift, which, theoretically, will take years.

• TSMC, Samsung, and Intel shore up support with ASML to deploy larger High-NA EUV photomasks — 6×12-inch photomask transition may take years despite unified effort

Lastly, the news has been dominated this week by the release of OpenAI’s latest frontier-level model. Named GPT-6 Astra, the AI model quickly topped charts for its intelligence and the ability to complete tasks on a per-task basis lower than other leading AI models. This comes hot on the heels of a fleet of rogue OpenAI agents running amok on internet forums, all in the name of co-ordination.

Astra’s release prompted OpenAI to release a blog post, explaining how it believed that Astra was aligned internally. But others believe that the threat of a looming humanity-threatening tech “singularity”, powered by powerful models like Astra, is on the way soon.

OpenAI has also claimed to have solved one of the Millennium Problems. The Navier-Stokes problem had been left long unsolved, while mathematician Tristan Buckmaster had been working on a step toward a solution for the problem, alongside a member of Anthropic staff. The pair had been using both Anthropic and OpenAI models in their research, so when the claim came out that an OpenAI model had solved the long-standing problem, it raised more than a few eyebrows. We chronicle the entire saga and the implications for researchers in our exhaustive breakdown.

• OpenAI’s breakthrough solution for the elusive Navier-Stokes problem overshadowed by plagiarism controversy — researcher says OpenAI scraped Codex session and issued career threats
• OpenAI claims GPT-6 Astra is an ethereal ‘Alien Mind’ with AGI-like qualities — company warns of alignment challenges as new frontier leader emerges

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