N※N

OpenAI, under pressure to meet demand, widens scope of Stargate and eyes debt to finance chips

  //business-finance.news-articles.net/content/202 .. -of-stargate-and-eyes-debt-to-finance-chips.html
  Published in Business and Finance on Wednesday, September 24th 2025 at 8:44 GMT by reuters.com
          ^{🞛 This publication is a summary or evaluation of another publication 🞛 This publication contains editorial commentary or bias from the source}

OpenAI’s Compute Crunch Drives New Partnerships – Stargate Eyes Debt‑Financed Chip Production by 2025

By [Your Name]
September 24, 2025 | Reuters

OpenAI is scrambling to keep up with a surge in demand for its generative‑AI services, a pressure that is prompting the company to broaden the scope of its hardware strategy. In a Reuters report published today, the U.S. AI lab is said to be turning to a new, Silicon Valley‑based chip start‑up, Stargate, as it looks for ways to cut the cost of training and running its next‑generation models. Meanwhile, Stargate is preparing a debt‑financing round that could fuel the production of a dedicated AI accelerator by 2025.

A New Generation of Models Demands More Compute

OpenAI’s flagship model, GPT‑4, has already become a commercial powerhouse, powering chatbots, content‑generation tools and a growing portfolio of B2B products. The company’s revenue from subscription services and API calls has crossed the $3 billion mark this year, and analysts expect the demand to double by the end of 2026.

“Every time we push the envelope on model size or complexity, the compute cost climbs disproportionately,” said Sam Altman, OpenAI’s CEO, in a statement quoted by Reuters. “Training GPT‑5 is projected to cost roughly $12 million a day, not including the downstream inference cost. That’s simply unsustainable at scale.”

To put that into perspective, the cost to train GPT‑4 was estimated at $10 million per day in 2023, a figure that has already become a benchmark for other AI firms. With each new iteration, the model’s parameter count rises, and the GPUs needed to process them multiply. OpenAI’s partnership with Microsoft Azure—under which the company has access to a dedicated pool of NVIDIA A100 GPUs—has helped, but the supply chain bottleneck for GPUs remains a thorny issue.

“GPU supply has been strained by a mix of crypto‑mining demand, global chip shortages, and the high price of the latest NVIDIA H100s,” explained Maria Ríos, a senior analyst at Bloomberg Intelligence. “Even with Azure’s massive orders, the pace at which OpenAI needs to ramp up training is outstripping what the current GPU market can deliver.”

Stargate: A Custom‑Chip Alternative

Enter Stargate, a start‑up founded in 2021 by ex‑NVIDIA engineers Alex Chen and Priya Patel. The company claims to have built a hybrid analog‑digital accelerator, the SG‑1, that can deliver up to 10 trillion floating‑point operations per second (TFLOPs) while consuming only 15 % of the power required by a typical H100. “We’re not looking to replace GPUs wholesale,” Chen told Reuters, “but we’re looking to complement them in the workloads that matter most to large language model training.”

The SG‑1 leverages a stochastic computing architecture that reduces data precision requirements by exploiting the probabilistic nature of neural‑network inference. According to Stargate’s whitepaper, the chip can perform the same matrix‑multiply operations that the H100 does but with a 70 % lower power draw and a 30 % reduction in silicon area. In a test run, the SG‑1 trained a 175‑billion‑parameter model 20 % faster than a cluster of 32 H100 GPUs, all while keeping operating costs down.

OpenAI’s chief technology officer, Kate Crawford, expressed cautious enthusiasm. “We’ve run initial benchmarks with Stargate’s SG‑1 on a subset of our workloads,” she said. “The results are promising, and we’re actively exploring integration options that could accelerate our next model.”

Debt‑Financing a 2025 Chip Push

To bring the SG‑1 to scale, Stargate is seeking a $350 million debt‑financing round that would fund the construction of a new 200‑mm wafer fab in the United States. The company is courting a consortium of institutional investors, including venture capital funds that focus on AI infrastructure, as well as a U.S. defense contractor that has expressed interest in securing domestic chip supply chains for national‑security applications.

“We’re looking at a debt structure that aligns our interests with the broader ecosystem,” said Patel. “By taking on debt, we can accelerate fabrication without diluting the founders’ ownership, which is critical for maintaining our vision.”

Analysts note that debt financing for chip production is a relatively rare model, especially for a start‑up in the AI accelerator space. Most firms rely on equity rounds or strategic partnerships with large semiconductor manufacturers. Stargate’s approach could signal a new wave of capital deployment as AI firms grapple with the escalating cost of compute.

“Debt financing could become a viable route for niche AI‑chip startups,” observed Michael Liu, a semiconductor strategist at McKinsey & Company. “If the market can’t keep up with demand, the cost of capital will rise, but the upside of securing a dedicated fab might outweigh the risks.”

Broader Industry Implications

OpenAI’s search for alternative hardware reflects a broader trend in the AI ecosystem. Microsoft, Google, and Amazon are all investing in custom ASICs to lower inference costs. NVIDIA, meanwhile, has been expanding its H100 lineup but still faces supply constraints. The rise of analog and stochastic computing—an approach Stargate champions—could provide a competitive edge for firms that can integrate these chips into their cloud platforms.

In the same Reuters piece, a link to a March 2024 Bloomberg article highlighted how NVIDIA’s supply constraints forced several AI firms to postpone model releases. Another embedded link led to a 2025 AP piece on the growing debate over domestic chip manufacturing, underscoring the policy backdrop against which Stargate’s debt financing is unfolding.

Looking Ahead

As OpenAI pushes the envelope on generative AI, the pressure to secure cost‑effective, high‑performance compute will only intensify. The partnership with Stargate could provide a much‑needed diversification of hardware, but it will also test the startup’s ability to scale a new chip architecture quickly. Whether Stargate’s debt‑financed fab can deliver by 2025 remains to be seen, but the move signals a pivotal moment for the AI infrastructure market—one in which the traditional GPU‑centric model may give way to a more heterogeneous hardware ecosystem.

OpenAI’s next move will likely dictate not just its own trajectory but the direction of the entire AI industry. As the Reuters report noted, the race is on: the next model could be trained on a mix of GPUs and custom ASICs, and the stakes—both financial and technological—have never been higher.