Why the first GPU financiers are turning to inference chips in a $400 million deal

General Compute, an AI inference cloud startup, has landed a $400 million loan from Upper90, a tech investment firm. According to TechCrunch, it might be the first deal to put up inference-specific chips as collateral. These chips are built to run already trained AI models quickly and efficiently, rather than the more expensive chips used to build the models in the first place.

A New Financing Paradigm for AI Infrastructure

The financing is the latest signal that markets are responding to concerns over the price of AI tools and tokens by turning to infrastructure that runs open source models more cheaply than the newest LLMs from frontier labs. Founded by CEO Finn Puklowski and CTO Jason Goodison, General Compute raised a $15 million seed round in May to build an inference neocloud around silicon from SambaNova, an Intel-backed chipmaker. Neoclouds are purpose-built for AI workloads, unlike the general-purpose infrastructure offered by traditional hyperscalers like AWS or Azure.

The company’s SN50 chips are designed for inference. They are power-efficient and do not require expensive water-cooling systems, which means they can be deployed more quickly than GPUs across a larger variety of data centers. General Compute says the new chips will provide 16 times faster inference than GPU-based clouds. The primary challenge remains getting a large volume of these chips, especially for a brand-new company entering the market.

Upper90 Playbook and the Evolution of Chip Financing

Upper90 co-founder and CEO Billy Libby, a former Goldman Sachs quantitative trader, had a playbook for this challenge. In 2021, his firm financed GPU purchases by Crusoe, the energy-focused data center startup, which he believes was the first loan against the value of advanced chips. Traditional lenders eschewed such deals at the time because of the risks and uncertainties around GPU depreciation. However, as CoreWeave made chips-backed loans into a business model and then the basis of a blockbuster IPO, this kind of financing has become common.

When Upper90 financed Nvidia GPUs as the first group to do that, the market was inefficient. Libby noted that they could really put together something as an early participant and get compensated for the risk. Now that GPUs are comparatively well understood and perhaps over-bought, Upper90 is turning to companies like General Compute to ride the next wave of the AI boom. Libby stated that open source models are going to be important and that they went and looked for a player last year that was in inference, noting that everyone does not need a supercomputer, but they do need inference and AI.

The Rise of Alternative Chipmakers and Open Source Models

That thesis has been growing stronger, with companies that provide access to open models, like OpenRouter and Fireworks, raising new rounds at huge valuations. New models like Kimi’s K3 have proven to compete with the latest releases from Anthropic and OpenAI on coding benchmarks. At the same time, new chipmakers like Groq and Cerebras have drawn interest from acquirers and public markets alike. General Compute’s ability to access chips outside of Nvidia’s ecosystem matters for the exact same reason.

TensorWave, another AI infrastructure company, is making a similar bet on a partnership with AMD. As more alternatives to Nvidia emerge, compute providers that are not locked into Nvidia deals may have an advantage in providing cost-efficient inference. Puklowski pointed out that there are a bunch of chips starting to scale that have amazing total cost of ownership or that can operate much faster than Nvidia, but there are not too many buyers for them.

Challenging Nvidia Dominance in the AI Market

Puklowski emphasized that the partnership with Upper90 represents more than just a cool startup getting money to buy some compute. Instead, he views it as the first signal of capital organizing itself and the fragmenting of Nvidia’s monopolistic dominance in the sector. As financing mechanisms evolve to support inference-specific hardware, the broader AI ecosystem continues to diversify beyond traditional GPU clusters. Companies are increasingly finding new ways to deploy cost-effective solutions for running open source models at scale.