Yesterday, @OrnnExchange's B200 and B300 indexes reached a new all-time high prices, with the B200 hitting $8.54/GPU-hour. It is clear that today's market is lacking GPU capacity, and that the companies building deploying capacity are looking to improve their credit. In the past trading week as the B200 rental price increased by 12.9%, $CRWV fell by 25.2%. Why does $CRWV move inversely to rising compute prices as a neocloud? 98% of Coreweave's Q2 revenue came from committed contracts, 72% from three customers, and most of its $104B backlog is recognized after mid-2028. CoreWeave's backlog is locked at fixed prices. These higher market rates matter for new contracts they sign, so the question is where rental rates end up when their contracts roll off. Ornn's index tracks transacted prices in order to capture what buyers are actually willing to pay for on-demand GPU rentals, real signal for tracking the value of compute.

When people talk about running AI inside a bank, the conversation usually starts with what model. In practice, the first real decision is how to secure the compute to run it - @Farsight_AI's partnership with @OrnnExchange now makes this simple. Ornn is the leader in financial markets for compute. With our partnership, banks can now source capacity through Ornn and run Farsight's model-agnostic solutions directly through it - all in their own environments on-premise. This brings the full stack to the firm, from the infrastructure to the work product that their teams rely on. More + the press release in the thread below.

CoreWeave ($CRWV) is falling while GPU rents keep climbing. Over the past six months, Ornn's OCPI H100 rent is up 79% and B200 is up 84%. Yet CRWV is down 26%, and its 2032 bonds just broke 15%. If compute is this scarce, what is the market pricing? Fundamentally, GPU rental risk is amplified by debt. CoreWeave paid $640M of interest last quarter and expects about $900M this quarter, nearly four times its operating profit. Its newest GPU loan matures across five years against contracts that pay over three. Around 2029, CoreWeave and its lenders will have to re-lease those GPUs at whatever rental prices are then, while the debt payments stay fixed. Future GPU rent is uncertain, which is exactly why operators and lenders need to hedge it. @OrnnExchange and @ICE_Markets are launching monthly, cash-settled futures on OCPI H100 (ICE:HPR) and B200 (ICE:BKL) to make this possible.

The @OrnnExchange GB300 forward surface is now available to Ornn Data Enterprise customers. Use Ornn to see @Nvidia GB300 pricing across contract terms, built from real cleared transactions from reserved contracts. See values at

GPU prices doubled from $4 to $8 per hour. OpenAI cut Luna's prices by 80%, then another 50%. We're getting more work out of each GPU hour, enough to keep prices falling. How long will this last?

Using transaction data from long-term contracts, @OrnnExchange has modeled a price surface for GPU rentals as a function of order size and contract length. The data shows a positive correlation between price and GPU capacity size, and an inverse correlation between price and contract length. Now, what's the underlying reasons behind these trends? 1/ Small orders are filled from leftover capacity. As a result, sellers may discount it to maximize utilization. 2/ Big buyers are buying scarcity. A request for >1k H100s, etc clustered together has only a few vendors to choose from. See the numbers at and rent your capacity at

The case for compute markets and compute indices begins with a growing imbalance: the ambition to scale AI depends on enormous commitments of capital, while the people financing that expansion face extreme market uncertainty. Building lasting AI infrastructure requires a better way to understand, price, and share that risk. Compute has become a strategic resource. Access to it shapes who can develop advanced AI, bring new products to market, and compete on a global stage. For companies and nations alike, securing reliable compute capacity is increasingly tied to economic opportunity and technological independence. The opportunity we are attacking is building the market infrastructure that allows more people to participate in AI’s expansion with confidence. That means giving a developer greater visibility into future compute costs, an operator a stronger basis for planning capacity, and a financier a clearer understanding of the exposure they are taking on. We have an opportunity to make the economics of compute more transparent and its risks more manageable. Doing so will support the next generation of AI infrastructure.

An NVIDIA GPU is a high-yield asset. An H100, bought on the secondary market in Sep 2025 for ~$19k: Resale today: ~$20.5k Net rent earned: ~$13.5k Total: ~$34,000, +76% in 1 year. A B300, bought in Jul 2026 for ~$54k: Resale today: ~$51k Net rent earned: ~$14k Total: ~$65,500, +21% in under 3 months What allows these assets continue to earn? Resale value has held as rental rates have increased and demand has grown across all hardware generations. Resale rates have held within −5% to +7%, while Ornn's H100 index is $2.91/hr, +49% from a year ago, and our new B300 index tracks at $11.32/hr, up 66% from June. H100 marketplace utilization is 87% today. B300 is at 90%. Ornn's forward curves predicts that in 2 years, by Sep 2028: H100 ~$63,500 (+227%) B300 ~$186,000 (+244%) NVIDIA Hopper and Blackwell hardware both hold incredible residual value.

Can the rental price of GPUs predict stock prices? Ornn's A100 GPU rental index has led Astera Labs's stock price by about 5 weeks. - The index's 20-day moves line up with ALAB's 20-day moves 25 trading days later (r = 0.74). - Moved forward 5 weeks, the index tracks ALAB's price at r = 0.87 after removing the shared upward trend. - It holds across the year, and it holds even after stripping out the chip sector's own moves or NVDA's. Astera Labs makes the connectivity chips inside AI servers. GPU rental prices show how tight AI capacity is right now. The companies that sell into AI server build-outs are priced on that tightness after it shows up in orders. Ornn prices compute where it actually trades. The signal shows up here before it shows up anywhere else.

The open vs closed source race is a price war. Since June, the average paid token from OpenAI got 62% cheaper. DeepSeek: −51%. Google: −29%. Anthropic: −11%. Over the same three months, Ornn has tracked B200 rent rising 50%. H200 rose 28%. Models are getting cheaper to buy. The compute to run them is getting more expensive to rent. So where does value accrue? To whoever owns the compute. The cheapest open-weight model finishes a task for about 1/5 the cost of a comparable closed model, and they don't need the newest chips. Every GPU is a shovel when the models are free. See more at

$NBIS's new rates sit 4 to 49 percent above what those chips rent for on OCPI, H200 closest at $5.40 against $5.19 and H100 widest at $4.50 against $3.03. B300 has no rental price on the index, and the forward curve prices it at $7.45 one month out against a $9.50 list.

Live rental prices, by accelerator: B200 $7.09 H200 $5.40 H100 $2.72 RTX PRO 6000 $1.50 A100 $1.05 RTX 5090 $0.68 Each GPU is a separate market with its own supply and demand. An hour of B200 costs 10.4 times an hour of RTX 5090. See more: