Author: David Lopez Mateos
Compiled by: Deep Tide TechFlow
Deep Tide Guide: The media likes to summarize the rise and fall of GPU compute prices with a single number, but the reality is: the quotes from four index providers on the Bloomberg terminal diverge by more than $2, with inconsistent directions and rhythms. The author of this article is David Lopez Mateos, founder of the GPU compute trading platform Compute Desk. Using first-hand transaction data, he breaks down the real pricing structure of H100 and B200, revealing a primitive market with no consensus benchmark, no standard contracts, and no forward curve—compute power is being hoarded and sublet like short-term rental apartments.
Media headlines would make you think GPU compute prices are soaring. This narrative is comfortable, perfectly fitting into the macro framework of "supply crunch + bottomless AI demand," and it implies something reassuring: we have a well-functioning market with clear and readable price signals.
But we don't. This narrative is almost entirely built on a single index, and it implies something that shouldn't be implied: the GPU rental market has become efficient enough to be represented by a single number.
The supply crunch is real, but the crunch felt by different people is completely different—depending on who you are, where you are, what contract you're trading, and what compute asset. Faced with this opacity, the market's natural reaction is not orderly price discovery but hoarding: locking in GPU time you might not even need yet, because you're not sure if you can get it at any price next month. Where there is hoarding and no transparent benchmark, fragmented secondary markets emerge. At Compute Desk, we have already facilitated tenants subletting their clusters like apartments during major events. This is not a hypothesis; it is happening.
Indices Do Not Converge
In mature commodity markets, indices built on different methodologies tend to converge. Brent crude and WTI have a few dollars of spread due to geographic location and crude quality, but they move in sync directionally (Figure 1). This convergence is a hallmark of an efficient market.
Caption: Comparison of Brent and WTI crude oil price trends, showing high directional alignment
There are now three GPU pricing index providers on the Bloomberg terminal: Silicon Data, Ornn AI, and Compute Desk. SemiAnalysis just released a fourth—a monthly H100 one-year contract price index based on survey data from over 100 market participants. Silicon Data and Ornn publish daily H100 rental indices, Compute Desk aggregates data at the Hopper architecture level, and SemiAnalysis captures negotiated contract prices rather than listed or crawled prices. Different methodologies, different frequencies, different angles of insight into the same market. Overlaying them reveals clear divergence (Figure 2).
Caption: Overlay comparison of four GPU indices, showing significant divergence in price levels and trends
Where Exactly Is the Price Increase Happening
Using Compute Desk data, we can break down H100 price changes by supplier type and contract structure, and overlay Silicon Data's SDH100RT index (Figure 3). All indicators show prices rising, but the starting points and magnitudes vary greatly depending on the index and contract type.
Caption: H100 price trends by contract type overlaid with the SDH100RT index
Compute Desk's H100 neocloud data tells a more specific story than the aggregate index. On-demand pricing was relatively stable throughout the winter, around $3.00/hour, then surged sharply to $3.50 in March. Spot pricing was noisier and lower, with only a slight upward trend until March. Silicon Data's SDH100RT shows a smoother, steady rise, increasing from $2.00 to $2.64 over the same period. The two indices remain at different price levels and describe different time rhythms: Compute Desk shows a March spike, Silicon Data shows a slow climb.
One-year reserved pricing was largely flat until February, then jumped sharply from $1.90 to $2.64 at the end of March—not a gradual catch-up, but a sudden repricing. This looks more like suppliers collectively adjusting contract rates after the on-demand market tightened, rather than a continuous structural demand driver.
The March story for B200 is even more dramatic (Figure 4). Compute Desk's on-demand index exploded from $5.70 to over $8.00 within weeks. Silicon Data's SDB200RT surged from $4.40 to $6.11 before falling back to $5.47. Both indices recorded this move, but the starting points differed by over $2, and the shapes of the rise and fall were different. B200 has less than five months of data, fewer suppliers, and larger spreads; the two indices are viewing the same event through very different lenses.
Caption: B200 on-demand and reserved price trends, overlaid with Compute Desk and Silicon Data data
Infrastructure Issues, Not Just Geographic Differences
Commodity markets have basis differentials. Appalachian natural gas is a textbook case: massive reserves sit atop structurally constrained pipeline capacity, with utilization rates in the Pennsylvania-Ohio corridor often exceeding 100%, and new projects like the Borealis Pipeline not coming online until the late 2020s.
The GPU market has a similar situation: an H100 in Virginia and an H100 in Frankfurt are not the same economic good. But geographic differences alone cannot explain why indices measuring the same market diverge so much. The dislocation in the GPU market is deeper than in Appalachian natural gas. The problem with natural gas is a single missing link: pipeline capacity connecting supply and demand. The infrastructure gap in the compute market exists on both the supply and demand sides. Physical infrastructure—the consistent networks, predictable configurations, and predictable availability needed for reliable compute distribution—is not yet mature and sometimes simply doesn't work. Financial infrastructure—standardized contracts that compress spreads despite physical differences, transparent benchmarks, arbitrage mechanisms—also doesn't exist yet.
The data tells one story. The real, painful experience of trying to procure compute in early 2026 tells another. On-demand capacity for all GPU types is virtually sold out. Finding 64 H100s is difficult: Compute Desk shows 90% of suppliers have zero on-demand cluster availability, and the reserved market isn't much better. In a well-functioning market, this level of scarcity would have pushed prices to a new equilibrium. But it hasn't. This suggests suppliers themselves lack real-time pricing intelligence to adjust. Prices are rising, but too slowly to clear the market. The gap between listed prices and true willingness to pay is being filled by hoarding, subletting, and informal secondary market transactions.
What Needs to Change
The current GPU compute market has seven core problems:
No consensus benchmark. Multiple indices coexist with different methodologies and contradictory conclusions.
Aggregate narratives mask structure. A single "H100 price" number masks huge differences between supplier types and contract terms.
Lack of transaction-level data. In bilateral markets, the deviation between listed prices and actual transaction prices is very large.
No contract standardization. Most GPU rentals are bilaterally negotiated with varying terms. Shorter, more standardized contract terms would improve liquidity and price discovery.
No delivery quality guarantee. Interconnect topology, CPU pairing, network stack, and uptime vary enormously. Buyers need to know the quality of the compute they are purchasing before committing.
Contracts lack liquidity. If demand changes during a reservation, options are limited: either eat the cost or sublet informally. The market needs infrastructure to transfer or resell committed compute, allowing capacity to flow to those who need it most.
No forward curve. Without the ability to price forwards, there is no hedging. This is why lenders discount GPU collateral by 40%-50%, keeping financing costs high.
Building a properly functioning market for the most important commodity of the century cannot advance on just one front. Measurement, standardization, contract structure, delivery quality, liquidity—these must advance in sync. Until then, no one can truly say what a GPU hour is worth.
