$334.97K
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$334.97K
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Trader mode: Actionable analysis for identifying opportunities and edge
This market will resolve to “Yes” if, for any day between February 2 and February 28, 2026, the Silicon Data H100 Index (SDH100RT) has a price equal to or above the listed price. Otherwise, this market will resolve to “No.” The resolution source for this market is Silicon Data — specifically, the H100 Index chart data available at https://www.silicondata.com/products/silicon-index. The daily values shown on the chart will be used for resolution. Daily data will be considered finalized once the
Prediction markets currently give only a 1% chance that the price to rent an H100 GPU will hit $2.25 per hour by the end of February 2026. In simpler terms, traders see this as a near-certain "no." There is roughly a 99 out of 100 chance that rental costs will stay below that specific low threshold. This shows very strong collective confidence that high demand for these chips will keep prices well above that floor.
Two main factors explain these odds. First, the H100 GPU from Nvidia is the primary engine behind training advanced AI systems. Demand from large tech companies and startups is intense and shows no sign of slowing before 2026. Rental prices reflect this ongoing scarcity.
Second, the specified target of $2.25 per hour is exceptionally low. According to tracking by Silicon Data, market rates have historically been multiple times higher. For a price to fall to that level, there would need to be a massive, unexpected increase in supply or a severe drop in AI development demand. Traders betting real money consider both scenarios very unlikely in the next two years.
While the resolution date is February 28, 2026, the market watches for earlier signals. Key developments include announcements from Nvidia about next-generation chip supply, major cloud providers like AWS or Azure changing their rental pricing, and any breakthroughs in alternative AI chips that could reduce reliance on H100s. Significant moves in this market would likely happen well before the February 2026 deadline.
Markets tracking prices for specialized commodities like GPU rentals can be useful, but they have limits. They effectively aggregate expert opinions on supply and demand. However, they can be surprised by sudden technological shifts or new chip releases. For a stable, high-demand asset like the H100, near-unanimous predictions like this 1% chance are often correct, but they cannot account for truly unforeseen events that could disrupt the entire AI industry.
The Polymarket contract asking if the Silicon Data H100 Index will hit $2.25 per GPU-hour by February 28, 2026, is trading at just 1¢, implying a 1% probability. This price indicates the market views the event as extremely unlikely. With over $335,000 in total volume across related contracts, there is significant speculative capital focused on this niche but economically important question. The market structure, offering a ladder of target prices from $2.25 to $3.00, allows traders to express nuanced views on future hardware costs.
The near-zero probability for the $2.25 target reflects a consensus that GPU rental economics have fundamentally shifted. The SDH100RT index, which tracks spot prices for renting Nvidia's flagship H100 processors, has collapsed from highs above $4.00 per GPU-hour in early 2024 to approximately $1.00 as of early 2025. This 75% price crash is driven by two concrete factors. First, a massive increase in supply from cloud providers like CoreWeave and Lambda, who have deployed billions of dollars worth of new H100 clusters. Second, demand growth for AI training, while strong, has not kept pace with this supply surge, leading to intense competition and price erosion among rental vendors.
For prices to rebound to $2.25, a severe supply shock or demand spike would be required. The primary bullish risk is a cascading failure among major cloud capital expenditure plans, potentially triggered by a broader economic downturn that halts new data center construction. Conversely, the launch of a "killer app" for AI requiring unprecedented compute scale could suddenly soak up available capacity. However, the market timeline to February 2026 also factors in the arrival of Nvidia's next-generation Blackwell GPUs. As Blackwell supply ramps through 2025, it will likely further displace H100 demand for cutting-edge projects, cementing its status as a lower-cost legacy workload chip and suppressing any major price recovery. The 1% odds suggest traders see these disruptive scenarios as remote.
AI-generated analysis based on market data. Not financial advice.
This prediction market topic concerns whether the rental price for NVIDIA H100 GPUs will reach a specified threshold during February 2026. The market resolves based on the Silicon Data H100 Index (SDH100RT), a benchmark tracking the daily spot price for renting these high-performance computing chips. The index provides a standardized measure of market value for a critical component in artificial intelligence development and scientific research. GPU rental markets have become a significant financial indicator, reflecting demand from AI companies, cloud providers, and academic institutions that require massive computational power but cannot afford or do not need to purchase hardware outright. The specific focus on February 2026 allows participants to speculate on seasonal demand patterns, potential new hardware releases, and the broader economic cycle affecting technology investment. Interest in this market stems from multiple sectors. AI startups and researchers monitor rental prices to forecast operational costs. Investors view GPU availability and pricing as leading indicators for the health of the AI industry. Hardware manufacturers and data center operators use these prices to make capacity planning decisions. The volatility of GPU rental costs, driven by factors like cryptocurrency mining demand in previous cycles and the current AI boom, makes this a dynamic and closely watched metric. The resolution mechanism relies on Silicon Data, a firm that aggregates pricing data from multiple cloud and rental providers to create its indices, similar to how stock indices track financial markets.
The market for renting GPU compute power has evolved significantly over the past decade. The first major price volatility occurred during the cryptocurrency boom of 2017-2018, when miners rented cloud GPUs to mine Ethereum, causing shortages and price surges for academic and commercial users. This period established the concept of GPU compute as a tradable commodity. A second phase began around 2020 with the rise of large language models. The computational requirements for models like GPT-3 created sustained, industrial-scale demand that dwarfed previous cycles. In 2022, the release of NVIDIA's H100 GPU, with specialized transformers engines for AI work, established a new benchmark for performance. Initial supply was constrained, leading to reported wait times of over six months for direct purchases and pushing users toward rental markets. The Silicon Data H100 Index launched in early 2023 to provide transparency to this opaque market. Historical data from the index shows considerable volatility. In Q4 2023, prices spiked following announcements of major AI funding rounds and new model development projects. Prices moderated in early 2024 as cloud providers added capacity, but remained well above the cost of previous-generation hardware. This historical pattern of demand spikes, supply lag, and eventual capacity catch-up informs expectations for the February 2026 timeframe.
GPU rental prices function as a real-time thermometer for the artificial intelligence industry. When prices are high, it indicates that demand for AI training and inference exceeds the available supply of computing power. This can slow down innovation by making research and development prohibitively expensive for all but the best-funded companies. It also creates a competitive moat for large tech firms that own their own hardware, potentially centralizing AI advancement. Economically, high GPU rental costs increase the operating expenses for any business built on AI, from startups to established enterprises. This can affect profit margins, investment decisions, and the pace of product development across the technology sector. For investors and policymakers, sustained high prices could signal either a fundamental shortage of manufacturing capacity or speculative overheating in AI investments. Downstream consequences include delayed scientific research in fields like climate modeling and drug discovery, which also depend on high-performance computing. The cost of accessing these tools influences which institutions can participate in cutting-edge work.
As of late 2024, the market for H100 rentals remains tight but has moved from extreme shortage to constrained availability. Major cloud providers have secured more supply from NVIDIA and are offering more instances. However, list prices for on-demand rentals remain high, and significant discounts are typically only available through one or three-year committed use contracts. Silicon Data's index has shown prices stabilizing after the surges of 2023. Industry attention is shifting toward NVIDIA's next-generation Blackwell architecture GPUs, expected in 2025. The anticipation of this new hardware could influence H100 rental prices in 2026, as users may delay projects or shift demand to newer chips, potentially freeing up H100 supply.
The Silicon Data H100 Index (SDH100RT) is a daily benchmark price for renting a single NVIDIA H100 GPU. It is calculated by Silicon Data using aggregated pricing data from multiple cloud providers and specialized GPU rental marketplaces. The index is designed to reflect the real-time spot market price for this specific computational resource.
H100 rental prices are high due to immense demand from companies training large AI models, coupled with constrained supply. Manufacturing these advanced chips is complex and capacity-limited. The chips also offer unique performance gains for AI work, creating a premium. High purchase costs for data center operators are passed through to rental rates.
The NVIDIA H100 is a data center GPU built for parallel processing in servers, not for gaming. Key differences include specialized tensor cores optimized for AI math, much higher memory bandwidth (3.35TB/s), support for connecting multiple chips together via NVLink, and error-correcting code (ECC) memory for reliability in continuous operation.
Prices could spike if a major AI company announces a new large-scale training project, if there are supply chain disruptions for next-generation chips, or if cryptocurrency mining becomes profitable again on GPUs. Seasonal factors, like academic grant cycles funding new research, could also concentrate demand in a specific month.
The current and historical values for the SDH100RT index are published on the Silicon Data website at https://www.silicondata.com/products/silicon-index. The site displays a chart with daily price data, which is the definitive source for resolving the prediction market.
Educational content is AI-generated and sourced from Wikipedia. It should not be considered financial advice.
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