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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 April 30, 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 fol
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 focuses on whether the rental price for NVIDIA H100 GPUs will reach or exceed a specific threshold by April 30, 2026. The resolution depends on the Silicon Data H100 Index (SDH100RT), a benchmark tracking the daily spot market price for renting these high-performance computing chips. The index provides a transparent, market-based reference point for a commodity that has become central to artificial intelligence development. The question reflects intense investor and industry interest in the supply, demand, and cost dynamics of the specialized hardware required to train and run large AI models. NVIDIA's H100 Tensor Core GPU, launched in 2022, is widely considered the industry standard for AI training workloads. Its performance advantages in processing the matrix calculations fundamental to neural networks have created overwhelming demand from cloud providers, AI startups, and research institutions. The rental market for these GPUs has emerged as a critical segment, allowing organizations to access computing power without massive capital expenditures. Prices fluctuate based on factors like chip supply from NVIDIA and its manufacturing partners, competitive pressure from alternative chips, and the investment cycles of major AI companies. The period from February to April 2026 specified in the market will capture pricing trends during a timeframe when next-generation GPU architectures from NVIDIA and competitors are expected to be in broader deployment, potentially altering the supply-demand balance for the H100.
The market for renting high-end GPUs traces its origins to the rise of cryptocurrency mining in the 2010s, where miners rented graphics cards for proof-of-work computations. However, the modern AI GPU rental market began forming around 2020 with the scaling of large language models. The release of NVIDIA's A100 GPU in 2020 established a new benchmark for AI training performance, and cloud providers began offering instances powered by these chips. Rental prices for A100s saw volatility, but the market remained relatively niche. The inflection point came with the launch of the H100 in late 2022. Its performance, reportedly 4 to 6 times faster than the A100 for some AI training tasks, coincided with the explosive popularity of generative AI following ChatGPT's November 2022 release. Demand for H100 compute immediately outstripped supply. By mid-2023, reports indicated rental costs could exceed $30,000 per month for a cluster of eight H100s, and waitlists for access stretched for months. The 2024 period saw continued scarcity, though the introduction of NVIDIA's H200 and the anticipated B100 architectures began to shift focus toward future supply. Historically, GPU rental prices have shown sensitivity to product cycles, with prices for previous-generation chips like the A100 declining as newer generations launch and supply catches up with demand.
The price of H100 rentals is a direct proxy for the cost of innovation in artificial intelligence. For startups and researchers, high rental prices create a significant barrier to entry, potentially concentrating advanced AI development in the hands of a few well-funded corporations. This affects the diversity of ideas and applications in the field. Economically, these costs are passed through the entire AI ecosystem, influencing the pricing of AI-powered services for businesses and consumers. Persistent high prices could slow the adoption of AI tools across industries like healthcare, finance, and scientific research. For investors and policymakers, the H100 rental index serves as a real-time indicator of the balance between technological ambition and physical manufacturing constraints. It highlights vulnerabilities in a concentrated supply chain for critical technology. Downstream consequences include potential delays in AI model development, increased venture capital burn rates for AI companies, and strategic stockpiling of chips by nations and large firms, mirroring behaviors seen in other strategic commodity markets.
As of early 2025, the supply of H100 GPUs has improved from the extreme shortages of 2023-2024, but demand remains strong. NVIDIA has ramped production through TSMC, and major cloud providers have deployed substantial clusters. However, the market is in a transitional phase. NVIDIA has begun shipping its next-generation Blackwell architecture GPUs, including the B200 and GB200. While these new chips are designed to be more powerful and efficient, initial supply is limited and they command a higher price. This transition period often creates a complex pricing dynamic for the previous generation. Some users may delay projects for Blackwell access, potentially softening H100 demand. Others may seek out H100 rentals as a more available or cost-effective option for current workloads, supporting its price. The Silicon Data H100 Index provides ongoing visibility into these spot market fluctuations.
The Silicon Data H100 Index (SDH100RT) is a daily benchmark price that tracks the spot market cost to rent NVIDIA H100 GPUs. It aggregates data from multiple cloud providers and rental marketplaces to create a standardized reference point for this specific computing commodity.
NVIDIA's H100 GPUs contain specialized Tensor Cores optimized for the matrix multiplication operations that are fundamental to training and running large neural networks. Their architecture and software ecosystem (CUDA) provide a significant performance advantage for AI workloads compared to general-purpose chips.
Prices increase when demand from AI companies outstrips the supply of chips from NVIDIA's manufacturing pipeline. Prices can decrease when new GPU generations launch, when supply improves, or if demand slows due to economic factors or a shift in AI research focus.
AI startups, academic research institutions, and companies running intermittent large-scale AI training jobs often rent. Renting avoids large upfront capital costs and provides flexibility. Large tech firms with sustained demand typically purchase chips directly for their data centers.
NVIDIA's Blackwell B200 and GB200 GPUs, announced in 2024, are designed to offer significantly higher performance for AI training and inference. However, as a new architecture, initial supply is constrained and cost is higher, which influences the market for the still-powerful and more available H100.
Educational content is AI-generated and sourced from Wikipedia. It should not be considered financial advice.
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| Market | Platform | Price |
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| Poly | 74% |
| Poly | 27% |
| Poly | 23% |
| Poly | 13% |
| Poly | 9% |
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| Poly | 8% |
| Poly | 6% |
| Poly | 6% |
| Poly | 4% |
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