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Price of NVIDIA A100 SXM4 compute by Jul 31, 2026?
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Price of NVIDIA A100 SXM4 compute by Jul 31, 2026?
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AI Analysis
Trader mode: Actionable analysis for identifying opportunities and edge
About This Event
07/01/26 If the value of A100 SXM4 compute per hour is above X on Jul 31, 2026, then the market resolves to Yes. The market resolves based on the value of A100 SXM4 compute per hour as reported by Ornn, https://dashboard.ornnai.com/. Revisions to the underlying made after expiration will not be accounted for. The "USD" iteration of the index will be used unless explicitly stated otherwise. If no data is available for the time period by the Expiration Date, all strikes excluding "No data" or "No
Current Market Outlook
Kalshi traders give a 54% probability that NVIDIA A100 SXM4 compute will cost more than $1.39 per hour by December 31, 2026. That is essentially a coin flip. The market sees the price hovering right around that threshold with no strong conviction either way.
This is a niche market tracking a specific GPU pricing index from Ornn AI. The A100 SXM4 launched in 2020 and remains relevant for AI inference workloads, though it has been superseded by the H100 and upcoming Blackwell B200. The $1.39 threshold sits near current spot pricing, which has been declining as newer hardware absorbs demand.
Key Factors Driving the Odds
Three forces are pulling in opposite directions. First, cloud providers are aggressively discounting older GPU generations. Google Cloud and AWS have cut A100 reserved instance prices by 30-40% since 2023 as H100 capacity ramped. Second, the supply overhang from 2022-2023 crypto mining busts still depresses spot GPU prices. Third, the counterargument: AI inference demand is exploding, and A100s remain the second-best option for many workloads. If H100/B200 supply remains constrained through 2025, A100 prices could stabilize or even rise.
The 54% price reflects genuine uncertainty about whether demand growth or supply glut wins by late 2026.
What Could Change These Odds
Watch the Ornn AI dashboard monthly. If the index drops below $1.10 by mid-2025, the market will likely shift below 30% as depreciation momentum takes hold. If it stays above $1.50 through Q3 2025, the probability could climb above 70%.
The wildcard is NVIDIA's Blackwell ramp. If B200 production hits volume by Q2 2026, A100 demand collapses and prices follow. If Blackwell is delayed, A100 retains pricing power. The Ornn index methodology matters too: it tracks "compute per hour" which includes cloud margins, not raw hardware costs. A price war among hyperscalers could crash the index even if hardware costs stay flat.
AI-generated analysis based on market data. Not financial advice.
Overview
The prediction market question regarding the price of NVIDIA A100 SXM4 compute by December 31, 2026, centers on the cost per hour to access one of the most widely used graphics processing units (GPUs) for artificial intelligence and high-performance computing. The A100, released in 2020, is built on NVIDIA's Ampere architecture and has been a workhorse for training large language models, scientific simulations, and data analytics. The market uses the Ornn AI compute index, specifically the USD iteration, to track the spot price of A100 SXM4 compute hours. This index aggregates cloud provider pricing and spot market rates, giving a real-time view of supply and demand dynamics for this specific hardware. The market resolves to Yes if the per-hour price exceeds a defined threshold (not specified in the prompt) by the end of 2026, or to No if it falls below, with a 'No data' option if the index stops reporting. The interest in this market reflects the broader volatility in GPU compute pricing, which has swung dramatically since 2022. During the AI boom of 2023-2024, demand for A100s surged, driving spot prices to over $4 per hour on some cloud platforms, as companies like OpenAI, Meta, and Microsoft scrambled for capacity. However, the introduction of newer GPUs, such as the H100 (Hopper) in 2022 and the B100 (Blackwell) expected in 2024, has created a complex market where older hardware like the A100 may see price declines, but demand for compute overall remains high. The market also factors in potential supply chain disruptions, geopolitical tensions affecting chip manufacturing, and the expansion of cloud data centers. By 2026, the A100 SXM4 will be a six-year-old product, which typically leads to depreciation and lower pricing as newer models dominate. However, the market's outcome depends on whether compute demand continues to outpace supply, or if the A100 becomes a commodity with falling prices. The Ornn index provides a transparent benchmark, but its reliability is tied to the continued operation of the dashboard and the accuracy of its data sources. This market is particularly relevant for investors, cloud customers, and AI startups that rely on GPU pricing models for budgeting and strategic planning. People are interested in this market because it offers a speculative bet on the long-term trajectory of AI infrastructure costs. If compute prices rise, it suggests sustained demand and potential bottlenecks, which could benefit GPU manufacturers and cloud providers. If prices fall, it signals oversupply or technological obsolescence, which could reduce barriers to AI development. The market also reflects broader trends in semiconductor economics, cloud pricing strategies, and the pace of hardware innovation.
Historical Context
The A100 GPU was announced by NVIDIA in March 2020 and began shipping in May 2020, during the early stages of the COVID-19 pandemic. Its launch coincided with a surge in demand for remote computing and AI research. The A100 SXM4 variant, with 40 GB of HBM2 memory and 312 teraflops of FP16 performance, became the standard for training large neural networks. In 2021, cloud providers began offering A100 instances, with AWS launching p4d instances in November 2020 and Azure following in 2021. Prices started around $3-4 per hour for an 8-GPU instance, but spot market rates fluctuated based on availability. In 2022, NVIDIA released the H100 GPU based on the Hopper architecture, which offered significant performance improvements over the A100. This led to a gradual shift in demand, though A100s remained popular due to their availability and lower cost. The AI boom of 2023, driven by the release of ChatGPT and other large language models, caused a spike in GPU demand. Cloud providers faced shortages, and prices for A100 compute rose to over $5 per hour in some spot markets. By late 2023, NVIDIA's revenue from data center GPUs exceeded $14 billion per quarter, reflecting the insatiable demand. In 2024, NVIDIA announced the B100 GPU based on the Blackwell architecture, expected to ship in late 2024. This continued the cycle of hardware obsolescence, but also maintained demand for older GPUs as budget options. Geopolitical factors, such as US export restrictions on advanced GPUs to China, affected global supply and pricing. The Ornn index was established around 2023 to provide transparent pricing data, becoming a reference for the prediction market. By 2025, the A100 SXM4 will be a five-year-old product, and its price trajectory will depend on whether demand for AI compute continues to grow or if newer GPUs cannibalize its market.
Why It Matters
The price of A100 SXM4 compute by December 2026 matters because it serves as a barometer for the cost of AI infrastructure. If prices remain high, it indicates that demand for compute continues to outstrip supply, which could constrain AI development for startups and researchers with limited budgets. This would benefit NVIDIA and cloud providers, but could slow innovation by making access to powerful GPUs expensive. Conversely, falling prices would lower barriers to entry, enabling more organizations to train and deploy AI models, potentially accelerating progress in fields like healthcare, autonomous driving, and natural language processing. This market also has implications for investors and policymakers. For investors, the price trend signals the profitability of GPU manufacturing and cloud services. For policymakers, it reflects the health of the semiconductor industry and the impact of export controls. If compute prices drop sharply, it might suggest that the AI boom is cooling or that supply chains have caught up with demand. The outcome of this market will be watched by cloud customers negotiating contracts, AI startups planning budgets, and hardware manufacturers assessing product lifecycles.
Educational content is AI-generated and sourced from Wikipedia. It should not be considered financial advice.
