ParallelAI (PAI)

YearBull Rank i
#7863
Bull Score
34
YB Market Risk
Low
Cycle
Early

Overview

ParallelAI (PAI) market snapshot: Price $0.00174182, market capitalization $174.18K, and reported 24-hour volume $390.

Trading activity: Reported 24-hour volume equals 0.22% of market capitalization. The local markets snapshot lists Uniswap V2 (Ethereum) among venues with observed trading activity.

YearBull indicators: YearBull Rank #7,863. Bull Score 34/100. YB Market Risk Low. This relative market-volatility label is not an investment-safety assessment. Cycle Early. Observed price change: 24h 0.09% · 7d -10.13% · 30d -23.27%.

Values are descriptive and should be read together rather than as a price forecast. Read the YearBull methodology. Snapshot date: 2026-10-27. Data history: 90 days available in the latest 90-day window.

Methodology responsibility: YearBull’s analytical methodology and presentation rules are developed and maintained by Alan Zelvin, Founder & Lead Crypto Researcher. This note identifies responsibility for the methodology; it does not attribute authorship of this data snapshot.

What is ParallelAI (PAI)?

Source description

“ParallelAI is a high-performance computing platform designed to optimize and accelerate complex computational tasks by utilizing advanced parallel processing techniques. It enables efficient execution across multiple processing units, significantly enhancing computing speed and resource utilization. Deploy Parallel Code To Harness The Full Value Of Your GPUs/CPUs & Drive Down Costs ParallelAI is an advanced AI language platform to improve the use of compute resources. Made for businesses facing performance issues, ParallelAI uses cutting-edge parallel processing techniques to increase efficiency, helping organizations get more done while reducing the need for large infrastructures. Introduction Unleashing The Power Of Parallel Processing Solution To Slash Computation Times For AI Developers By Up To 20x The exponential rise of generative AI technology has created unprecedented demand for GPUs, which current supply is struggling to keep up with. Multiple decentralized GPU marketplaces have emerged to fill this supply gap, allowing AI developers to tap into idle compute power distributed amongst both individuals and enterprise-scale solutions providers. However, increasing the volume of available GPUs is only half the battle in meeting the ever-growing demands of the AI industry. For increasingly complex generative AI tools to become scaleable, not only do they need access to more GPU capacity, but they also need to use that capacity in a more cost-effective and efficient manner. Whilst much is being done to address the capacity issue, little progress is being made in ensuring this new capacity is used in ways that maximize processing speed and minimize costs.”

This source-supplied description may contain old, promotional, or unverified claims and is not YearBull editorial analysis.

ParallelAI (PAI) project facts

Official links and contract records appear in Key Facts. Project details can change, so verify current information with the project.

ParallelAI (PAI) FAQ

How does ParallelAI’s automatic parallelization approach affect AI workloads?

The project states that ParallelAI automatically converts familiar programming patterns into optimized parallel code. This is intended to let applications run tasks concurrently across multiple CPU or GPU cores, rather than relying mainly on sequential execution. By abstracting away much of the complexity of parallel programming, the platform presents parallel processing as a more accessible option for AI developers working with computationally intensive workloads.

How does ParallelAI match computational tasks with GPU providers?

ParallelAI describes a matching process that connects AI developers with GPU providers offering specifications suited to particular parallel-processing workloads. The project says it aggregates capacity from existing decentralized GPU marketplaces, selects best-fit hardware offerings, and optimizes their outputs through its preconfigured parallelization process. This positions the platform as an intermediary between developers seeking compute and providers with available GPU resources.

What role does the PAI token play in ParallelAI’s compute marketplace model?

According to the project, PAI is used to pay for ParallelAI services and to integrate payment flows with decentralized GPU marketplaces where its parallel code is executed. ParallelAI also presents itself as a bulk purchaser of compute power, suggesting that marketplaces accepting PAI could support negotiated discounts. These are stated elements of the project’s proposed payment and procurement model, rather than independently verified outcomes.

Which organizations does ParallelAI present as its intended users?

The project positions ParallelAI primarily for AI developers and businesses experiencing performance or infrastructure constraints. Its stated use case is helping these users execute complex workloads more efficiently across GPUs and CPUs, potentially reducing reliance on large infrastructure deployments. The platform is described as covering the workflow from code execution through compute allocation and back-end infrastructure, although the claimed efficiency gains remain project assertions.

What efficiency improvement does ParallelAI claim for computational tasks?

ParallelAI claims that parallel execution can substantially reduce processing times by distributing work across multiple processing units and improving hardware utilization. Its materials give an example in which a task taking 10 minutes could be completed in 10 seconds, described as an improvement of up to 20 times. This is a promotional performance claim and should not be treated as a guaranteed result for every workload.

ParallelAI metric comparison

This comparison is a stored snapshot generated 2026-10-22 06:31 UTC from 292 daily observations available from 2025-12-30 through 2026-10-22. It is separate from the latest analytical cards above. Percentiles compare the snapshot value with that day's analytical universe; a higher percentile means a larger observed value, not necessarily a better investment characteristic.

MetricSnapshot30d before90d beforeChange vs 30dUniverse percentile
Price$0.00193967$0.00232639$0.00135772-16.6%n/a
Market cap$194.0K$232.6K$135.8K-16.6%P43.9
YearBull Rank#5,905#3,834#6,464Lower by 2,071P30.7
Bull Score38/10055/10014/100-17.0 ptsP31.4
Turnover0.06%0.10%0.07%0.0 ptsP24.5
YB Market RiskLowLowLowUnchangedn/a
CycleEarlyEarlyEarlyUnchangedn/a

Median absolute daily movement 2.31%; distance from the highest local daily price -94.7%; circulating supply change 0.0%. These measurements are descriptive and do not predict future direction.

YearBull Rank timeline

Most recent YearBull Rank reading for parallelai is #7863.

Rank timeline (last 365 days)

Rank change (nearest points).

Reading rule: lower is better in this ranking.

  • 7d window (2026-10-20): #5994 → #7863 (down by 1869).
  • 30d window (2026-09-27): #4391 → #7863 (down by 3472).

YearBull Rank is a relative placement score used on YearBull to compare a coin against peers within the same dataset. Smaller numbers mean the coin sits higher in the YearBull list.

Liquidity note: If the line only moves on high-volume days, liquidity is a key filter. relative rank is sensitive to who is active in the window.

Where it trades: If rank deteriorates while the curve stays smooth, it can be cohort strength shifting. consolidation can make rank more stable.

Regime context: If both windows align, the direction is clearer. cycle shifts often show up as slope changes, not spikes.

Risk posture: If it is flat for long, the coin may be tracking the cohort. ranking moves can reflect regime shifts rather than one-off events.

Practical note: rank is relative by design, so peers matter.

Update date: 2026-10-27

Disclaimer: Not financial advice. Cryptocurrency investments carry high risk. DYOR.

Editorial note: This analysis was prepared by the YearBull research team under the direction of Alan Zelvin, Founder and Lead Crypto Researcher. The assessment follows YearBull’s internal research methodology and editorial standards. Methodology · Editorial Policy

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ParallelAI (PAI) Markets

Stored venue snapshot. Markets last checked: 2026-07-26. Updated on a rolling basis; no fixed refresh date is promised. Venue listings and volumes are stored snapshots, not live quotes.
Exchange Top Pair Stored 24h volume (snapshot) Trust Rank
Uniswap V2 (Ethereum) PAI/WETH $170 #227

Listings are ordered by reported snapshot volume. Trust Rank is an external venue-quality indicator; it is not an endorsement or a solvency guarantee.