PEARL

PRL

2026-10-14 06:14:52

PRL tops 1.9 USDT to set a new all-time high

Market data cited by Odaily shows PRL has risen above 1.9 USDT, setting a new all-time high. The same update said the token is up more than 60% over the past seven days. No other price details or trading metrics were provided in the brief. Odaily also linked to a related article titled, "PRL surges 3x, can Pearl become an AI version of Bitcoin?" The item was published as a short market update under the homepage headline category.

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Pearl

2026-10-12 01:01:02

Pearl’s 320,000-GPU network is being framed as a live market for AI compute pricing

A market for GPU compute worth hundreds of billions of dollars still lacks a pricing benchmark that reflects what large buyers actually pay. In an analysis published by TechFlowPost and attributed to Decentralisedco, Pearl, a Layer 1 blockchain built around proof-of-work, is presented as an alternative way to discover the price of AI compute through real-time market behavior rather than posted cloud rate cards. The article argues that public GPU indexes from Silicon Data, Ornn, and Compute Desk struggle to price H100 rentals because the same chip can represent very different products depending on networking, uptime guarantees, cooling, and facility quality. It also notes that CME’s planned GPU futures, set for Oct. 5, 2026, use cash settlement against a Silicon Data survey index based on public list prices, even though large buyers often negotiate discounts of 40% to 50%. Pearl’s model replaces SHA-256 hashing with matrix multiplication on GPUs and uses a mechanism called NoisyGEMM to keep miners from shortcutting the work. According to the piece, the network measures how much useful matrix computation GPUs complete every 194 seconds under real operating conditions. With 320,000 GPUs participating across multiple regions, the resulting hashrate is described as a continuously updated signal of the opportunity cost of compute, shaped by whether operators choose to mine Pearl or rent capacity to paying AI customers.

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Pearl

2026-10-10 13:44:00

Pearl draws TOKEN2049 attention as PoW-AI narrative gains steam, but PRL still faces proof-of-demand questions

Pearl, the project behind token PRL, emerged as one of the more talked-about names around TOKEN2049 as interest in fresh proof-of-work designs picked up while some Layer 2 projects, including Blast and Abstract, were described in the source as shutting down one after another. The project runs as an independent PoW chain launched on April 27, 2026 and uses Proof of Useful Work, replacing Bitcoin-style SHA-256 mining with matrix multiplication workloads common in AI inference. The pitch is simple: the same GPU compute used to serve AI customers could also mine PRL, giving operators two revenue streams. The project has a research-heavy founding team and says its PoUW paper was published in 2025. It also has an early commercial reference point: Together AI announced on May 15 that it was working with Pearl Research Labs on Gemma-4-31B-it-Pearl inference, saying mining subsidies cut inference costs by more than 25%. Still, the source points to major open questions. Pearl has not disclosed key operating metrics such as real paid revenue, daily inference volume, or customer retention. Token distribution and economics also remain under scrutiny, with low early mining difficulty, continued token issuance, and no confirmed large-scale buyback, burn, or revenue-sharing mechanism tied to PRL.

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Pearl

2026-10-11 02:48:00

Pearl pitches a market-based price signal for GPU compute through on-chain mining activity

GPU pricing remains hard to standardize because an H100 hour is not a uniform product. Public indexes tracked on Bloomberg terminals may follow listed rental rates, but the underlying service can vary sharply by networking, uptime, cooling, facility quality, and contract structure. The article cited by PANews argues that this makes both spot indexes and futures design difficult, especially when large buyers often negotiate prices 40% to 50% below public rate cards. Against that backdrop, the piece points to Pearl, a Layer 1 blockchain that uses GPU-based matrix multiplication rather than SHA-256 hashing in its proof-of-work process. According to the article, Pearl’s network has 320,000 GPUs, and their collective decision to mine on Pearl or leave for external rental markets creates a live opportunity-cost signal for compute. The network updates around every 194 seconds, which the article presents as a continuous benchmark for real-world AI compute under actual power, cooling, and networking conditions. The piece also describes Pearl’s NoisyGEMM design, which adds structured noise to prevent miners from shortcutting matrix multiplication while still allowing the system to recover useful work. It further notes that Together AI announced an inference endpoint on May 15, 2026, offering Gemma-4-31B-it-Pearl at more than 25% below standard cloud rates by combining inference revenue with Pearl mining rewards.

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Pearl

2026-10-10 05:00:56

Why Pearl Became One of the Most Mentioned Projects at Token2049

Pearl has become a frequent topic at Token2049, largely because it sits at the intersection of two strong themes in 2026: AI and fair launch. The project pitches itself as a Proof-of-Useful-Work network, where miners use GPU power for matrix multiplication rather than conventional hash calculations, with the idea that mining could also support AI inference. That narrative has helped Pearl earn comparisons to an "AI-native Bitcoin" among supporters. Bulls point to three main arguments: a sovereign-free digital asset backed by compute and energy, a mining model aligned with AI-driven energy demand, and a "2-for-1" unit economy in which one GPU workload could generate both inference revenue and PRL rewards. The pushback is more technical. Pearl’s own design, as described in the source text, can verify that matrix multiplication happened, but not whether the work was tied to an actual AI inference task. Critics argue this opens the door to "job-binding failure," where miners can perform cheaper random matrix calculations instead of useful inference while still receiving rewards. The article also cites research involving 8,012 miner nodes and 44 accepted shares generated from random matrices. Pearl’s team has acknowledged that the chain currently has no onchain metric for measuring the share of useful mining and argues the economics could improve through future protocol upgrades. That leaves Pearl positioned less as a settled AI infrastructure play and more as a high-conviction bet on whether AI inference and GPU mining can eventually merge into one system.

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Pearl

2026-10-09 04:13:19

Pearl says AI inference and PoW mining can run on the same GPU. The network still appears dominated by random matrices.

Pearl is pitching a narrow but ambitious idea: replace hash-based proof-of-work with matrix multiplication so the same GPU workload can both secure a blockchain and produce verifiable AI computation. The standalone PoW chain, whose token is PRL, argues that one batch of neural-network style multiply-accumulate operations can generate block rewards and useful inference at the same time. The project has already released certified model variants on Hugging Face, open-sourced code, and launched a Gemma-based API with Together AI that, according to Together’s announcement, is priced 25% below a standard endpoint with the discount offset by the token’s future value. The harder question is whether the network is actually doing useful AI work. A June 2026 paper by researcher Abhinaba Basu, titled The Usefulness Gap in Proof-of-Useful-Work, estimated Pearl’s network at roughly 24 EH/s at the time, equivalent to about 320,000 RTX 3090-class GPUs and 112 megawatts of power draw, while concluding that useful AI output was zero. The paper sampled 8,012 miner work units, found matrix-multiplication related strings but no machine-learning framework strings in major mining software, and showed that a custom miner using randomly filled matrices could still win accepted shares. That leaves Pearl facing its central problem: the protocol can verify that a matrix product was computed, but not that the input matrices came from a real customer task rather than synthetic data. The project’s bet is that inference demand will eventually make real jobs more profitable than filling random numbers. Until then, critics argue, Pearl remains a GPU mining chain with different math rather than a network delivering meaningful AI compute at scale.

540

Entropy

2026-09-26 08:13:56

Entropy spends 500 HYPE to secure Pearl ticker, with PRL perpetuals possibly next

BlockBeats reported on Sept. 26 that Entropy, a deployer in the HIP-3 market, spent 500 HYPE, worth about $45,000, to win the Pearl (PRL) code. The move may signal that Entropy is preparing to list a perpetual contract tied to PRL. Pearl is described in the report as an independent public blockchain scheduled to go live in April 2026. The project positions itself as an "AI version of Bitcoin." According to the description cited by BlockBeats, miners use GPUs to perform large-model matrix computations, and each block simultaneously produces verifiable AI compute. The token’s total supply is about 2.1 billion. The report did not provide a launch date for the possible perpetual contract, but it linked Entropy’s purchase of the PRL code to expectations that the product could be listed soon.

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Pearl

2026-09-25 18:34:07

Pearl uses matrix-multiplication PoW to combine AI inference with mining

Techub, citing a post from @Delphi_Digital, said Proof-of-Useful-Work blockchain Pearl is designed to let miners use the same compute resources for AI inference and block production. The project aims to create two complementary revenue streams: block rewards would help subsidize inference costs, while payments from customers would help cover the base cost of mining. Pearl uses matrix multiplication as its mining primitive, the same operation that sits at the core of AI inference. When miners handle compatible inference requests, the same computation can both generate mining attempts and produce outputs for customers. Miners can also run arbitrary matrix multiplications solely to compete for block rewards. The protocol checks whether the required computation was executed correctly, rather than whether a matrix came from a real inference request. Under that setup, miners serving real inference demand may be able to earn both customer fees and mining rewards from the same underlying work, giving them a stronger economic model than miners running only synthetic computation.

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