Что такое Reported Hashrate

Crypto.ruPublicado em 2022-11-18Última atualização em 2022-11-18

Resumo

Хешрейт — показатель вычислительной мощности майнингового оборудования. Этот параметр отображает число операций в секунду, которые производит компьютерная техника. Показатель выражается в H/s и высших мерах исчисления. Майнеры криптовалют нередко отслеживают разные параметры. Одна из характеристик — Reported Hashrate. Эта фраза переводится с английского языка как «заявленный хешрейт». Значение параметра, как правило, отличается от фактической мощности используемого оборудования. Новички редко понимают, почему так происходит.

Отличия заявленного хешрейта от фактического и среднего

Существует 3 термина, касающиеся значений вычислительной мощности:

-Current. Это фактическое (подтвержденное) значение. Его вычисляет майнинговый пул. Параметр зависит от количества вычисленных пользователем шар (shares). Показатель влияет на доходность добычи криптовалютных активов. Любой майнинг-пул оплачивает работу участников, учитывая именно предоставленные шары.

-Average. Это среднее значение. Оно вычисляется за конкретный промежуток времени — как правило, за 24 часа. Показатель используется в статистике. Другого применения у него нет.

-Reported. Это заявленная мощность. Значение применяется программным обеспечением (ПО) для фиксации отправленного хешрейта в пул. Параметр используется только для сравнения с подтвержденным значением. Между этими показателями, как правило, есть небольшая разница. Также параметр отображается только в ПО для подключенных воркеров (устройств для криптомайнинга).

Как стабилизировать Reported Hashrate

Разброс характеристик есть всегда. Если отличия не критичны, то не стоит тратить время на стабилизацию показателей. Но иногда фактические данные бывают сильно ниже заявленных. Тогда стабилизация необходима.

Current Hashrate может быть гораздо меньше Reported Hashrate по ряду причин, представленных в таблице.

Оптимальное заявленное значение

В норме Reported превышает Average только на 10-20%. Эта разница считается оптимальной в сообществе криптовалютных майнеров.

Диаграмма с хешрейтом, шарами и воркерами

Почему не следует гнаться за высокими показателями

Для увеличения подтвержденного хешрейта майнеры «разгоняют» свои видеокарты и другую технику. Однако новичкам не стоит делать этого. Чрезмерный или неправильный разгон приводит к увеличению вырабатываемого тепла. В результате майнинг-техника перегревается и выходит из строя без возможности ремонта. Также разгон повышает нагрузки на электронные чипы. Это снижает срок службы техники.

Leituras Relacionadas

Robots Begin to 'Consume Data': The Hidden Production Chain from Indian Data Factories to Billion-Dollar Humanoid Robots

Robots have started to 'consume data,' driving the formation of a new industrial supply chain focused on producing training data for embodied AI. Unlike large language models, which are trained on vast internet text corpora, embodied AI models face a 'data desert' in the physical world. This has created a massive demand for first-person perspective video data (Ego Data), captured by workers wearing cameras in places like Indian garment factories. Companies like Neocambrian AI are establishing 'data factories' where workers perform standardized tasks (e.g., sorting clothes, kitchen organization) to generate thousands of hours of video. Research, such as NVIDIA's EgoScale, demonstrates that scaling this human demonstration data predictably improves robot performance, particularly for dexterous manipulation. This has validated a training path combining large-scale human data for pre-training with smaller amounts of robot-specific data for fine-tuning. The value of different data types varies significantly, forming a 'data pyramid.' The base consists of low-cost, large-scale internet and Ego Data. Higher layers include more expensive motion-capture data (e.g., from data gloves), simulation/synthetic data, and the most costly and scarce layer: real robot teleoperation data. This demand has spawned a layered ecosystem of data suppliers: low-cost data factories, motion capture and alignment specialists, robot-native teleoperation service providers, simulation data companies, and platforms aiming for data standardization. Robot companies themselves are adopting a 'layered procurement' strategy: outsourcing generic Ego Data while building in-house capabilities for robot-specific adaptation data and the critical deployment/failure data generated in real-world applications. The industry is shifting focus from hardware and basic mobility to the data pipelines required for general-purpose capability. While parallels exist to data labeling companies like Scale AI in the LLM boom, the physical complexity of robot data—involving action success ambiguity and sim-to-real gaps—requires more integrated solutions for data collection, annotation, and a continuous feedback loop. The race is on to build the data engines that will teach robots to operate reliably in the unstructured real world.

marsbitHá 44m

Robots Begin to 'Consume Data': The Hidden Production Chain from Indian Data Factories to Billion-Dollar Humanoid Robots

marsbitHá 44m

Spicy Commentary | Michael Saylor's 'Player Talk'; 60-Year-Old Aunt Liquidated After 'Scamming a Young Man'

**"Spicy Commentary": Three Tales of Crypto's Wild Week** This week's "Spicy Commentary" column highlights three dramatic stories from the cryptocurrency world. First, **MicroStrategy's Michael Saylor** addressed the controversy over his company potentially selling Bitcoin. At the BTC Prague event, he clarified, "I never said the company can't sell Bitcoin. I told *you* never to sell *your* Bitcoin." This "do as I say, not as I do" stance was criticized by netizens as peak linguistic gymnastics, noting a history of him previously stating the company would "never" sell. Second, a **bizarre fraud case** emerged from Beijing. A 60-year-old woman, obsessed with getting rich from crypto but unwilling to risk her own savings, posed online as the 20-something "god-daughter" of a high-ranking official. She catfished a young man, convincing him to give her over 200,000 yuan for fabricated emergencies. She then invested all the stolen money into cryptocurrency with 10x leverage, only to lose everything in a market crash. The woman was sentenced to four years in prison for fraud. Finally, a **sobering trader's tale** surfaced on Reddit. A user posted "Tale of a crypto trader," confessing their net worth had plummeted from a peak of $45 million to roughly $17,200, primarily due to holding meme coins too long. The post, described as a crypto "book of confessions," sparked reactions ranging from sympathy to critique about greed, poor risk management, and the perils of treating meme coins as long-term investments instead of taking profits. The column concludes that this week featured masterful rhetoric, elaborate scams, and extreme financial volatility, stitching together another chapter in crypto's unpredictable theater.

Foresight NewsHá 1h

Spicy Commentary | Michael Saylor's 'Player Talk'; 60-Year-Old Aunt Liquidated After 'Scamming a Young Man'

Foresight NewsHá 1h

Tremble Humans, AI Continues Its Accelerated Sprint

Trembling, Humans: AI Continues Its Accelerated Sprint Yes, AI is still rapidly accelerating. While deep learning seemed to stall quickly in its early years, large models after years of development show no sign of hitting their ceiling. At the Zhiyuan Conference 2026, the focus is on enabling AI to move from the digital world into the physical world. Scaling Law remains effective, continuing to drive advancements in both large language models and multimodal models. The industry is now entering a phase of pursuing World Models, though unresolved technical paths and data issues mean this exploration may take 3-5 more years. Concurrently, breakthroughs in Agents are accelerating AI's real-world application in fields like healthcare and meetings. Making Agents truly useful requires key hardware-software co-design, evident from the strong presence of chip vendors at the conference. We stand at a new historical threshold where AI is becoming a foundational force reshaping the world. The first day of the conference highlighted AI's evolution from "knowing how to chat" to "knowing how to work." Scaling Law persists, World Models are the next key battleground, and Agents are transitioning from usable to好用 (user-friendly). Scaling Law is not ending but diversifying. New models like Anthropic's Fable 5 demonstrate scaling through parameter size, synthetic data, and reinforcement learning. Advancements in AI Coding and Agent deployment are enabling a trend of AI self-evolution, potentially allowing AI to take over digital world iterations. World Models represent the next frontier for large models extending into the physical realm, but no current model is truly impressive at solving real-world problems. Technical consensus is lacking, with debates on data sources (video, simulation, real-world). Different approaches are emerging: language-centric, pixel-centric, 3D-structure-centric, and visual-representation-centric models. Zhiyuan Institute is exploring a fifth path: unified latent space modeling fusing language and visual representations, and introduced its own under-development World Model, Physis-v0.1. On the product side, Agents are key to bringing AI into daily life. Since 2025, the "Year of the Agent," products have become more proactive and capable of complex tasks. Zhiyuan showcased four vertical Agents for cardiac diagnosis, autonomous research, meeting summarization, and protein risk discovery. However, technical challenges remain, particularly in context engineering like memory and orchestration. "Harness" – the engineering framework around an Agent – is crucial for maximizing its capabilities by clarifying intent, designing workflows, and incorporating validation and feedback. In summary, AI's breakneck pace continues on multiple fronts: foundational model scaling, the ambitious pursuit of World Models for physical understanding, and the ongoing refinement of practical Agents. The journey from capable to truly reliable and useful AI systems is well underway.

marsbitHá 1h

Tremble Humans, AI Continues Its Accelerated Sprint

marsbitHá 1h

The Backside of Musk's Trillion-Dollar Fortune: 85% Can't Be Sold

Elon Musk becomes the world's first trillionaire, driven by SpaceX's IPO valuing the company at $1.77 trillion. However, his vast wealth is largely illiquid: he holds over 85% voting control, likely through super-voting shares that are subject to lock-ups and selling restrictions. While his net worth surpasses $1 trillion across SpaceX, Tesla, and private holdings, only a tiny fraction (potentially under 2% annually) could be converted to cash without jeopardizing control and market confidence. SpaceX's IPO also creates paper millionaires for roughly 4,400 employees, but their holdings face lock-up periods, exercise costs, and taxes, delaying and reducing actual cash proceeds. Only 4.2% of total shares are initially available for public trading, making the stock price highly sensitive to limited net buying or selling pressure. A major test will come when lock-ups expire for the remaining 96% of shares. The article contrasts SpaceX's wealth distribution with potential AI IPOs. Anthropic and OpenAI could generate employee wealth pools 20 times larger than SpaceX's in paper value, due to their higher valuations relative to revenue and potentially more distributed ownership. However, sustaining those high price-to-sales multiples post-IPO is uncertain. A key financial puzzle for SpaceX investors is its xAI unit. While it has locked in an estimated $26 billion in annual compute revenue from clients like Anthropic and Google, the unit reported a $6.4 billion loss in 2025. More critically, estimated annual capital expenditures of ~$30.8 billion exceed that revenue. The long-term viability of SpaceX's AI narrative hinges on whether this compute income can eventually cover the unit's massive ongoing investments and losses.

链捕手Há 1h

The Backside of Musk's Trillion-Dollar Fortune: 85% Can't Be Sold

链捕手Há 1h

Ethereum Researchers Propose SPHINCS- Signature Scheme For Post-Quantum Wallets

Ethereum researchers have proposed SPHINCS-, a stateless post-quantum signature verification scheme optimized for the Ethereum Virtual Machine (EVM). Designed to function within the existing EVM without protocol changes or new precompiles, it replaces standard hash functions with EVM-native KECCAK256, enabling a Solidity implementation. The scheme is tailored for wallet use, targeting a more practical signature budget (2^14 to 2^20 signatures per key) rather than the standard astronomical limit, reflecting typical Ethereum address transaction patterns. A key variant, C13, is reported to verify signatures at about 127,000 gas with a 3,704-byte signature. The proposal, credited to researcher nicocsgy with acknowledgments to Vitalik Buterin, is currently a non-standard research concept, not a finished standard. It highlights trade-offs, such as long signing times on certain hardware wallets, but contributes to the broader, essential conversation on preparing Ethereum's account security for a future with quantum computers.

bitcoinistHá 3h

Ethereum Researchers Propose SPHINCS- Signature Scheme For Post-Quantum Wallets

bitcoinistHá 3h

Trading

Spot

Futuros