gpu

Luke Dashjr has randomly determined that BLAKE2b will be the new POW algorithm for the BIP-110 chain, expected to replace the currently used SHA-256d algorithm in Bitcoin. Compared to SHA-256d, BLAKE2b is more suitable for mining with general-purpose CPUs/GPUs, being more CPU-friendly, which can reduce the advantages of existing Bitcoin ASIC miners.Previously, it was reported that Luke Dashjr plans to officially implement the BIP-110 hard fork on September 1, making it an independent blockchain and generating a new token distinct from Bitcoin. Luke Dashjr has had his Bitcoin BIP editing permissions revoked by the Bitcoin BIP editing team.

Lambda, an AI cloud computing service provider supported by Nvidia, is financing $917 million through the leveraged loan market to procure AI chips. As the construction of artificial intelligence infrastructure accelerates, chip financing is becoming a new way for capital investment in the AI industry. Lambda belongs to the rapidly developing "new cloud vendor" camp in recent years, with its main business being to provide GPU computing power and AI infrastructure services to enterprises and developers.This financing plan will be completed through a loan based on GPU asset-related rights, aimed at supporting the company's expansion of AI computing resources. Reports indicate that AI infrastructure companies are actively exploring new financing methods to meet the enormous capital investment required for building large-scale computing clusters. Previously, AI cloud service provider CoreWeave completed the first transaction in the institutional leveraged loan market for chip financing, providing a new financing model for the industry. As the demand for generative AI continues to grow, Nvidia's GPU supply has become a core resource for AI companies' expansion. By using GPU assets as the basis for financing, AI cloud service providers can rapidly scale their computing power without fully relying on equity financing, while also allowing the traditional credit market to participate in the wave of AI infrastructure investment.

The domestic GPU manufacturer Moore Threads released its semi-annual report for 2026, with revenue of 1.736 billion yuan, a year-on-year increase of 147.42%. The net profit attributable to the parent company was a loss of 11.56 million yuan, narrowing by about 259 million yuan compared to the same period last year; the net profit attributable to the parent company after deducting non-recurring gains and losses was a loss of 151 million yuan, narrowing by 52% year-on-year. Revenue in the second quarter was approximately 999 million yuan, a quarter-on-quarter increase of 35.4%. Research and development expenses were 769 million yuan, accounting for 44.3% of revenue. The company stated that the improvement in performance was driven by the demand for AI computing power and the accelerated commercialization of the Kuage Intelligent Computing Cluster.As of the end of the quarter, the book value of inventory was 3.55 billion yuan, an increase of 166.5% compared to the end of last year, reaching a four-year high. The company stated that this was due to proactively increasing stock to meet market demand. On the same day, Moore Threads announced plans to issue H shares and list on the main board of the Hong Kong Stock Exchange, just about 8 months away from its listing on the Sci-Tech Innovation Board in December 2025. If successful, it will achieve an "A+H" layout. The announcement stated that this issuance still requires approval from the shareholders' meeting and regulatory approval, which carries uncertainty. Another domestic GPU manufacturer, Muxi Co., has already launched its H share plan in June, with a shorter interval. Moore Threads is accelerating the development of the new generation "Huagang" architecture and the Huashan and Lushan chips. In the first half of the year, the net cash outflow from operating activities was 2.169 billion yuan, an increase of about 86% year-on-year.

According to the latest supply chain survey by TrendForce, despite the United States implementing export controls on high-end GPUs, China's AI industry has not slowed down; instead, it is accelerating the construction of a domestic AI supply chain. The report points out that the supply structure of high-end AI chips in China is shifting from reliance on imported GPUs to a dual-track model of "domestic GPUs plus self-developed ASICs," with domestic solutions expected to capture nearly 90% of the Chinese high-end AI chip market by 2026.This year, the Chinese government is actively promoting the adoption of domestic AI chips, with policies prioritizing support for high-potential local manufacturers to significantly expand their market share in the high-end AI server market. Meanwhile, the domestic ecosystem is gradually maturing in key areas such as advanced processes, advanced packaging, and thermal management. TrendForce expects that, against the backdrop of an accelerated domestic substitution process, domestic chip manufacturers like Huawei Ascend, Cambricon, and Haiguang are likely to continue benefiting, and the related industrial chain will usher in structural growth opportunities.

On July 29, the U.S. Department of Commerce signed letters of intent with seven companies, totaling up to $874 million, to support seven "post-GPU era" underlying technology routes such as CPO, ferroelectric memory, and 3D packaging in the form of equity investments. This marks a shift in the U.S. chip strategy from "capacity reshoring" to "technology route selection."The seven companies and their technology directions include: GlobalFoundries (CPO silicon photonic integration, $300 million), Kepler Computing (ferroelectric 3D memory, $245 million), Multibeam (multi-electron beam direct-write lithography and advanced packaging, $140 million), Extropic (thermodynamic sampling unit TSU, $75 million), Thintronics (ultra-low loss dielectric materials, $50 million), Aeluma (large-size phosphorus-free optoelectronic device substrates, $30 million), and OBSIDIA (hardware zero-trust chip anti-counterfeiting, $34 million). All companies are required to provide non-controlling minority equity to the U.S. government.This move shows that the funding usage of the CHIPS Act is shifting from subsidizing wafer fabs to directly holding equity in cutting-edge technology companies with national capital, in order to secure rule-making authority in the post-Moore era.

According to The Information, Nvidia is weighing a significant adjustment measure, considering using less high-bandwidth memory for the Rubin Ultra GPU to address the shortage of advanced high-bandwidth memory chips.

According to Jinshi reports, DDN has partnered with NVIDIA (NVDA.O) to advance GPU-driven data access technology.

According to SemiAnalysis, AMD reached a performance-based warrant agreement with Meta and OpenAI from the end of last year to the beginning of this year. The warrants will vest in batches based on the two companies' procurement and deployment of up to 6GW of AMD Instinct GPUs, while AMD's stock price must exceed a tiered target (up to $600).As AMD's stock price has risen from around $200 in early 2026 to the current $500-600, SemiAnalysis estimates that the GPU capital costs for Meta and OpenAI have decreased from about $2.15/hour and $2.50/hour to approximately $1.60/hour and $1.75/hour, respectively. If AMD's stock price rises to $600-700, the capital cost of the GPUs themselves will approach zero; if it further exceeds $1000, the total capital cost of the entire cluster (including servers, networks, and CPUs) will effectively be zero for both companies. This analysis is based on the assumption of linear vesting and the premise that the entire 6GW capacity is deployed, and the specific terms of the warrants have not been disclosed.

According to BBX data, yesterday global listed companies disclosed the latest real announcements regarding adjustments in cryptocurrency assets and business payment applications, with the following core dynamics:Hyperscale Data monetizes 100 BTC to build an AI data center: U.S. listed company Hyperscale Data, Inc. (NYSE American: $GPUS) announced the sale of 100 bitcoins to fund the construction of its AI data center located in Michigan. The company's CEO William Horne stated that this move is "converting one balance sheet asset into another," and clarified that the company's underlying belief in bitcoin has not changed.Quantum Solutions sells 1,000 ETH to inject into AIDC business: Japanese listed company Quantum Solutions announced that it sold 1,000 ETH through its merged subsidiary GPT Pals Studio Limited (GPT) for $1.9 million (approximately 311 million yen). The proceeds from the sale will be fully invested in the expansion of artificial intelligence infrastructure (AIDC) and data center business.Vida Global seamlessly accesses bitcoin payroll through the Lightning Network: U.S. listed company Vida Global (NYSE American: $VIDA) announced the adoption of Voltage Credit to pay its global team members in bitcoin via the Lightning Network. This mechanism utilizes a revolving credit line to instantly issue bitcoin and repays the balance in U.S. dollars at the end of each month, similar to processing standard vendor invoices, allowing the company to establish a cryptocurrency payroll settlement channel without directly holding a bitcoin wallet or reflecting cryptocurrency assets on its balance sheet.

Fully Homomorphic Encryption (FHE) company Zama CEO Rand Hindi stated on X on Monday that the company has achieved benchmark results of 1,000 confidential transactions per second on GPUs, an increase of about 5,000 times compared to the 0.2 transactions per second demonstrated at ETHcc in 2022. Hindi mentioned that the cost of running an FHE node is approximately $0.000004 per ERC7984 transaction, significantly lower than zero-knowledge proofs, but this comparison has not been independently verified. The benchmark has not yet been activated on any mainnet, is self-reported data from the company, and the testing methods have not been published, with plans to go live by the end of the year.Hindi also set long-term goals: 10,000 tps by 2028 and 100,000 tps by 2030. Zama has been active recently: on July 24, a confidential RFQ exchange product was launched, which within weeks shielded over $121 million USDT on Ethereum, collaborated with Elliptic to enhance compliance screening for confidential transactions, and co-founded the Confidential Token Association with OpenZeppelin and Inco to standardize encrypted ERC-20 tokens. Zama previously completed a $57 million Series B funding round.

According to a report by the WSJ, Nvidia has invested in the AI laboratory Safe Superintelligence (SSI), founded by former OpenAI chief scientist Ilya Sutskever. The two parties have reached a long-term cooperation agreement aimed at expanding SSI's computing resources while helping Nvidia secure important clients in the AI field. Both companies stated that Nvidia made a "large-scale" investment after understanding some of SSI's research progress, but did not disclose the specific amount.As part of the collaboration, SSI will receive a significant amount of Nvidia's flagship GPU resources, with its computing power expected to increase by an order of magnitude. Previously, SSI primarily relied on TPU chips provided by Google for AI research and development. This collaboration indicates that Nvidia is further expanding its AI chip ecosystem and binding future computing power demands through investments in top AI laboratories.SSI was established in 2024 by Ilya Sutskever, with the goal of developing "Safe Superintelligence." The company has previously raised about $2 billion in funding, with investors including Andreessen Horowitz and Sequoia Capital, and reached a valuation of approximately $30 billion last year. This is not Nvidia's first investment in an AI company founded by former core members of OpenAI. In March of this year, Nvidia also invested in Thinking Machines Lab, founded by former OpenAI chief technology officer Mira Murati, whose first AI model is trained on Nvidia hardware.Ilya Sutskever is one of the important researchers in the field of modern artificial intelligence, having co-founded OpenAI and promoted the development of large model technologies such as ChatGPT. However, after leaving OpenAI, he began to question the approach of solely relying on expanding data and computing power to drive AI progress, turning instead to explore new directions in superintelligence research.

Libbitcoin developer @evoskuil stated that the latest version of Libbitcoin Server can complete the initial block download of a Bitcoin full node in 55 minutes and perform full verification. This speed improvement comes from a new GPU-based signature verification system that runs in parallel with conventional script verification, rather than as a separate step.The software does not pause execution to verify each ECDSA and Schnorr signature; instead, it captures signature checks, batches them, and processes them via GPU while the remaining verification continues. @evoskuil indicated that this method achieves about a 99.5% signature capture rate without blocking verification, thus eliminating the need for a second verification. If a batch signature check fails, the software traces it back to the corresponding block and marks that block as invalid. He also mentioned that this design can batch process an almost arbitrary number of signatures, keeping the GPU ahead of the remaining verification process and significantly shortening the full node synchronization time.

According to official news, Kimi stated that in the past 48 hours, the demand for Kimi K3 has exceeded expectations, and GPU resources are nearing the current capacity limit. To ensure the experience of existing subscription users, the platform has temporarily suspended new subscriptions, and current subscribed users will not be affected. Kimi mentioned that it is working to expand computing power as soon as possible and will gradually open new subscription slots in batches.Meanwhile, the platform will split the membership system into two types of plans in the future: one for Kimi members on the Kimi web portal, App, and Work, and the other for Kimi Code members focused on programming workflows, to more accurately match computing resources and maintain service stability.

According to The Block, Bernstein pointed out in its latest research report that the perpetual contract structure originating from the cryptocurrency market has first entered the AI computing power market. The Architect AX exchange, registered in Bermuda and not regulated by the CFTC, has launched GPU computing power perpetual contracts, which are anchored to spot prices through funding rates, a mechanism that directly originates from the cryptocurrency market. The cash-settled computing power futures from CME and ICE are still awaiting CFTC review, with a launch expected by the end of 2026.In addition, the CFTC-regulated prediction market platform Kalshi has previously launched prediction contracts linked to GPU rental prices and introduced the computing power pricing curves for B200, H200, and A100 chips on July 14, with the B200 quoted at $5.41 and a historical peak of $7.39.Bernstein noted that unused GPU computing power cannot be stored, and hedging is the only way to bear price risk. However, the current market liquidity is still dominated by speculative funds, and the difficulty in constructing computing power benchmark indices is also a major bottleneck in market development.

According to BBX data, yesterday the global US stock market and companies listed on the Tokyo Stock Exchange made significant moves in the accumulation of digital asset reserves. The latest updates from two core companies are as follows:GPUS core treasury breaks the 1,000 mark: Artificial intelligence data center company Hyperscale Data, Inc. (NYSE American: $GPUS) officially announced that the number of bitcoins held in its treasury has officially exceeded 1,000. Management stated that the company will continue to closely align with market conditions and existing capital allocation opportunities, supported by its core data center business, to continuously manage and expand its bitcoin reserve scale.Metaplanet raises $137 million through targeted private placement: Metaplanet Inc. (TSE: $3350) announced that it will issue new shares to specific institutional investors through the Third-Party Allotment mechanism under Japanese securities law, aiming to raise approximately $137 million (equivalent to 21.4 billion yen), with all funds to be used for purchasing bitcoin. The company accumulated 2,823 BTC in Q2 this year, and as of June 30, its total holdings have reached 43,000 BTC (valued at approximately $2.67 billion). Based on the current market price of about $62,000 per coin, this lightning private placement is expected to add approximately 2,210 BTC to its reserves, helping it strive towards its ultimate goal of 100,000 BTC by the end of the year.

According to reports from Jiemian News, Kingsoft Cloud will accelerate the construction of GPU computing clusters in the second half of the year to meet the explosive growth in computing power demand from major clients. Among them, Xiaomi's demand for Kingsoft Cloud's GPU computing power has upgraded from a ten-thousand-card cluster, with the related budget significantly increasing from nearly 4 billion yuan to over 10 billion yuan. In addition, Alibaba's large model team has signed a 5-year computing power leasing contract with Kingsoft Cloud, involving more than 3,000 eight-card GPU servers. Based on the contracted price, the annualized revenue after full delivery will exceed 4 billion yuan.To meet the surging customer demand, Kingsoft Cloud's capital expenditure plan for 2026 has been raised to 15 billion yuan, with an annual revenue target of 12.5 billion to 13.5 billion yuan. It is reported that due to tight upstream supply, Kingsoft Cloud is currently only accepting long-term contracts from clients for 3 to 5 years, and some orders are facing delivery delays. Due to concerns about the risk of impairment of high-level card assets, Kingsoft Cloud is currently pausing aggressive hardware expansion, anticipating that the prices of computing hardware may reach a turning point for decline in the third quarter of this year.

According to a report by the Financial Associated Press, Nvidia announced a new AI infrastructure collaboration model on July 1 local time. Through revenue-sharing and credit-support mechanisms, it will jointly build large-scale, multi-tenant AI factories with AI cloud service providers, helping startups, model developers, enterprises, and research institutions to access AI computing power more quickly, while creating a sustainable revenue stream for Nvidia linked to computing power usage.According to the plan, AI cloud vendors will deploy AI factories based on the Nvidia DSX AI Factory architecture to provide cloud computing services. The first batch of partners includes Sharon AI and Firmus. Among them, Sharon AI plans to deploy up to 40,000 NVIDIA Grace Blackwell GB300 GPUs; Firmus will build a DSX AI factory park on Batam Island, Indonesia, with plans to expand to a power scale of 360 megawatts and deploy up to 170,000 Nvidia GPUs.