News linked to both this project and an event.
analyst KawzInvests stated that Moonshot AI's upcoming Kimi K3 could become a significant event in the open-source AI space, and the infrastructure demand behind it may drive growth for AI cloud service platforms. Kimi K3 has approximately 2.8 trillion parameters, making it one of the ultra-large-scale open-source models. According to Moonshot's official evaluation, the model's performance is only slightly behind frontier models like Claude Fable 5 and GPT 5.6 Sol, and the full model weights are expected to be released on July 27.KawzInvests pointed out that a model of this scale cannot run on an ordinary laptop or even a single server; users need a computing cluster composed of a large number of GPUs to complete model loading and inference. When top-tier open-source models are made available for free, the biggest beneficiaries might not be ordinary users, but rather platforms that offer model hosting and inference services. For example, $DOCN (DigitalOcean) already supports serverless inference services for models like Kimi K2.6. Developers do not need to deploy hardware; they can call the model via API and pay per Token. Additionally, the platform hosts over 70 models and covers GPU leasing, model fine-tuning, and AI Agent development tools.As more large-scale open-source models are released, developers' demand for low-barrier AI infrastructure will continue to increase. Model hosting, inference services, and GPU cloud platforms may become key beneficiaries in the open-source AI wave.
According to BitcoinTreasuries data, South Korean listed company K Wave Media (KWM) has sold all of its remaining 88 BTC to repay $6 million in debt. Following the sale, the company's Bitcoin holdings have dropped to zero, exiting the ranks of Bitcoin treasury companies.K Wave Media announced last year that it had secured a $1 billion Bitcoin treasury financing capacity and planned to expand its Bitcoin holdings to 10,000 BTC as soon as possible. However, in May this year, the company redirected up to $485 million of its remaining financing capacity from the Bitcoin treasury strategy to AI infrastructure construction, including data centers, GPU computing power, and related acquisitions.
: In response to the community debate over "Micron VS Nvidia," Jukan, an analyst at Citrini Research, posted on platform X, stating, "MU (Micron) may not be Nvidia, but its future importance could surpass Nvidia. Inference is now directly tied to revenue, but improvements in inference performance cannot be achieved simply by adding more Nvidia GPUs. In inference, GPUs are often underutilized and idle due to memory bottlenecks. For inference, increasing memory provides higher value. The return on investment for inference ultimately depends on memory, not GPU. Therefore, why are people still fixated on obtaining Micron through Nvidia's framework? One must think more comprehensively. Inference is memory."
Odaily News Alex Svanevik, CEO of on-chain analytics platform Nansen, stated that when enterprises begin effectively using Chinese large language models, the bubble in the AI industry may burst. While the U.S. regulatory environment could limit this process, the overall trend remains that Chinese models are continuously becoming more efficient, capable of running on non-cutting-edge hardware, while global GPU supply (including non-Nvidia chips) is increasing.Alex Svanevik also pointed out that the recent decline in H100 and H200 GPU rental prices reflects a shift in the supply-demand structure of computing power. He raised the question of how to interpret the market signal of declining GPU rental prices. As model efficiency improves alongside expanding computing power supply, the AI infrastructure market may be entering a phase of repricing.
sources familiar with the matter have revealed that Goldman Sachs and JPMorgan are exploring trading methods based on the cost of computing power, including futures contracts linked to GPU rental prices. As one of the scarcest resources amid the AI boom, related futures for GPUs are expected to be listed on exchanges later this year.Industry insiders stated that this move reflects how the influx of hundreds of billions of dollars into data centers and the chip sector is reshaping the financial market landscape. For banks financing the construction of AI infrastructure, such innovative instruments could become a new means of risk management. (The Information)
"New Stock God" Serenity posted on X platform, reminding investors to pay attention to financing structures and the dynamics of outstanding shares, as these are crucial for investment returns, and provided examples:IREN: The financing method approaches infinite dilution, with each rebound met by selling pressure—essentially a "bad stock."NBIS: Up 153% year-to-date, thanks to an optimized financing structure (such as direct offerings, convertible bond combinations, etc.).CRWV: High debt interest; the company uses usurious loans for GPU financing, which erodes free cash flow over the long term.Serenity pointed out that if a company has strong fundamentals, one could consider going long after the original shareholding has been diluted to near zero. However, for equity value appreciation, one should stay away from companies with "toxic" financing structures or crushing debt. The risk is especially high for small-cap companies, such as $SLNH adding a $500 million ATM while its market cap is only $250 million; $BKKT continuously diluting stock for executive compensation. Essentially, these companies are transferring investor funds to the enterprise, masked by media hype or influencer promotion.Serenity emphasized that investors must carefully analyze equity structure, dilution risk, and hidden costs when screening targets, to avoid focusing solely on profits while seeing their actual equity shrink.
According to Citrini analyst Jukan, FundaAI's latest report reveals that NVIDIA's NVL576 passive co-packaged optics technology nearly doubles the density of optical engines and optical components. The passive co-packaged optics technology increases the configuration volume of 3.2T optical engines per GPU from approximately 2.25 to around 4.0, an increase of 78%. It is estimated that the demand for Rubin Ultra optical engines will reach about 12 million units.This news may directly benefit direct suppliers of optical engines, including Lumentum, Coherent, POET, among others.
AethirClaw has officially launched CARA (Pre-configured Crypto AI Agent), running on Aethir's decentralized GPU infrastructure. Equipped with over 50 skills, it covers core crypto scenarios such as real-time market monitoring, whale wallet tracking, on-chain analysis, social media sentiment monitoring, and project due diligence, and users can use it out-of-the-box without any technical configuration.The platform supports payments via credit card as well as USDT, USDC, and ATH tokens. Aethir also disclosed that it will soon launch a Model-as-a-Service (MaaS) layer, running mainstream open-source large models on Aethir's decentralized GPU infrastructure, and expand multimodal capabilities including text-to-image and video generation.