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Flash

first_img SemiAnalysis: HBF non-HBM alternative, cost and heat dissipation still have uncertainties

P Equity Research and SemiAnalysis researcher Nick Doyle and others discussed high bandwidth flash (HBF) in X Space. Nick stated that it is still too early to determine how much the cost premium of HBF relative to HBM can shrink; existing data mostly comes from vendor claims, such as Sandisk stating that the cost per bit is about one-eighth that of HBM. Yields, testing, and other factors will improve with scale, but structural costs such as TSV, stacking, and pSLC mode will always exist, and durability is a key unknown; if wear exceeds expectations, costs will rise.The application scenarios for HBF are narrow, targeting only AI inference, especially low batch and long context MoE models, and it is not a substitute for HBM. The actual bandwidth target is about 1.6 TB/s, which is at the HBM3E level, suitable for sequential reads to load model weights, more aligned with the capacity needs of a small number of GPUs in local or private enterprises, rather than ultra-large-scale bandwidth scenarios. Heat dissipation reliability has not yet been resolved; flash memory will degrade faster at high temperatures next to GPUs, and mitigation measures such as UCIe separation and daily refresh have yet to be validated.In terms of manufacturing, Sandisk/Kioxia has experience with 3D NAND, and SK Hynix complements HBM-style stacking capabilities, but mass production is still to be confirmed. Overall, storage is shifting towards a specialized layered market, with NAND shortages expected to continue until 2028, and HBF may further impact supply and demand.

The Ethena Foundation announced four major adjustments to the ecosystem: repurchasing ENA and canceling monthly VC unlocks

According to official news, the Ethena Foundation announced four adjustments to the Ethena ecosystem, including repurchasing locked tokens held by early investors, further aligning the value of tokens with equity, launching a governance proposal for income to repurchase ENA, and canceling future monthly unlocks for VC investors.The Ethena Foundation stated that it has completed the acquisition of all locked ENA tokens from some major seed round investors who had sold ENA in the past 9 months. Regarding the alignment of token and equity value, the Ethena Foundation and Ethena Labs have reached a "Master Framework Agreement," which stipulates that the intellectual property and value generated by the agreement will exclusively belong to the foundation and be governed by ENA holders, while equity investors in Labs entities will no longer enjoy residual cash flow.In addition, the governance proposal for income to repurchase ENA has been launched. According to the proposal, the net income generated by all business lines under the Ethena brand will be used for programmatic repurchase of ENA, and the proposal has been approved by the Risk Committee. The Ethena Foundation also stated that it has reached an agreement with major investors to eliminate the selling pressure caused by future monthly unlocks for VC investors by releasing unallocated tokens. Team tokens will still remain locked according to the original allocation plan.

first_img Jensen Huang defends Nvidia's AI ecosystem financing, stating that the risks are relatively low

NVIDIA CEO Jensen Huang defended the company's increasingly expanding role in financing the AI ecosystem on CNBC's "Mad Money," calling the investment in cutting-edge labs a "once-in-a-generation" opportunity and stating that the risks are relatively low. He pointed out that the outside world overlooks a key point: these are the first batch of startups that require hundreds of billions of dollars in funding, with a very high capital intensity for building and deploying AI. NVIDIA has invested in model companies like OpenAI and Anthropic, as well as new cloud service providers, and has provided financial support for data center projects, including $105 billion in support for a large computing power park in Ohio (with OpenAI as a tenant), and has collaborated with Wall Street institutions to arrange up to $500 billion in data center financing.In response to criticisms of "circular financing" and comparisons to similar internet bubbles, Huang stated that NVIDIA hopes to become an equity investor in cutting-edge AI labs and provide broader support, as these companies do not yet have investment-grade qualifications and low-cost financing records. He emphasized that the invested capital will yield substantial returns and that the risks are low because the computing infrastructure can be redeployed to other clients and workloads when the supported companies encounter difficulties. NVIDIA just announced better-than-expected results for the second quarter of fiscal year 2027: revenue of $96.2 billion, more than doubling year-on-year; data center revenue increased by 117% to $89 billion, and it is expected that revenue will grow by about 70% in fiscal year 2028. Following the announcement, the stock price rose about 4% in after-hours trading.

first_img Tomasz Tunguz: AI infrastructure exhibits a long tail effect, with bottlenecks gradually transmitting and driving up costs

Venture capitalist Tomasz Tunguz pointed out that the narrative of AI infrastructure resembles a slow relay race, with bottlenecks sequentially transmitting from GPUs to memory, CPUs, and storage, each link freezing the supply chain of the next for years and locking in higher baseline costs. At the beginning of 2023, the GPU shock caused H100 rental prices to exceed $9 per hour, and server shipments fell by 22%; subsequently, manufacturers shifted capacity to HBM, leading to an 80% quarterly increase in enterprise SSD prices and over a 60% rise in DRAM.By the end of 2025, the workload of intelligent agents will push the CPU to GPU ratio to about 1:1, with the average price of server CPUs rising by 27% year-on-year; in 2026, nearline HDD annual capacity will be sold out. The construction cost of data centers has risen to about $20 billion per gigawatt, with orders for long-cycle equipment such as transformers and turbines scheduled as far out as 2029 to 2031.Tunguz referred to this as the long whip effect in the hardware sector: years of manufacturing delays amplify downstream demand shocks upstream, and when pressure is relieved at a certain bottleneck, it will be delayed in transmitting to the next link, with transformers scheduled for delivery in 2027 to 2028, NAND wafer fabs, and turbine production lines potentially facing the risk of overcapacity.
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