Back to News
SourceBlockbeats

Citrini观点:看多AMD看空NVDA,编码AI从软件侧侵蚀英伟达壁垒,CUDA护城河正在终结

BlockBeats 消息,7 月 24 日,Citrini 分析师 Jukan 引用 DeepSeek 创始人梁文锋近期核心观点指出,AI 驱动的代码生成与 TileLang 等高级编程语言正迅速降低 CUDA 生态的进入壁垒。DeepSeek 虽使用英伟达 GPU 训练 V3 模型,但已通过自研编译器和 TileLang 环境大幅降低对英伟达软件生态的依赖。此前梁文锋预计,若将 TileLang 与 DeepSeek 编译器移植至华为芯片,中国芯片的生态问题大约一年内即可基本解决,真正的瓶颈只剩产能。梁文锋量化了中美芯片差距——硬件效率约 4 倍、时间滞后约两年——并透露 DeepSeek 正与华为紧密合作,预计将获得约 1.6 万颗华为 AI 芯片,华为 950 SuperNode 可替代 GB200/GB300 的工作负载。分析师 Jukan 将此定性为「CUDA 的护城河正在终结」,对英伟达极为看空。 Jukan 补充称,这一逻辑恰恰是看多 AMD 的原因之一——编码 AI 的进步同样将自然加速 ROCm 生态的发展,帮助缩小其与 CUDA 的差距。AMD 近期投资 Anthropic 时已宣布将积极使用 Claude Code 进行芯片设计与软件工程。综合来看 AI 编程工具的进步正在从软件侧系统性侵蚀英伟达的竞争壁垒,中国芯片生态问题将因代码生成能力快速解决,AMD 则因 ROCm 加速受益,英伟达赖以维系开发者黏性的 CUDA 护城河正面临双重夹击,而硬件效率和产能的追赶只是时间问题。
Disclaimer: The views above are the author's only and do not represent 711BTC. Nothing here constitutes investment advice.

Related

07-06 13:31

Citrini analyst Jukan: Samsung Foundry achieved monthly profitability in June, its first since 2023.

According to a post on the X platform by Citrini analyst Jukan, as reported by Odaily Korean media, Samsung Foundry achieved monthly profitability in June, its first such month since 2023. With this positive profit in June, Samsung internally believes the likelihood of a profit turnaround starting in the third quarter has increased. The use of Samsung's 4nm process for HBM4 base dies is considered a key factor driving this turnaround, while improved yield rates are also seen as a major factor supporting the positive monthly profit. Industry sources estimate that Samsung's 4nm process yield has improved to approximately 80%.

07-02 06:35

Citrini analyst Jukan: MediaTek reportedly has secured Meta as an ASIC customer, following its TPU offerings.

According to a post on the X platform by Citrini analyst Jukan, Odaily Odaily, supply chain sources indicate that MediaTek has essentially secured a second ASIC customer besides Google. If all goes as planned, this customer will be Meta, previously rumored. MediaTek, as is customary, does not publicly comment on individual products, customer situations, or market rumors. While Qualcomm currently appears to have key customers such as Meta, Microsoft, and ByteDance, industry insiders believe that MediaTek's continued deepening of its cooperation with Google and its potential to secure a second customer puts it at a disadvantage. Semiconductor supply chain sources state that, considering the order prospects and generational transition pace of major cloud service providers for AI data centers and ASIC products, Google remains the most aggressive and proactive major customer. MediaTek not only has two products codenamed Zebrafish and Humufish, but according to market information and confirmation from industry insiders familiar with ASICs, its participation in the v9 generation Triggerfish is almost certain. This means that from the end of 2026 to 2028, and even into 2029, MediaTek can reliably receive revenue contributions from TPU mass production. Compared to Qualcomm's $15 billion cloud AI revenue target for 2029, it's only a matter of time before MediaTek, holding multiple TPU ASIC orders, reaches the $10 billion level. The industry is also focused on whether MediaTek can successfully secure its second major cloud service provider client. Based on earlier market information and recent supply chain confirmations, MediaTek is still actively collaborating with Meta on ASIC products, likely focusing on AI acceleration chips. Meta recently partnered with Arm and Qualcomm, but the products are all CPU-oriented, with no further concrete news regarding its self-developed AI acceleration chips. IC design industry insiders say that Meta's recent cloud AI development strategy has indeed been rather chaotic and unclear, with multiple adjustments to its internal chip development plans, seeking multiple partners and adopting different solutions in the CPU field alone. Regarding AI acceleration chips, although Meta has officially announced its collaboration with Broadcom, supply chain sources indicate that this has not interrupted the ongoing collaboration between MediaTek and Meta. IC design industry professionals also emphasized that MediaTek and Qualcomm's cloud AI development blueprints are still significantly different. MediaTek is concentrating all its resources on ASIC business, while Qualcomm plans to simultaneously promote customization and standardization, while also covering AI acceleration chips and CPU products.

06-21 16:20

Citrini researcher Jukan: Citibank report makes conservative assumptions; Micron Technology (MU)'s performance this quarter may exceed expectations.

According to Odaily Odaily, Jukan, a researcher at the well-known investment research firm Citrini, published an article on the X platform stating that after reading Citibank's earnings preview report, he believes that the institution's assumptions regarding Micron Technology's product pricing are too conservative. If the market consensus is at that level, Micron Technology may have exceeded expectations this quarter. Data shows that Micron Technology (MU) stock is currently trading at $1133.99. Previously, several institutions raised their target price for Micron Technology (MU) to between $1200 and $1500, among which: Citigroup raised its price target for Micron Technology to $1,200; Wedbush raised its price target for Micron Technology to $1,300. Deutsche Bank and investment bank Stifel have raised their target price for Micron to $1,500.

06-16 10:49

Citrini: AMD and Apple are simultaneously pushing forward with flash memory solutions to replace DRAM in AI products.

According to Mars Finance, on June 16th, Citrini Research, the organization behind the "AI Doomsday Report," published an article stating that AMD and Apple are simultaneously advancing solutions to partially replace DRAM with flash memory in their AI products. AMD acquired MEXT to optimize flash memory, bringing its performance close to DRAM, thereby reducing memory costs in AI data centers; Apple, on the other hand, achieves similar optimizations on the device side through its "LLM in a flash" technology. Citrini's latest research report highlights the high memory demands of key-value caching in AI inference, and the "memory tax" pressure from HBM (Hardware Bus) already accounting for 25% of DRAM capacity. Flash memory, costing only 1/55th that of DRAM, can provide a capacity and bandwidth alternative for edge AI through controller optimization, NAND stacking, and cell mode adjustments. This report provides theoretical support for the recent surge in memory stocks, particularly SanDisk.

07-05 17:08

Citrini Analyst: China's CXMT tests pilot production line for bonding DRAM; South Korean media claims its technology and development speed may be ahead of its South Korean rivals.

According to a Odaily by Citrini analyst Jukan on the X platform, South Korean media reports that China's CXMT is currently testing a bonded DRAM pilot production line in Hefei, aiming to achieve high-performance DRAM without using EUV lithography. Bonded DRAM is a technology that manufactures the memory cell array and peripheral circuitry on separate wafers and then bonds them together. This method can produce ultra-high-density DRAM using only multi-patterned deep ultraviolet (DUV) lithography, without requiring EUV equipment. Samsung Electronics is developing its own bonded DRAM under Project B1b, and SK hynix is ​​also advancing similar technology. South Korean media warns that assessments suggest CXMT may currently be ahead of its South Korean competitors in terms of both the technology itself and the speed of its development.

06-29 14:29

Micron (MU) is more important than Nvidia! Citrini analysts say AI model inference performance relies more on memory than GPU.

According to Odaily Odaily, in response to the "Micron vs. Nvidia" debate discussed in the community, Jukan, an analyst at Citrini Research, posted on the X platform, stating, "While Micron is not Nvidia, its future importance may surpass Nvidia's. Inference is now directly linked to revenue, but performance improvements are not solely achieved by adding Nvidia GPUs. GPUs often remain idle with low utilization due to memory bottlenecks during inference. For inference, increasing memory is more valuable. The ROI of inference ultimately depends on memory, not GPUs. Therefore, why are people still focused on acquiring Micron through NVIDIA's framework? A more comprehensive approach is needed. Inference is memory."