Toyota Selects Nvidia, Intel Feels Heat

发布时间:2017-05-11 00:00
作者:Ameya360
来源:EE Times
阅读量:1932

  Nvidia revealed Wednesday at its GPU Technology Conference that Toyota will use Nvidia’s Drive PX AI automotive platform to power advanced autonomous driving systems planned for market introduction.

  Mike Demler, a senior analyst at The Linley Group, described Toyota’s move as “potentially a big deal.”

  In the brewing battle between Nvidia’s AI car computing platform and an Intel-Mobileye platform, Nvidia now appears to be building momentum.

  According to Egil Juliussen, director research, Infotainment & ADAS at IHS Automotive, Toyota has become the fourth major car OEM publicly committed to Nvidia’s Drive PX for their highly automated vehicle. The other three OEMs are Audi, Daimler, and VW Group.

  In addition to those OEMs — which include the world’s two biggest carmakers Toyota and VW, Juliussen added that Nvidia also previously picked up smaller OEMs including Volvo, Tesla and Nio (formerly known as NextEV). Since tier ones such as Bosch and ZF have also embraced Nvidia’s hardware platform, Juliussen believes that this “will probably help Nvidia getting other OEMs on board.”

  Demler, who attended Nvidia’s conference Wednesday, also noted that Argo.ai, Ford’s autonomous driving group, gave a presentation on “Deep Learning in Argo.ai’s Autonomous Vehicles.”

  Of course, it’s important to note that the automotive industry is “still in a very early stage of development for Level 4 and Level 5 self-driving cars,” cautioned Demler. It’s premature to declare any platform’s victory. Juliussen noted that “other platforms for autonomous driving are likely to appear.”

  But so far, it’s hard to deny that Nvidia is picking up steam.

  Toyota’s deal

  A year ago, Toyota Research Institute CEO Gill Pratt came to Nvidia’s conference to deliver a keynote speech, in which he emphasized why simulation is the key to autonomous driving. By leveraging Nvidia’s GPU-powered platform and developing simulation programs, Pratt explained that it’s incumbent upon researchers at the Toyota Research Institute to tackle “corner cases” that happen rarely during trillions of miles of driving in the real world.

  Without simulations to augment learning from huge quantities of real-world data, miles of cumulative driving alone won’t help the industry find answers for such edge cases, he explained.

(备注:文章来源于网络,信息仅供参考,不代表本网站观点,如有侵权请联系删除!)

在线留言询价

相关阅读
NVIDIA Reportedly Eyes More Than 15% Price Hikes for Vera Rubin, Grace Blackwell Servers in Early 2027
  NVIDIA is reportedly moving to raise prices across its AI server ecosystem. According to Bloomberg, sources say prices for servers powered by NVIDIA’s AI chips will increase by more than 15% for some of its biggest customers. The hikes will apply to systems shipped early next year, including those featuring NVIDIA’s flagship Vera Rubin and Grace Blackwell chips, with the size of the increase varying by chip generation and memory configuration.  Server manufacturers that build systems under contract for major data center operators, including Microsoft, Alphabet’s Google, and Oracle, have reportedly informed customers of the upcoming increases. The move comes as memory supply remains a key consideration for AI systems. Wccftech notes that analysts believe NVIDIA may have secured multi-year agreements with SK hynix and Micron for HBM and DRAM supplies.  The higher prices are also raising concerns about rising costs for AI data center development and operations worldwide. Chosun Ilbo, citing The Information, notes that the increases could add at least US$5 billion (approximately KRW 7 trillion) to the cost of building a 1-gigawatt (GW) AI data center.  Memory Supply Emerges as Key AI Chip Pricing Factor  Meanwhile, Hankyung notes that higher NVIDIA AI chip prices could weigh on the broader AI data center ecosystem, potentially slowing demand and bringing the rise in memory prices to a halt.  Analysts cited by Hankyung say NVIDIA’s decision to pass higher costs on to customers highlights the growing influence of memory suppliers. With memory supply increasingly affecting both AI chip production volumes and pricing, its role in overall system costs is also expanding. Morgan Stanley notes that GPUs once accounted for more than 80% of AI server costs, but their share has fallen to roughly half that level in next-generation systems as memory-related costs rise rapidly.  According to TrendForce, server DRAM contract prices are expected to rise 13–18% quarter-over-quarter in 3Q26. With the market remaining undersupplied, memory suppliers may continue revising quotations upward throughout the rest of the quarter. Overall, TrendForce expects server DRAM contract prices to continue rising each quarter from the second half of 2026 through the second half of 2027, although the pace of increases is likely to moderate.
2026-08-25 15:11 阅读量:105
NVIDIA Reportedly Halves Vera Rubin SOCAMM Capacity as Memory Costs Near 29% of System BOM
  Rising memory costs are prompting AI hardware vendors to rethink their product strategies. According to Wccftech, citing analysis from GF Securities, NVIDIA is reportedly reducing the memory capacity of its Vera Rubin NVL72 rack-scale AI system to cope with elevated memory prices and ongoing supply shortages. Without these adjustments, memory could account for around 29% of the Vera Rubin VR200 system’s estimated US$2.1 million bill of materials (BOM), well above NVIDIA’s preferred level of 20%.  The cost burden is significant. Wccftech, citing an earlier Bernstein report, notes that a single Vera Rubin NVL72 rack could cost as much as US$9.1 million, with memory accounting for a significant share of the cost. Bernstein also forecasts HBM4 pricing to rise to US$53 per GB by 2027.  To lower costs and ease supply constraints, NVIDIA may adopt 96GB SOCAMM modules for each Vera CPU in its Vera Rubin NVL72 racks, down from the previously planned 192GB, the report says. As a result, total Vera CPU memory would decline from about 55TB to 28TB, while GPU HBM4 capacity would remain unchanged at 20.7TB per rack.  The revised memory configuration could deliver significant savings. As noted by Wccftech, GF Securities originally estimated LPDDR5X costs for the VR200 at around US$1.2 million based on the original memory configuration. Under the revised configuration, the cost is estimated to fall to around US$586,000 and could decline further to as low as US$293,000 if memory capacity is reduced to one-quarter.  According to a press release released by TrendForce in early June, based on preliminary allocation plans from Samsung, SK hynix, and Micron, NVIDIA is expected to receive enough LPDRAM to meet only about 60% of its estimated demand, with limited room for further increases. As a result, the company may reduce the SOCAMM memory configuration of its Vera Rubin Superchip platform to increase Vera CPU production and mitigate the risk of prolonged supply shortages.  Strengthening Long-Term Memory Partnerships  As NVIDIA navigates these cost and supply challenges, it is also strengthening its long-term partnerships with South Korea’s memory industry. According to Tom’s Hardware, NVIDIA and SK Group signed letters of intent formalizing a strategic partnership valued at more than US$500 billion. The collaboration spans multiple areas, including a long-term memory supply agreement with SK hynix that was announced in June.
2026-07-29 13:48 阅读量:554
NVIDIA Enters PC Market with RTX Spark Featuring MediaTek-Co-Designed N1X CPU on TSMC 3nm
  As traditional CPU leaders such as Intel push further into the AI accelerator market, NVIDIA is moving in the opposite direction—leveraging its dominance in AI computing to expand into the PC processor arena. At GTC Taipei on June 1, CEO Jensen Huang unveiled the NVIDIA RTX Spark, developed in partnership with Microsoft and powered by the new Arm-based N1X processor co-designed with MediaTek, according to NVIDIA and CNBC.  According to CNBC, the initial rollout will include more than 30 notebook models and 10 desktop systems. RTX Spark-powered devices from Microsoft, Dell, HP, ASUS, Lenovo, and MSI are expected to debut this fall, marking NVIDIA’s first large-scale push into the Windows PC CPU market.  CNBC adds that the platform combines NVIDIA’s Blackwell GPU architecture with the N1X CPU and 128GB of unified memory, bringing data center-class AI capabilities to personal computers. Notably, the new PC processor will be manufactured using TSMC’s 3nm process, which is currently produced exclusively in Taiwan, according to CNBC.  More Spec Details  Interestingly, as noted by The Verge, the flagship RTX Spark mirrors the DGX Spark almost exactly — 20 CPU cores, 6,144 GPU cores, 128GB of LPDDR5X memory — though NVIDIA plans to release leaner, more affordable variants, with some configurations dropping to just 16GB of RAM.  Meanwhile, NVIDIA has provided additional details on the platform’s performance. According to The Verge, with up to 128GB of unified memory—on par with AMD’s previous-generation Strix Halo—RTX Spark laptops and desktops are also capable of hosting AI agents with up to 120 billion parameters, a capability Microsoft appears eager to integrate into Windows.  Powered by RTX Spark, NVIDIA claims the system can render a 90GB 3D scene, edit 12K video, or run graphically intensive titles like Indiana Jones and the Great Circle at a smooth 100fps in 1440p—all within a 14mm-thin laptop operating without being plugged into power, the report adds.  CNBC, citing an NVIDIA spokesperson, reports that RTX Spark is described as being “roughly equivalent” to the company’s flagship RTX 5070 laptop GPU.  NVIDIA is certainly not the only player eyeing to expand its CPU footprint. As noted by CNBC, Apple now designs its own Arm-based processors for Mac computers, having rolled out a higher-end MacBook lineup powered by its latest M5 chips in March. In the same month, Arm unveiled its first in-house CPU, with Meta reportedly serving as the launch customer for the Arm AGI CPU, according to TechCrunch.
2026-06-02 10:29 阅读量:1308
NVIDIA Reportedly Plans GPU-Direct Storage for Vera Rubin, Raising Expectations for HBF Beyond HBM
  As AI models continue to scale, HBM may struggle to meet future memory-capacity demands, prompting industry experts to view GPU-driven storage architectures as a potential next frontier. According to The Elec, NVIDIA and Amazon are reportedly advancing storage architectures that allow GPUs to directly control storage devices such as SSDs. NVIDIA is said to plan the introduction of GPU-Initiated Direct Storage Access (GIDS) starting with its Vera Rubin AI platform, a shift that could accelerate the emergence of high-bandwidth flash (HBF), the report notes.  Citing Song Ki-hwan, a professor in the Department of System Semiconductor Engineering at Yonsei University, the report explains that GIDS goes beyond existing GPU Direct Storage (GDS) architecture. Under GDS, CPUs issue data requests to storage devices before data is transferred to GPUs. GIDS advances this by allowing GPUs to access storage directly, bypassing CPUs and DRAM.  Both GIDS and GDS aim to overcome data-transfer bottlenecks tied to traditional von Neumann computing architectures. Microsoft and AMD are also said to be exploring similar approaches. The report, citing Song, adds that traditional data-transfer methods are inefficient because CPUs are structurally limited in thread processing, while GPUs can generate tens of thousands of parallel threads. Song also notes that GPU-HBM data transfer already accounts for roughly half of total system power, strengthening the case for HBF architectures that place ultra-fast NAND closer to GPUs to address future AI bottlenecks.  GIDS Could Accelerate HBF and Expand NAND’s Role in AI Memory  The emergence of GIDS could allow NAND storage to take on a larger role in AI memory systems while easing pressure on HBM capacity. As the report notes, this shift would require higher-performance NAND flash capable of keeping pace with GPU processing speeds. One proposed approach is high-bandwidth flash (HBF), which stacks NAND flash vertically in a structure similar to HBM and connects it using through-silicon vias (TSVs).  The report notes that NAND flash offers roughly 30 times higher bit density than DRAM, enabling far greater memory capacity in a similar footprint. According to Song, combining six HBF units with two HBM units could increase GPU memory capacity more than 16 times, from 192GB to 3,120GB, potentially supporting AI models with parameter sizes around 16 times larger than current architectures.  Still, NAND flash has endurance limits, typically supporting only around 100,000 write-and-erase cycles versus DRAM’s near-unlimited write capability. As a result, HBF is seen as better suited for storing AI model parameters, which remain largely unchanged during inference and function as read-only workloads.  Meanwhile, memory makers have also been exploring GPU-driven memory architectures. According to an Edaily report last year, sources said Samsung Electronics is actively researching next-generation high-performance Z-NAND. The company is also developing GIDS technology that would allow GPUs to directly access Z-NAND-based storage devices. If implemented, GPUs would be able to access Z-NAND devices without intermediaries, potentially shortening processing times for AI workloads.
2026-05-20 11:20 阅读量:2060
  • 一周热料
  • 紧缺物料秒杀
型号 品牌 询价
RB751G-40T2R ROHM Semiconductor
MC33074DR2G onsemi
CDZVT2R20B ROHM Semiconductor
BD71847AMWV-E2 ROHM Semiconductor
TL431ACLPR Texas Instruments
型号 品牌 抢购
STM32F429IGT6 STMicroelectronics
TPS63050YFFR Texas Instruments
BP3621 ROHM Semiconductor
IPZ40N04S5L4R8ATMA1 Infineon Technologies
BU33JA2MNVX-CTL ROHM Semiconductor
ESR03EZPJ151 ROHM Semiconductor
热门标签
ROHM
Aavid
Averlogic
开发板
SUSUMU
NXP
PCB
传感器
半导体
相关百科
关于我们
AMEYA360微信服务号 AMEYA360微信服务号
AMEYA360商城(www.ameya360.com)上线于2011年,现 有超过3500家优质供应商,收录600万种产品型号数据,100 多万种元器件库存可供选购,产品覆盖MCU+存储器+电源芯 片+IGBT+MOS管+运放+射频蓝牙+传感器+电阻电容电感+ 连接器等多个领域,平台主营业务涵盖电子元器件现货销售、 BOM配单及提供产品配套资料等,为广大客户提供一站式购 销服务。

请输入下方图片中的验证码:

验证码