NVIDIA 公司 (NVDA) 首席执行官 Jen-Hsun Huang 在 2020年 第二季度业绩 - 收益电话会议记录

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NVIDIA Corporation (NASDAQ:NVDA) Q2 2020 Earnings Conference Call August 15, 2019 5:30 PM ET

NVIDIA公司(纳斯达克股票代码:[NVDA])2020年第二季度收益电话会议2019年8月15日美国东部时间下午5:30

公司参与者

Simona Jankowski - Investor Relations
Jen-Hsun Huang - President and Chief Executive Officer
Colette Kress - Executive Vice President and Chief Financial Officer

  • Simona Jankowski - 投资者关系
  • Jen-Hsun Huang - 总裁兼首席执行官
  • Colette Kress - 执行副总裁兼首席财务官

电话会议参与者

C.J. Muse - Evercore
Vivek Arya - Bank of America Merrill Lynch
Toshiya Hari - Goldman Sachs
Harlan Sur - JPMorgan
Timothy Arcuri - UBS
Matt Ramsay - Cowen
Joe Moore - Morgan Stanley
Aaron Rakers - Wells Fargo
Stacy Rasgon - Bernstein Research

  • C.J.Muse - Evercore
  • Vivek Arya - 美国银行美林
  • Toshiya Hari - Goldman Sachs
  • Harlan Sur - 摩根大通
  • 蒂莫西·阿库里 - 瑞银集团
  • 马特拉姆齐 - 考恩
  • 乔摩尔 - 摩根士丹利
  • Aaron Rakers - 富国银行
  • Stacy Rasgon - 伯恩斯坦研究

会议主持员

Good afternoon. My name is Christina, and I will be your conference operator today. Welcome to NVIDIA’s financial results conference call. [Operator Instructions] I will now turn the call over to Simona Jankowski from Investor Relations to begin your conference.

下午好。 我叫克里斯蒂娜,今天我将成为你的会议运营商。 欢迎来到NVIDIA的财务业绩电话会议。 [操作员说明]现在,我将从Investor Relations转到Simona Jankowski,开始您的会议。

Simona Jankowski

Thank you. Good afternoon, everyone and welcome to NVIDIA’s conference call for the second quarter of fiscal 2020. With me on the call today from NVIDIA are Jen-Hsun Huang, President and Chief Executive Officer and Colette Kress, Executive Vice President and Chief Financial Officer.
I would like to remind you that our call is being webcast live on NVIDIA’s Investor Relations website. The webcast will be available for replay until the conference call to discuss our financial results for the third quarter of fiscal 2020. The content of today’s call is NVIDIA’s property. It can’t be reproduced or transcribed without our prior written consent.
During this call, we may make forward-looking statements based on current expectations. These are subject to a number of significant risks and uncertainties, and our actual results may differ materially. For a discussion of factors that could affect our future financial results and business, please refer to the disclosure in today’s earnings release, our most recent Form 10-K and 10-Q and the reports that we may file on Form 8-K with the Securities and Exchange Commission. All our statements are made as of today, August 15, 2019, based on information currently available to us. Except as required by law, we assume no obligation to update any such statements. During this call, we will discuss non-GAAP financial measures. You can find a reconciliation of these non-GAAP financial measures to GAAP financial measures in our CFO commentary, which is posted on our website.
With that, let me turn the call over to Colette.

谢谢。大家下午好,欢迎参加NVIDIA 2020财年第二季度的电话会议。今天NVIDIA的电话会议主席是总裁兼首席执行官黄仁勋和执行副总裁兼首席财务官Colette Kress。

我想提醒您,我们的电话正在NVIDIA投资者关系网站上进行网络直播。网络直播将重播,直到电话会议讨论我们在2020财年第三季度的财务业绩。今天的电话内容是NVIDIA的财产。未经我们事先书面同意,不得复制或转录。

在此次电话会议中,我们可能会根据当前的预期做出前瞻性陈述。这些都存在许多重大风险和不确定因素,我们的实际结果可能会有重大差异。有关可能影响我们未来财务业绩和业务的因素的讨论,请参阅今天的收益发布中的披露,我们最新的10-K表格和10-Q表格,以及我们可以在表格8-K上提交的报告。证券交易委员会。根据我们目前可获得的信息,我们所有的陈述均于2019年8月15日截止。除法律要求外,我们不承担更新任何此类声明的义务。在此次电话会议中,我们将讨论非GAAP财务指标。您可以在我们的CFO评论中找到这些非GAAP财务指标与GAAP财务指标的对账,该评论发布在我们的网站上。

有了这个,让我把电话转到科莱特。

Colette Kress

Thanks, Simona. Q2 revenue was $2.58 billion, in line with our outlook, down 17% year-on-year and up 16% sequentially. Starting with our gaming business, revenue of $1.31 billion was down 27% year-on-year and up 24% sequentially. We are pleased with the strong sequential growth in the quarter when we launched our RTX SUPER lineup for desktop gamers, wrapped up our greatest ever number of gaming laptops and launched our new RTX studio laptops for creators.
In July, we unveiled 3 GeForce RTX SUPER GPUs, delivering the best-in-class gaming performance and power efficiency and real-time ray tracing for both current and next-generation games. These GPUs delivered a performance boost of up to 24% from our initial Turing GPUs launched a year earlier. The SUPER lineup strengthens our leadership in the high end of the market and the response has been great. We look forward to delighting gamers with the best performance in ray tracing as we get into the back to school and holiday shopping seasons. Ray tracing is taking the gaming industry by storm and have quickly come to define the modern era of computer graphics. A growing number of blockbuster AAA titles have announced support for NVIDIA RTX ray tracing, including Call of Duty: Modern Warfare, Cyberpunk 2077, Watch Dogs: Legion and Wolfenstein: Youngblood.
Excitement around these titles is tremendous. GameSpot called Cyberpunk one of the most anticipated games of the decade. NVIDIA GeForce RTX are the only graphic cards in the market with hardware support for ray tracing. They deliver a 2 to 3x performance speed up over GPUs without a dedicated ray tracing core. The laptop business continues to be a standout growth driver as OEMs are ramping a record 100-plus gaming laptop models ahead of the back to school and holiday season. The combination of our energy-efficient Turing architecture and Max-Q technology enables beautifully crafted thin and light form factors that can deliver the performance of high-end gaming desktop or our next-generation console.
At Computex in May, we unveiled NVIDIA RTX Studio laptops, a new design artist platform that extends our reach to the large, underserved market of creators. In the age of YouTube, creators and freelancers are rapidly growing population, but they have traditionally not had access to professional-grade workstations through online and retail channels. RTX Studio laptops are designed to meet their increasing complex workflows such as photorealistic ray tracing, AI image enhancement and ultra high-resolution video. Powered by our RTX GPUs and optimized software, RTX Studio laptops deliver performance that’s up to 7x faster than that of the MacBook Pro. A total of 27 RTX Studio models have been announced by major OEMs. Sequential growth also benefited from the production ramp of the two new models of Nintendo Switch gaming console. We are expecting our console business to remain strong in Q3 before the seasonal production slowdown in Q4 when console-related revenue is expected to be fairly minimal, similar to last year.

谢谢,西蒙娜。第二季度收入为25.8亿美元,与我们的前景一致,同比下降17%,比上一季度增长16%。从我们的博彩业务开始,13.1亿美元的收入同比下降27%,比上一季度增长24%。我们对本季度强劲的连续增长感到高兴,因为我们推出了针对桌面游戏玩家的RTX SUPER阵容,包装了我们最大数量的游戏笔记本电脑,并为创作者推出了新的RTX工作室笔记本电脑。

7月,我们推出了3款GeForce RTX SUPER GPU,为当前和下一代游戏提供同类最佳的游戏性能和功效以及实时光线追踪。这些GPU的性能比一年前推出的初始图灵GPU高出24%。 SUPER阵容加强了我们在高端市场的领导地位,并且响应非常好。当我们回到学校和假日购物季节时,我们期待让光线追踪中表现最佳的游戏玩家满意。光线追踪正在风靡游戏产业,并迅速定义了计算机图形学的现代时代。越来越多的轰动一时的AAA游戏宣布支持NVIDIA RTX射线追踪,包括使命召唤:现代战争,Cyber​​punk 2077,Watch Dogs:Legion和Wolfenstein:Youngblood。

围绕这些头衔的兴奋是巨大的。 GameSpot将Cyber​​punk称为十年来最受期待的游戏之一。 NVIDIA GeForce RTX是市场上唯一具有光线跟踪硬件支持的图形卡。它们可以在GPU上提供2到3倍的性能,而无需专用的光线跟踪核心。笔记本电脑业务仍然是一个突出的增长动力,因为原始设备制造商在回到学校和假日季节之前正在提升创纪录的100多款游戏笔记本电脑型号。我们的节能图灵架构和Max-Q技术相结合,可实现精美的轻薄外形,可提供高端游戏台式机或下一代控制台的性能。

在5月份的Computex上,我们推出了NVIDIA RTX Studio笔记本电脑,这是一个新的设计艺术家平台,可以扩展我们对大型,服务欠缺的创作者市场的影响。在YouTube时代,创作者和自由职业者的人口迅速增长,但他们传统上无法通过在线和零售渠道访问专业级工作站。 RTX Studio笔记本电脑旨在满足日益复杂的工作流程,如逼真的光线追踪,AI图像增强和超高分辨率视频。 RTX Studio笔记本电脑由我们的RTX GPU和优化软件提供支持,其性能比MacBook Pro快7倍。主要原始设备制造商共宣布了27款RTX Studio型号。连续增长也得益于两款新型Nintendo Switch游戏机的生产阶段。在第四季度季节性产量放缓之前,我们预计我们的控制台业务将在第三季度保持强劲,因为与去年类似,与控制台相关的收入预计相当微小。

Moving to data center, revenue was $655 million, down 14% year-on-year and up 3% sequentially. In the vertical industries portion of the business, expanding AI workload drove sequential and year-over-year growth. In hyperscale portion, we continue to be impacted by relatively weak overall spending at a handful of CSPs. Sales of NVIDIA GPUs for use in the cloud were solid. While sales of internal hyperscale use were muted, the engineering focus on AI is growing.
Let me give some color on each of these areas. We are building a broad base of customers across multiple industries as they adopt NVIDIA’s platforms to harness the power of AI. Public sectors, higher education and financial services were among the key verticals driving growth this quarter. In addition, we won Lighthouse account deals in important industries that are on the cusp of being transformed by AI. For example, in retail, Wal-Mart is using NVIDIA GPUs to run some of its product demand forecasting models, slashing the time to do so in just 4 hours from several weeks on CPUs. By accelerating its data science workflow, Wal-Mart can improve its algorithms, reduce development cycles and test new features.
Earlier this week, we announced breakthroughs for the fastest training and inference of the state-of-the-art model for natural language process understanding called BERT, or Bidirectional Encoder Representations from Transformers, a breakthrough AI language model that achieves a deeper sense of language, context and meaning. This can enable mere human comprehension in real-time by chat box, intelligent personal assistants and search engines. We are working with Microsoft as an early adopter of these advances. AI computing leadership is a high priority for NVIDIA. Last month, we set records for training deep learning neural network models on the latest MLPerf benchmarks, particularly in the most demanding areas. In just 7 months, we have achieved up to 80% speed-ups enabled by new algorithms and software optimizations across the full stack while using the same hardware. This is a direct result of the productive programming environment and flexibility of CUDA.
Delivering AI at scale isn’t just about silicon. It’s about optimizing across the entire high-performance computing system. In fact, the NVIDIA AI platform is getting progressively faster. Every month, we publish new optimization and performance improvements to CUDA-X AI libraries, supporting every AI framework and development environment. All in, our ecosystem of developers is now 1.4 million strong. In setting these MLPerf records, we leveraged our new DGX SuperPOD AI supercomputer, demonstrating that leadership in AI research demands leadership in computing infrastructure. This system debuted in June at #22 on the TOP500 list of the world’s fastest supercomputers at the annual International Supercomputing Conference. Used to meet the massive demand for autonomous vehicle development program, it is powered by more than 1,500 NVIDIA V100 Tensor Core GPUs linked with Mellanox interconnects. We have made DGX SuperPOD available commercially to customers, essentially providing them with the turnkey supercomputer that they can assemble in weeks rather than months. It is roughly 400x smaller in size than other similarly performing TOP500 systems, which are built from thousands of servers. Also at the conference, we announced that by next year’s end, we will make available to the ARM ecosystem NVIDIA’s full stack of AI and HPC software, which accelerates more than 600 HPC applications and all AI frameworks. With this announcement, NVIDIA will accelerate all major CPU architectures, including x86, POWER and ARM. Lastly, regarding our pending acquisition of Mellanox, we have received regulatory approval in the U.S. and are engaged with regulators in Europe and China. The approval process is progressing as expected, and we continue to work toward closing the deal by the end of this calendar year.

迁至数据中心,收入为6.55亿美元,同比下降14%,比上一季度增长3%。在业务的垂直行业部分,不断扩大的AI工作量推动了连续增长和逐年增长。在超大规模部分,我们继续受到少数CSP相对较弱的总体支出的影响。用于云计算的NVIDIA GPU的销售情况良好。虽然内部超大规模使用的销售受到抑制,但人工智能的工程重点正在增长。

让我在这些方面给出一些颜色。我们正在为多个行业建立广泛的客户群,因为他们采用NVIDIA的平台来利用AI的力量。公共部门,高等教育和金融服务是本季度推动增长的关键垂直行业之一。此外,我们在重要行业赢得了灯塔账户交易,这些行业正处于被人工智能转型的尖端。例如,在零售业中,沃尔玛正在使用NVIDIA GPU来运行其部分产品需求预测模型,从而在数小时内在CPU上花费4个小时的时间。通过加速其数据科学工作流程,沃尔玛可以改进其算法,缩短开发周期并测试新功能。

本周早些时候,我们宣布了最快的培训和最先进的自然语言过程理解模型的推广,称为BERT,或变形金刚的双向编码器表示,这是一种突破性的AI语言模型,可以实现更深层的语言感,背景和意义。这可以通过聊天框,智能个人助理和搜索引擎实现人类的实时理解。我们正在与微软合作,作为这些进步的早期采用者。人工智能计算的领导地位是NVIDIA的首要任务。上个月,我们在最新的MLPerf基准测试中创建了深度学习神经网络模型的记录,特别是在最苛刻的领域。在短短7个月内,我们通过整个堆栈中的新算法和软件优化实现了高达80%的加速,同时使用相同的硬件。这是生产性编程环境和CUDA灵活性的直接结果。

大规模提供AI不仅仅是硅。它是关于整个高性能计算系统的优化。事实上,NVIDIA AI平台正在逐步加快。每个月,我们都会向CUDA-X AI库发布新的优化和性能改进,支持每个AI框架和开发环境。总而言之,我们的开发者生态系统现在已经达到了140万。在设置这些MLPerf记录时,我们利用了我们新的DGX SuperPOD AI超级计算机,证明人工智能研究的领导地位需要计算基础设施的领导地位。该系统于6月在年度国际超级计算机大会上的世界最快超级计算机TOP500排行榜上排名第22位。用于满足对自动驾驶汽车开发计划的巨大需求,它由超过1,500个与Mellanox互连相连的NVIDIA V100 Tensor Core GPU提供动力。我们已经向客户提供商用DGX SuperPOD,主要是为他们提供交钥匙超级计算机,他们可以在几周而不是几个月内组装。它比其他同类型的TOP500系统小了约400倍,这些系统由数千台服务器构成。同样在会议上,我们宣布,到明年年底,我们将向ARM生态系统提供NVIDIA的全套AI和HPC软件,这些软件可加速600多个HPC应用程序和所有AI框架。随着这一宣布,NVIDIA将加速所有主要的CPU架构,包括x86,POWER和ARM。最后,关于我们即将收购Mellanox,我们已获得美国监管部门的批准,并与欧洲和中国的监管机构合作。审批流程正在按预期进行,我们将继续努力在本日历年年底之前完成交易。

Moving to pro visualization, revenue reached $291 million, up 4% from our prior year and up 9% sequentially. Year-on-year and sequential growth was led by record revenue for mobile workstations with strong demand for new thin and light form factors. We had a great showing at SIGGRAPH, the computer graphics industry’s biggest annual conference held in Los Angeles. Our researchers won several Best in Show awards. In just a year since the launch of RTX ray tracing, over 40 design and creative applications with RTX technology had been announced by leading software vendors, including Adobe, Autodesk and Dassault systems and many others. NVIDIA RTX technology has reinvigorated the computer graphics industry by enabling researchers and developers to take a leap in photorealistic rendering, augmented reality and virtual reality.
Finally, turning to automotive, Q2 revenue was $209 million, up 30% from a year ago and up 26% sequentially. This reflects growing adoption of next-generation AI cockpit solutions and autonomous vehicle development projects, including one particularly sizable development services transaction that was recognized in the quarter. In addition, in June, we announced a new partnership with the Volvo Group to develop AI and autonomous trucks utilizing NVIDIA’s end-to-end AI platform for training, simulation and in-vehicle computing. The strategic partnership will enable Volvo Group to develop a wide range of autonomous driving solutions for freight transport, recycling collection, public transport, construction, mining, forestry and more. This collaboration is a great validation of our long-held position that every vehicle, not just cars but also trucks, shuttles, business, taxis and many others, will have autonomous capability 1 day. Autonomous features can bring enormous value to the trucking industry, in particular as the demand of online shopping put ever greater stress on the world’s transport systems. Expectations for overnight or same-day deliveries create challenges that can only be met by autonomous trucks, which can operate 24 hours a day. To help address these needs, NVIDIA has created an end-to-end platform for autonomous vehicles from AI computing infrastructure to large-scale simulation to in-car computing. Multiple customers from OEMs like Mercedes-Benz, Toyota and Volvo to Tier 1s like Bosch, Continental and ZF are already onboard. We see this as a $30 billion addressable market by 2025.
Moving to the rest of the P&L, Q2 GAAP gross margins was 59.8% and non-GAAP was 60.1%, up sequentially, reflecting higher automotive development services, a favorable mix in gaming and lower component cost. GAAP operating expenses were $970 million, and non-GAAP operating expenses were $749 million, up 19% and 8% year-on-year, respectively. We remain on track for high single-digit OpEx growth in fiscal 2020 while continuing to invest in the key platforms driving our long-term growth, namely graphics, AI and self-driving cars. GAAP EPS was $0.90, down 49% from a year earlier. Non-GAAP EPS was $1.24, down 36% from a year ago.

转向专业可视化,收入达到2.91亿美元,比上一年增长4%,比上一季度增长9%。移动工作站的收入创历史新高,同时对新的轻薄外形有强烈需求,导致同比增长和连续增长。我们在SIGGRAPH展出了一场精彩的展示,这是计算机图形行业在洛杉矶举行的最大年会。我们的研究人员赢得了多项最佳展示奖。自推出RTX光线跟踪仅一年后,领先的软件供应商宣布了40多种采用RTX技术的设计和创意应用,包括Adobe,Autodesk和Dassault系统以及许多其他公司。 NVIDIA RTX技术使计算机图形行业重新焕发活力,使研究人员和开发人员在照片级渲染,增强现实和虚拟现实方面实现了飞跃。

最后,转向汽车,第二季度收入为2.09亿美元,比一年前增长30%,比上一季度增长26%。这反映了下一代AI驾驶舱解决方案和自动驾驶汽车开发项目的日益普及,其中包括本季度认可的一项特别大规模的开发服务交易。此外,6月,我们宣布与沃尔沃集团建立新的合作伙伴关系,利用NVIDIA的端到端AI平台开发人工智能和自动驾驶卡车,用于培训,模拟和车载计算。该战略合作伙伴关系将使沃尔沃集团能够为货运,回收,公共交通,建筑,采矿,林业等开发各种自动驾驶解决方案。这种合作很好地证明了我们长期以来的立场,即每辆车,不仅仅是汽车,还有卡车,航天飞机,商务,出租车和其他许多车辆,都将拥有1天的自主能力。自动化功能可以为卡车运输行业带来巨大价值,特别是随着网络购物需求给世界运输系统带来更大的压力。对隔夜或当天交付的期望带来的挑战只能通过自动卡车来满足,自动卡车可以全天24小时运营。为了满足这些需求,NVIDIA为自动驾驶汽车创建了一个端到端平台,从人工智能计算基础设施到大规模仿真再到车载计算。从梅赛德斯 - 奔驰,丰田和沃尔沃等原始设备制造商到博世,大陆和ZF等一线机构的众多客户已经上市。到2025年,我们认为这是一个价值300亿美元的可寻址市场。

转向剩余的损益表,第二季度美国通用会计准则毛利率为59.8%,非美国通用会计准则为60.1%,这反映了汽车开发服务的增长,游戏的有利组合和较低的组件成本。 GAAP运营支出为9.7亿美元,非GAAP运营支出为7.49亿美元,同比分别增长19%和8%。我们继续保持2020财年高单位数运营支出增长的轨道,同时继续投资推动我们长期增长的关键平台,即图形,人工智能和自动驾驶汽车。 GAAP每股收益为0.90美元,较上年同期下降49%。非GAAP每股收益为1.24美元,比一年前下降36%。

With that, let me turn to the outlook for the third quarter of fiscal 2020. We expect revenue to be $2.9 billion, plus or minus 2%. GAAP and non-GAAP gross margins are expected to be 62% and 62.5%, respectively, plus or minus 50 basis points. GAAP and non-GAAP operating expenses are expected to be approximately $980 million and $765 million, respectively. GAAP and non-GAAP OI&E are both expected to be income of approximately $25 million. GAAP and non-GAAP tax rates are both expected to be 10%, plus or minus 1%, excluding discrete items. Capital expenditures are expected to be approximately $100 million to $120 million. Further financial details are included in the CFO commentary and other information available on our IR website.
In closing, let me highlight upcoming events for the financial community. We will be at the Jefferies conference, hardware and communications infrastructure summit, on August 27 and at the Citi Global Technology Conference on September 25. With that, we will now open the call for questions. Operator, would you please poll for the questions?

有了这个,让我转向2020财年第三季度的前景。我们预计收入将达到29亿美元,上下浮动2%。 GAAP和非GAAP毛利率预计分别为62%和62.5%,上下浮动50个基点。 GAAP和非GAAP运营费用预计分别约为9.8亿美元和7.65亿美元。 GAAP和非GAAP OI&E预计都将获得约2500万美元的收入。 GAAP和非GAAP税率预计为10%,正负1%,不包括离散项目。资本支出预计约为1亿至1.2亿美元。更多财务详情包含在CFO评论和我们的IR网站上提供的其他信息中。

最后,让我重点介绍金融界即将举行的活动。我们将于8月27日参加Jefferies会议,硬件和通信基础设施峰会,以及9月25日举行的花旗全球技术大会。届时,我们将开启提问的呼吁。接线员,请您查询问题?

问答环节

[Operator Instructions] And your first question comes from the line of C.J. Muse with Evercore.

[操作员说明]你的第一个问题来自C.J.Muse和Evercore的系列。

C.J。 沉思

Good afternoon. Thank you for taking the questions. I guess first question on gaming, how should we think about your outlook into the October quarter vis-à-vis kind of normal seasonality? How are you thinking about Switch within that? And considering now that you have full Turing lineup as well as content truly coming to the forefront here, how do you think about trends beyond the October quarter? Thank you.

下午好。 感谢您提出问题。 我想关于游戏的第一个问题,我们应该怎样考虑你对10月季度的看法与正常的季节性? 您如何看待Switch内部? 现在考虑到你有完整的图灵阵容以及真正在这里走在前列的内容,你如何看待10月季度以后的趋势? 谢谢。

Jen-Hsun Huang

Sure. Colette, why don’t you take the Switch question? And then I will take the rest of the RTX questions.

当然。 科莱特,你为什么不接受Switch的问题呢? 然后我将接受其余的RTX问题。

Colette Kress

Sure. From a gaming perspective, the overall Switch or the overall console business definitely is a seasonal business. We usually expect to see production ramping in Q2 and in Q3, with it coming down likely in Q4. So you should see Switch to be a portion definitely of our gaming business in Q3.

当然。 从游戏角度来看,整体Switch或整体控制台业务绝对是季节性业务。 我们通常预计第二季度和第三季度的产量将会增加,第四季度可能会下降。 所以你应该看到Switch在第三季度成为我们游戏业务的一部分。

Jen-Hsun Huang

Yes. C.J., thanks for your question. RTX as you know is – first of all, RTX is doing great. I think we have put all the pieces in place to bring ray tracing into the future of games. The number of games, the blockbuster games that adopted RTX is really snowballing. We announced several 6 games in the last couple of months. There is going to be some exciting announcements next week at gamescom. It’s pretty clear now the future of gaming will include ray tracing. The number of software developers that create – with creative tools that adopted RTX is really quite spectacular. We now have 40 – over 40 ISV tools that was announced at SIGGRAPH that have accelerated ray tracing and video editing. And some of the applications’ amazing AI capabilities for image optimization enhancement support RTX.
And so looking forward, this is what I expect. I expect that ray tracing is going to drive a reinvigoration of gaming graphics. I expect that the over 100 laptops that we have RTX designed – RTX GPUs designed into is going to contribute our growth. Notebook gaming is one of the fastest-growing segments of the gaming platform world. The number of notebooks that are able to game is only a few percent, so it’s extremely underexposed. And yet, we know that gamers are – like the rest of us, they like thin and light notebooks, but they like it to be able to run powerful games. And so this is an area that has grown significantly for us year-over-year, and we’re expecting it to grow through the end of the – through the second half and through next year. And one of the things that’s really exciting is our RTX Studio line that we introduced recently. We observed, and through our discussions with the PC industry, that the creatives are really underexposed and underserved by the latest technologies. And they want notebooks and they want PCs that have powerful graphics. They use it for 3D content creation and high-definition video editing and image optimization and things like that. And we introduced a brand-new line of computers that we call RTX Studio. Now the OEMs were so excited about it. And at SIGGRAPH, we now have 27 different laptops shipping and more coming. And so I think RTX is really geared for growth. We have great games coming. We got the SUPER line of GPUs. We have all of our notebooks that were designed into that we are ramping and of course, the new RTX Studio line. And so I expect this to be a growth market for us.

是。 C.J.,谢谢你的提问。你知道RTX - 首先,RTX做得很好。我认为我们已经把所有的部分放在适当的位置,以便将光线追踪带入游戏的未来。游戏的数量,采用RTX的大片游戏真的很滚雪球。在过去的几个月里,我们宣布了6场比赛。下周将在gamescom上发布令人兴奋的消息。现在很清楚,游戏的未来将包括光线追踪。使用RTX创建工具的软件开发人员数量非常惊人。我们现在有四十多个ISV工具在SIGGRAPH上宣布,它们加速了光线跟踪和视频编辑。一些应用程序用于图像优化增强的惊人AI功能支持RTX。

如此期待,这就是我的期望。我预计光线追踪将推动游戏图形的重新焕发活力。我希望我们有RTX设计的100多台笔记本电脑 - 设计的RTX GPU将有助于我们的增长。笔记本电脑游戏是游戏平台世界中增长最快的部分之一。能够游戏的笔记本电脑数量只有几个百分点,所以它的曝光率极低。然而,我们知道游戏玩家 - 就像我们其他人一样,他们喜欢轻薄的笔记本电脑,但他们喜欢它能够运行强大的游戏。所以这个领域对我们来说已经逐年增长,我们预计它会在下半年到明年结束时增长。其中一件非常令人兴奋的事情是我们最近推出的RTX Studio系列。我们通过与PC行业的讨论观察到,创意产品实际上是曝光不足,而且是最新技术的不足。他们想要笔记本电脑,他们想要拥有强大显卡的电脑。他们将它用于3D内容创建和高清视频编辑和图像优化等等。我们推出了一种全新的计算机系列,我们称之为RTX Studio。现在,原始设备制造商对此感到非常兴奋。在SIGGRAPH,我们现在有27种不同的笔记本电脑出货,还有更多。所以我认为RTX真的适合增长。我们有很棒的游戏。我们得到了SUPER系列的GPU。我们所有的笔记本电脑都是我们正在设计的,当然还有新的RTX Studio系列。所以我希望这对我们来说是一个增长的市场。

克里斯托弗詹姆斯

Very helpful. If I could follow-up on the data center side, perhaps you can speak directly just to the hyperscale side, both internal and cloud, and whether you’re seeing any green shoots, any signs of life there and how you are thinking about what that rate of recovery could look like over time?

很有帮助。 如果我可以在数据中心方面进行跟进,也许你可以直接对超大规模方面说话,无论是内部还是云端,以及你是否看到任何绿芽,那里的生活迹象以及你如何思考什么 随着时间的推移,恢复的速度会是什么样子?

Jen-Hsun Huang

With the exception of a couple of hyperscalers, C.J., I would – we’re seeing broad-based growth in data centers. In the area of training, the thing that’s really exciting everybody, and everybody is racing towards, is training these large gigantic natural language understanding models, language models. The transformer model that was introduced by Google, called BERT, has since been enhanced into XLned and RoBERTa and, gosh, so many different, GP2, and Microsoft’s MASS. And there are so many different versions of these language models. And in the AI, NLU, natural language understanding, is one of the most important areas that everybody’s racing to go to. And so, these models are really, really large. It’s over 1,000x larger than image models that we’re training just a few years ago, and they’re just gigantic models. It’s one of the reasons why we built the DGX SuperPOD so that we could train these gigantic models in a reasonable amount of time. The second area – so that’s training in the hyperscalers. The second area where we are seeing enormous amounts of activity has to do with trying to put these conversational AI models into services so that they could be interactive and in real time. Whereas photo tagging and photo enhancement is something that you could put off-line and you could do that while you have excess capacity when it’s off of the most busy time of the day. You can’t do that with language and conversational AI. You better to respond to the person in real time. And so the performance that’s required is significant. But more importantly, the number of models necessary for conversational AI from speech recognition to language understanding to recommendation systems to text-to-speech to wave synthesis these 5, 6, 7 models have to be processed in real time – in series and in real time so that you can have a reasonable conversation with the AI agent. And so these type of activities is really driving interest and activity at all of the hyperscalers. My expectation is that this is going to continue to be a big growth opportunity for us. But more importantly, in addition to that, we’re seeing that AI is – the wave of AI is going from the cloud to the enterprise to the edge and all the way out to the autonomous systems. The place where we’re seeing a lot of excitement, and we talked about that in the past and we’re seeing growth there, has to do with the vertical industry enterprises that are starting to adopt AI to create new products, whether it’s a delivery robot or some kind of a Chat Bot or the ability to detect fraud in financial services, these applications in vertical industries are really spreading all over the place. There’s some over 4,000 AI start-ups around the world. And the way that we engage them is they use our platform to start developing AI in the cloud. And as you know, we’re the only AI platform that’s available on-prem and in every single cloud. And so they can use our AI platforms for – in all the clouds, which is driving our cloud computing, external cloud computing growth. And then they can also use it on-prem if their usage really grows significantly. And that’s one of the reasons why our Tesla for OEMs and DGX is growing. And so we’re seeing broad-based excitement around AI as they use it for their products and new services. And these 4,000, 4,500 start-ups around the world is really driving consumption of that.

除了几个超大规模公司,C.J。,我会 - 我们看到数据中心的广泛增长。在训练方面,真正令每个人兴奋的事情,每个人都在奔向,正在训练这些庞大的自然语言理解模型,语言模型。谷歌引入的变压器模型,称为BERT,后来被加强到XLned和RoBERTa,以及天哪,许多不同的GP2和微软的MASS。这些语言模型有很多不同的版本。在人工智能,NLU,自然语言理解,是每个人都要去的最重要的领域之一。所以,这些模型非常非常大。它比我们几年前训练的图像模型大1000多倍,而且它们只是巨大的模型。这是我们构建DGX SuperPOD的原因之一,这样我们就可以在合理的时间内训练这些巨大的模型。第二个区域 - 这是在超大规模的培训。我们看到大量活动的第二个领域是尝试将这些会话式AI模型放入服务中,以便它们可以实时交互。虽然照片标记和照片增强功能可以让您离线,但是当您在一天中最忙碌的时间之后有足够的容量时,您可以这样做。你不能用语言和会话AI来做到这一点。你最好实时回复这个人。因此,所需的性能非常重要。但更重要的是,从语音识别到语言理解到推荐系统,从文本到语音到波形合成的会话AI所需的模型数量必须实时处理 - 串联和实际时间,以便您可以与AI代理进行合理的对话。因此,这些类型的活动真正推动了所有超大规模的兴趣和活动。我的期望是,这对我们来说将继续成为一个巨大的增长机会。但更重要的是,除此之外,我们还看到人工智能是 - 人工智能的浪潮从云到企业再到边缘,一直到自治系统。我们看到很多兴奋的地方,我们过去谈过这个,我们看到那里的增长,与开始采用AI创造新产品的垂直行业企业有关,无论是交付机器人或某种聊天机器人或检测金融服务欺诈的能力,这些垂直行业的应用程序真正遍布各地。全世界有超过4,000名人工智能初创企业。我们与他们合作的方式是他们使用我们的平台开始在云中开发AI。如您所知,我们是唯一可在本地和每个云中使用的人工智能平台。因此,他们可以使用我们的人工智能平台 - 在所有云中,这正在推动我们的云计算,外部云计算的增长。如果他们的使用真的显着增长,他们也可以在本地使用它。这也是我们针对OEM和DGX的特斯拉正在发展的原因之一。因此,我们看到围绕人工智能的广泛兴奋,因为他们将其用于产品和新服务。全世界有4,000家,4,500家初创企业真正推动消费。

会议主持员

And your next question comes from the line of Vivek Arya with Bank of America Merrill Lynch.

而你的下一个问题来自Vivek Arya与美国银行美林公司的合作。

Vivek Arya

Alright thanks for taking my questions. I actually had 2 as well, one quick one for Colette and one for Jensen. Colette, good to see the gross margin recovery getting into October is this 62% to 63% range a more sustainable level and perhaps a level you could grow off of as sales get more normalized levels? And then a bigger question is for Jensen. Again, on the data center side, Jensen, when I look back between – 2015 to 2018, your data center business essentially grew 10x. And then the last year has been a tough one with the slowdown in cloud CapEx and so forth. When do you think your data center starts to grow back on a year-to-year – on a year-on-year basis? Can that happen sometime – later this year? And then just longer term, what is the right way to think about this business? Does it go back to prior levels? Does it go at a different phase? This is the one part of the business that I think is toughest for us to model, so any color would be very helpful.

好的,谢谢你接受我的问题。 我实际上还有2个,一个用于Colette,一个用于Jensen。 科莱特,很高兴看到10月份的毛利率恢复是62%到63%的范围是一个更可持续的水平,也许你可以从销量变得更加正常化的水平上增长? 然后一个更大的问题是詹森。 再次,在数据中心方面,Jensen,当我回顾2015年到2018年间,您的数据中心业务基本上增长了10倍。 然后去年是一个艰难的一年,因为云资本支出放缓等等。 您认为您的数据中心何时开始逐年增长 - 与去年同期相比? 这可能发生在某个时候 - 今年晚些时候? 那么从长远来看,考虑这项业务的正确方法是什么? 它会回到以前的水平吗? 它会进入不同的阶段吗? 这是我认为最难对我们进行建模的业务的一部分,所以任何颜色都会非常有用。

Colette Kress

Great, so let me start first with your question, Vivek, regarding gross margins. Yes, thanks for recognizing that we are moving towards our expectations that, over time, we’ll continue to see our overall volumes improve. Essentially, our business is normalized. We’ve reached normalized levels through the last couple of quarters. And this quarter, just very similar to what we will see going forward, is mix is the largest driver, what drives our overall gross margins and our gross margin improvements.

太棒了,让我首先回答你的问题,Vivek,关于毛利率。 是的,感谢您认识到我们正朝着我们的期望发展,随着时间的推移,我们将继续看到我们的整体产量有所改善。 从本质上讲,我们的业务正常化。 我们在过去几个季度达到了标准化水平。 本季度,与我们未来看到的非常相似,混合是最大的驱动因素,是推动我们整体毛利率和毛利率改善的因素。

Jen-Hsun Huang

Yes, Vivek, if you look at the last several years, there’s no question our data center business has grown a lot. And my expectation is that it’s going to grow a lot more, and let me explain to you why. Aside from a couple – a few of uncontrollable circumstances and the exception of a couple of large customers, the overall trend, the broad-based trend, of our data center business is upward, to the right. And it is growing very nicely. There’s a couple of different dynamics that’s causing that on first principles to grow. And of course, one of them is as AI is well known now to require accelerated computing, our computing architecture is really ideal for it. AI is not just one network. It’s thousands of different types of networks, and these networks are getting more and more complex over time, the amount of data you have to process is enormous. And so like all software programs, you cannot predict exactly how the software is going to get programmed. And having a programmable architecture like CUDA and yet optimized for AI like Tensor Cores that we’ve created is really the ideal architecture.
We know also that AI is the most powerful technology force of our time. The ability for machines to learn and write software by itself and write software that no humans can write is pretty extraordinary. And the applications of AI, as you guys are watching yourself, are just spreading in every single industry. And so the way we think about AI is in waves, if you will. The first wave of AI is developing the computer architecture, and that was the first part where – that’s when a lot of people discovered who we are, and we emerged into the world of high-performance computing in AI. The second wave is applying the AI for cloud service providers or hyperscalers. They have a large amount of data. They have a lot of consumer applications. Many of them are not life-critical and so, therefore, the application of an early technology – early-adoption technology was really viable. And so you saw hyperscalers adopt AI. And the thing that’s really exciting for us is beyond recommendations, beyond image enhancement, the area where we believe the most important application for AI is likely conversational AI. Most people talking and asking questions and talking to their mobile devices and looking for something or asking for directions instead of having a page of – a list of options, it responds with an answer that is very likely a good one. The next phase of AI is what we call vertical industry enterprise AI. And this is where companies are using it not just to accelerate the business process internally, but they’re using AI to create new products and services. They could be new medical instruments to IoT-based medical instruments to monitor your health. It could be something related to an application that – used for financial services for forecasting or for fraud detection. It could be some kind of device that delivers pizza to you, delivery bots. And the combination of IoT and artificial intelligence, for the very first time, you actually have the software capabilities to make use of all of these sensors that you’re putting all over the world. And that’s the next phase of growth. And it affects companies from large industrials, transportation companies, retailers, you name it. Health care companies, you name it. And so that phase of growth of AI is the phase that we’re about to enter into.

是的,Vivek,如果你看看过去几年,毫无疑问我们的数据中心业务已经增长了很多。我的期望是它会增长得更多,让我向你解释原因。除了几个 - 一些无法控制的情况以及几个大客户的例外,我们的数据中心业务的整体趋势,基础广泛的趋势是向右,向右。而且它的成长非常好。有一些不同的动态导致第一原则增长。当然,其中之一就是人工智能现在众所周知需要加速计算,我们的计算架构非常适合它。 AI不仅仅是一个网络。它是成千上万种不同类型的网络,随着时间的推移,这些网络变得越来越复杂,您必须处理的数据量巨大。因此,与所有软件程序一样,您无法准确预测软件的编程方式。并且拥有像CUDA这样的可编程架构,并且像我们创建的Tensor Cores一样针对AI进行了优化,这实际上是理想的架构。

我们也知道人工智能是我们这个时代最强大的技术力量。机器自己学习和编写软件以及编写人类无法编写的软件的能力非常特别。正如你们所看到的那样,人工智能的应用只是在每个行业中传播。因此,如果你愿意的话,我们对人工智能的思考方式就会如此。人工智能的第一波正在开发计算机体系结构,这是第一部分 - 当许多人发现我们是谁时,我们进入人工智能的高性能计算世界。第二波是将AI应用于云服务提供商或超大规模。他们有大量的数据。他们有很多消费者应用程序。其中许多都不是生命关键因素,因此,早期技术的应用 - 早期采用技术确实可行。所以你看到超大规模的人采用AI。对我们来说真正令人兴奋的事情超出了建议,超越了图像增强,我们认为人工智能最重要的应用可能是会话式人工智能的领域。大多数人都在谈论和提问,与他们的移动设备交谈,寻找某些东西或寻求方向,而不是有一个选项列表的页面,它会回答一个很可能是一个很好的答案。 AI的下一阶段就是我们所说的垂直行业企业AI。这就是公司使用它不仅仅是为了加速内部业务流程,而是使用AI来创建新产品和服务。它们可能是基于物联网的医疗仪器的新医疗仪器,用于监测您的健康状况。它可能是与应用程序相关的东西 - 用于预测或欺诈检测的金融服务。它可能是某种设备,为您提供比萨饼,交付机器人。物联网和人工智能的结合,实际上,您实际上拥有的软件功能可以充分利用您在世界各地所有这些传感器。这是下一阶段的增长。它影响了大型工业公司,运输公司,零售商的公司。医疗保健公司,你说出来。因此AI的增长阶段是我们即将进入的阶段。

And then the longer term is an industry that we all know to be extremely large, but it takes time because it’s life-critical, and it has to do with transportation. It’s a $100 trillion industry. We know it’s going to be automated. We know that everything that moves in the future will be autonomous or have autonomous capabilities. And that’s just a matter of time before we realize its full potential. And so the net of it all is that I believe that AI is the single most powerful technology force of our time, and that’s why we’re all in on it. And we know that acceleration and accelerated computing is the perfect model for that. And it started in the cloud, but it’s going to keep moving out into the edge and through data centers and enterprises and hopefully – well, eventually, all the way out into autonomous devices and machines in the real world. And so this is a big market, and I’m super enthusiastic about it.

然后从长远来看,我们都知道这个行业非常庞大,但需要时间,因为它对生命至关重要,而且与运输有关。 这是一个价值100万亿美元的行业。 我们知道它会自动化。 我们知道,未来发生的一切都将是自主的或具有自主能力。 在我们充分发挥潜力之前,这只是时间问题。 因此,我认为人工智能是我们这个时代最强大的技术力量,这就是为什么我们全力以赴。 我们知道加速和加速计算是完美的模型。 它始于云端,但它将继续走向边缘,通过数据中心和企业,并希望 - 最终,一直到现实世界中的自动设备和机器。 所以这是一个很大的市场,我对它非常热衷。

会议主持员

And your next question comes from the line of Toshiya Hari with Goldman Sachs.

而你的下一个问题来自Toshiya Hari与Goldman Sachs的合作。

Toshiya Hari

Hi guys. Thanks very much for taking the questions. I had two as well, one for Jensen and the other for Colette. Jensen, you guys called out inference as a significant contributor to growth in data center last quarter. I think you guys talked about it being a double-digit percentage contributor, curious what you saw from inference in the quarter. And more importantly, if you can talk about the outlook, both near term and long term, as it relates to inference, that’ll be helpful. And then secondly, for Colette, just want to double click on the gross margin question. The sequential improvement that you’re guiding to is a pretty significant number. So I was just hoping if you can kind of break it down for us in terms of overall volume growth mix dynamics, both between segments and within segments and also to the extent DRAM pricing is impacting that, any color on that will be helpful as well. Thank you.

嗨,大家好。 非常感谢您提出问题。 我有两个,一个用于Jensen,另一个用于Colette。 Jensen,你们推断推断是上季度数据中心增长的重要贡献者。 我想你们谈到它是一个两位数的百分比贡献者,好奇你在本季度的推论中所看到的。 更重要的是,如果你可以谈论近期和长期的前景,因为它与推理相关,那将是有帮助的。 其次,对于科莱特,只想双击毛利率问题。 您所指导的顺序改进是非常重要的数字。 所以我只是希望你能否在整体销量增长组合动态方面对我们进行细分,无论是细分市场还是细分市场之间以及DRAM价格影响的程度,任何颜色都会有所帮助。 谢谢。

Jen-Hsun Huang

Yes, Toshiya, I got to tell you, I’m less good at normal pre – near-term productions than I am good at thinking about long-term dynamics. But let me talk to you about inference. Our inference business is – remains robust. It’s double digits. It’s a large part of our business. And – but more importantly, the two dynamics that I think are near term and that’s going to drive growth, number one is interactive conversational AI, interactive conversational AI inference. If you simply ask a chat bot a simple question, where is the closest pizza and you would – pizza shop, and you would like to have a conversation with this bot, it would have to do speech recognition, it has to understand what it is that you asked about, it has to look it up in a recommender based on the locations you’re at, maybe your preferences of styles of pizza and the price ranges that you’re interested and how far you’re willing to go, to go get it. It has to recommend a pizza shop for you to go to. It has to then translate that from text-to-speech and then into human – a human understand a voice. And those models have to happen in just a few – ideally, a few hundred milliseconds. Currently, it’s not that. And it makes it really hard for these services to be deployed quite broadly and used for all kinds of different applications. And so that’s the near-term opportunity, it’s interactive conversational AI inference. And you could just imagine every single hyperscaler racing to go make this possible because recently, we had some important breakthroughs in machine learning language models. The BERT model that I mentioned earlier is really, really an important development, and it’s caused a large number of derivatives that has improved upon it and so near-term conversational AI inference. But, we are also seeing near term the inference at the edge. There are many types of applications where because of the laws of physics reasons, the speed of light reasons or the economics reasons or data sovereignty reasons, it’s not possible to stream the data to the cloud and have the inference done at the cloud. You have to do that at the edge. You need the latency to be low, the amount of data that you’re streaming is continuous. And so you don’t want to be paying for that line rate the whole time, and maybe the data is of great confidentiality or privacy. And so we’re seeing a lot of excitement and a lot of development for edge AI. Smart retail, smart warehouses, smart factories, smart cities, smart airports, you just make a list of those kind of things, basically locations where there is a lot of activity, where safety or cost or large amount of materials is passing through, you could just imagine the applications. All of those really want to be edge computing systems and edge inference systems. And so those are near term – two near-term drivers, and I think it’s fair to say that both of them are quite large opportunities.

是的,Toshiya,我得告诉你,我不太擅长正常的近期前期制作,而不是善于考虑长期动态。但是,让我谈谈推理。我们的推理业务是 - 保持强劲。这是两位数。这是我们业务的重要组成部分。并且 - 但更重要的是,我认为这两种动态是近期的,这将推动增长,第一是交互式会话AI,交互式会话AI推理。如果你只是问一个聊天机器人一个简单的问题,哪个是最接近的披萨,你会 - 披萨店,你想与这个机器人进行对话,它必须进行语音识别,它必须了解它是什么你问的问题,它必须根据你所在的位置在推荐人中查找,也许你喜欢的披萨风格和你感兴趣的价格范围以及你愿意走的距离,去实现它(梦想);去得到它(东西。它必须推荐一家比萨饼店去。然后它必须将其从文本转换为语言转换为人类 - 人类理解声音。而这些模型只需要几个 - 最好是几百毫秒。目前,它不是那样的。这使得这些服务很难被广泛部署并用于各种不同的应用程序。所以这是近期的机会,它是交互式会话AI推理。而你可以想象每一个超级赛车手都可以实现这一目标,因为最近,我们在机器学习语言模型方面取得了一些重大突破。我前面提到的BERT模型确实是一个非常重要的发展,并且它导致大量的衍生产品在其上进行了改进,因此近期的会话AI推理也是如此。但是,我们也看到了近期的边缘推断。有许多类型的应用程序,由于物理定律原因,光速原因或经济原因或数据主权原因,不可能将数据流式传输到云并在云端进行推理。你必须在边缘做到这一点。您需要延迟较低,您流式传输的数据量是连续的。因此,您不希望一直支付该线路费率,也许这些数据具有很高的机密性或隐私性。所以我们看到了边缘AI的很多兴奋和大量的发展。智能零售,智能仓库,智能工厂,智能城市,智能机场,您只需列出这类物品,基本上是存在大量活动的地方,安全或成本或大量物料通过的地方,您可以想象一下应用程序。所有这些都真的想成为边缘计算系统和边缘推理系统。所以这些是近期的 - 两个近期的驱动因素,我认为可以说两者都是相当大的机会。

Colette Kress

So to answer your question regarding gross margin in a little bit more detail, probably our largest area that we expect improvement in terms of our mix is our mix return regarding our overall gaming business. We expect to have a full quarter of our SUPER lineup within the next quarter including our RTX as well as our notebook becoming a bigger mix as well as it grows. These drivers are one of the largest reasons why we see that growth in our gross margin. We always think about our component cost, our overall cost of manufacturing, so this is always baked in over time, but we’ll continue to see improvements on that as well.

因此,为了更详细地回答您关于毛利率的问题,我们期望在我们的组合方面改善的最大区域是我们整体博彩业务的混合回报。 我们预计下一季度将有超过四分之一的SUPER阵容,包括我们的RTX以及我们的笔记本电脑成为更大的组合以及它的增长。 这些驱动因素是我们看到毛利率增长的最大原因之一。 我们总是考虑我们的组件成本,我们的整体制造成本,所以随着时间的推移,这总是很好,但我们也会继续看到它的改进。

会议主持员

And your next question comes from the line of Harlan Sur with JPMorgan.

而你的下一个问题来自于摩根大通的Harlan Sur系列。

哈伦苏尔

Good afternoon. Thanks for taking my question. Again on your data center business, many of your peers on the compute and storage side are seeing spending recovery by cloud and hyperscalers in the second half of this year after a similar weak first half of the year. You guys saw some growth in Q2 driven primarily by enterprise. It seems like you had some broadening out of the customer spending this quarter. Inferencing continues to see strong momentum. Would you guys expect that this translates into a double-digit percentage sequential growth in data center in Q3 off of the low base in Q2?

下午好。 谢谢你提出我的问题。 再次对您的数据中心业务而言,计算和存储方面的许多同行都看到了今年下半年云计算和超大规模公司在上半年出现类似疲软之后的支出复苏。 你们看到第二季度的增长主要是由企业推动的。 看起来您本季度的客户支出有所扩大。 推论继续看到强劲势头。 您是否预计这会导致第三季度数据中心的数据中心连续增长率达到两位数,低于第二季度的低基数?

Jen-Hsun Huang

Our hyperscale data center with a few customers don’t give us very much – we don’t get very much visibility from a handful of customers in hyperscale. However, we’re seeing broad-based growth and excitement in data centers. And the way to think about data center, our data center business consists of hyperscale training, internal training, hyperscale inference, cloud computing – and that’s hyperscale, and that cloud is a public cloud. And then we have vertical industry enterprise, what sometimes we call enterprise, vertical industry enterprise, it could be transportation companies, retailers, telcos, vertical industry adoption of AI either to accelerate their business or to develop new products and services. And then the – so when you look at our data center from that perspective and these pieces, although we don’t see as much – we don’t get as much visibility as we like in a couple of the large customers, the rest of the hyperscalers, we’re seeing broad-based growth. And so we’re experiencing the enthusiasm and the energy that maybe the others are and so we will keep updating you guys as we go. We will see how it goes.

我们的超大规模数据中心与少数客户并没有给我们太多关注 - 我们没有从超大规模的少数客户那里获得太多的可见性。但是,我们在数据中心看到了广泛的增长和兴奋。考虑数据中心的方式,我们的数据中心业务包括超大规模培训,内部培训,超大规模推理,云计算 - 以及超大规模,而云是公共云。然后我们有垂直的行业企业,有时我们称之为企业,垂直行业企业,它可能是运输公司,零售商,电信公司,垂直行业采用AI来加速他们的业务或开发新的产品和服务。然后 - 所以当你从这个角度来看我们的数据中心和这些部分时,虽然我们看不到那么多 - 我们在几个大客户中没有获得我们喜欢的可见性,其余的超大规模,我们看到了广泛的增长。所以我们正在体验其他人的热情和精力,所以我们会不断更新你们。我们看看事情会怎样。

会议主持员

And your next question comes from the line of Timothy Arcuri with UBS.

而你的下一个问题来自于瑞银与蒂莫西·阿库里的合作。

Timothy Arcuri

Thanks a lot. I had two. I guess first for Jensen, Volta’s been around now for about 2 years. Do you see signs of demand maybe building up ahead of the new set of nanometer products, whenever that comes out? I guess I’m just wondering whether there’s some element of this is more around product cadence that gets resolved as you do roll out the product. That’s the first question. And then I guess, the second question, Colette, is of the $300 million growth into October, it sounds like Switch is pretty flat, but I’m wondering if you can give us maybe some qualitative sense of where the growth is coming from, is it maybe like two-third gaming and one-third data centers, something like that? Thanks.

非常感谢。 我有两个。 我首先想到的是Jensen,Volta已经存在了大约2年。 您是否看到需求的迹象可能会在新的纳米产品出现之前形成,无论什么时候出现? 我想我只是想知道是否有一些元素更多地围绕产品节奏,当你推出产品时会得到解决。 这是第一个问题。 然后我想,第二个问题,Colette,是10月份增长3亿美元,听起来像Switch非常平淡,但我想知道你是否可以给我们一些定性的感觉来看增长的来源, 它可能像三分之二的游戏和三分之一的数据中心,类似的东西? 谢谢。

Jen-Hsun Huang

Well, Volta data center products can churn that fast. We gamers could churn products quickly because they’re bought and sold one at a time. But data centers data center infrastructure really has to be planned properly, and the build-out takes time. And we expect Volta to be successful all the way through next year. And software still continues to be improved on it. We’re still improving systems on it. And in fact, just 1 year in just 1 year, we improved our AI performance on Volta by almost 2x, 80%. And so, you could just imagine the amount of software that’s built on top of Volta and all the Tensor Cores and all the GPUs connected with NVLink and the large number of nodes that are connected to build supercomputers. The software of building these systems, large-scale systems, is really, really hard. And that’s one of the reasons why you hear people talk about chips, but they never show up because building the software is just an enormous undertaking. The number of software engineers we have in the company is in the thousands, and we have the benefit of having built on top of this architecture for over 1.5 decades. And so, when we’re able to deploy into data centers as quickly as we do, I think we kind of lose sight of how hard it is to do that in the first place. The last time a new processor entered into a data center was an x86 Xeon, and you just don’t bring processors in the data centers that frequently or that easily. And so, I think the way to think about Volta is that it’s surely in its prime, and it’s going to keep continue to do well all the way through next year.

那么,Volta数据中心产品可以快速流失。我们的玩家可以快速搅动产品,因为他们一次买卖一个。但数据中心数据中心基础设施确实必须正确规划,而扩建需要时间。我们希望Volta能够在明年一直取得成功。软件仍然在不断改进。我们仍然在改进系统。事实上,仅仅1年时间仅仅1年,我们将Volta的AI性能提高了近2倍,80%。因此,您可以想象在Volta和所有Tensor核心以及与NVLink连接的所有GPU以及连接到构建超级计算机的大量节点之上构建的软件数量。构建这些系统,大规模系统的软件确实非常困难。这就是为什么你听到人们谈论芯片的原因之一,但它们从未出现过,因为构建软件只是一项艰巨的任务。我们公司拥有的软件工程师数量达到数千人,而且我们拥有超过1.5年建立在这种架构之上的优势。因此,当我们能够像我们一样快速地部署到数据中心时,我认为我们首先忽略了这样做有多难。新处理器最后一次进入数据中心的是x86 Xeon,而您只是不经常或轻松地将数据中心的处理器带入数据中心。因此,我认为考虑沃尔特的方式是,它肯定处于鼎盛时期,并将在明年继续保持良好状态。

Colette Kress

In regard to our guidance on revenue, and we do guide in terms of the total. You have seen, in this last quarter, we executed a sequential increase really focusing on moving to a normalization of our gaming business. And we’re now approaching the second half of the year getting ready for the back to school and the holidays. So, you should expect also our gaming business to continue to grow to reach that full normalization by the end of Q3. We do expect the rest of our platforms to likely also grow. We have a couple different models on how that will come out. But yes, we do expect our data center business to grow, and then we’ll see on the rest of our businesses as well.

关于我们的收入指导,我们确实在总数方面提供指导。 您已经看到,在上一季度,我们实施了连续增长,真正专注于转向我们的游戏业务正常化。 我们现在正在接近今年下半年准备回到学校和假期。 因此,您应该期望我们的游戏业务在第三季度末继续增长以达到完全正常化。 我们确实希望其他平台也可能增长。 关于如何产生,我们有几种不同的模型。 但是,是的,我们确实希望我们的数据中心业务增长,然后我们也会看到其他业务。

会议主持员

Your next question comes from the line of Matt Ramsay with Cowen.

你的下一个问题来自Matt Ramsay与Cowen的合作。

马特拉姆齐

Thank you very much. Good afternoon. A couple of questions. I guess the first one is Jensen, if you have any, I guess, high-level qualitative commentary on how the new SUPER upgrades of your Turing platform have been received in the market and how you might think about them progressing through the year. And then, I guess, the second question is a bigger one. Intel’s talked quite openly about One API. The software stack at Xilinx is progressing with Versal ACAP. I mean you guys get a lot of credit for the decade of work that you’ve done on CUDA. But I wonder if you might comment on if you’ve seen any movement in the competitive landscape on the software side for the data center space. Thank you.

非常感谢你。 下午好。 几个问题。 我想第一个是Jensen,如果你有任何关于如何在市场上收到图灵平台的新SUPER升级的高级定性评论,以及你如何看待它们在全年的进展。 然后,我想,第二个问题是一个更大的问题。 英特尔公开谈论One API。 Xilinx的软件堆栈正在与Versal ACAP合作。 我的意思是你们在CUDA上完成的十年工作中获得了很多荣誉。 但我想知道你是否可以评论你是否看到过数据中心空间软件方面的竞争格局。 谢谢。

Jen-Hsun Huang

SUPER is off to a great start. Goodness, SUPER is off to a super start. And if you look at if you do channel checks all over, even though we’ve got a lot of products in the channel and we last quarter was a transitional quarter for us actually. And we didn’t we shipped SUPER later in the quarter. But because the entire ecosystem and all of our execution engines are so primed, we were able to ship a fair number through the channel. And so, and yet, if you do spot checks all around the world, they’re sold out almost everywhere. And the pricing in the spot market is drifting higher than MSRP. That just tells you something about demand. And so that’s really exciting. SUPER is off to a super start for and at this point, it’s a foregone conclusion that we’re going to buy a new graphics card, and it’s going to the last 2, 3, 4 years to not have ray tracing is just crazy. Ray tracing content just keeps coming out. And between the performance of SUPER and the fact that it has ray tracing hardware, it’s going to be super well positioned for throughout all of next year.
Your question about APIs and software programmability, APIs is just one of the issues. The large issue about processors is how do you program it. The reason why x86s and CPUs are so popular is because they solve the great challenge of software developers: how to program a computer and how to program a computer and how to compile for that computer is a paramount concern to computer science, and it’s an area of tremendous research. Going from single CPU to multi-core CPUs was a great challenge. Going from multi-core CPUs to multi-node multi-core CPUs is an enormous challenge. And yet, when we created CUDA in our GPUs, we went from 1 CPU core or one processor core to a few to now, in the case of large-scale systems, millions of processor cores. And how do you program such a computer across multi-GPU, multi-node? It’s a concept that’s not easy to grasp. And so, I don’t really know how one programming approach or a simple API is going to make 7 different type of weird things work together. And I can’t make it fit in my head. But programming isn’t as simple as a PowerPoint slide, I guess. And I think it’s just time will tell whether one programming approach could fit 7 different types of processors when no time in history has it ever happened.

SUPER是一个很好的开始。天哪,SUPER是一个超级开始。如果你看看你是否全部进行渠道检查,即使我们在渠道中有很多产品,我们上个季度实际上也是过渡季度。我们没有在本季度晚些时候发运SUPER。但是因为整个生态系统和我们所有的执行引擎都已经准备就绪,我们能够通过渠道发送一个公平的数字。然而,如果你在世界各地进行抽查,它们几乎在所有地方都销售一空。而且现货市场的价格漂移高于MSRP。这只是告诉你有关需求的事情。所以这真的令人兴奋。 SUPER是一个超级开始,在这一点上,我们将购买一个新显卡已成定局,而且最近2年,3年,4年没有射线追踪只是疯了。光线追踪内容不断涌现。在SUPER的性能和它具有光线跟踪硬件的事实之间,它将在明年的整个过程中处于非常好的位置。

关于API和软件可编程性的问题,API只是其中一个问题。有关处理器的大问题是如何编程。 x86和CPU如此受欢迎的原因是因为它们解决了软件开发人员面临的巨大挑战:如何编程计算机以及如何编程计算机以及如何编译计算机是计算机科学最重要的问题,它是一个领域大量的研究。从单CPU到多核CPU是一个巨大的挑战。从多核CPU到多节点多核CPU是一项巨大的挑战。然而,当我们在GPU中创建CUDA时,我们从1个CPU核心或一个处理器核心到现在,在大规模系统的情况下,数百万个处理器核心。那你如何跨多GPU,多节点编程这样的计算机?这是一个不易理解的概念。所以,我真的不知道一种编程方法或简单的API将如何使7种不同类型的奇怪事物协同工作。我无法让它适合我的脑袋。但我想,编程并不像PowerPoint幻灯片那么简单。而且我认为现在只是时间会判断一种编程方法是否适合7种不同类型的处理器,而历史上没有任何时间。

会议主持员

Your next question comes from the line of Joe Moore with Morgan Stanley.

你的下一个问题来自乔摩尔与摩根士丹利的合作。

乔摩尔

Great. thank you. I wonder if you could talk about the strength in the automotive business. Looks like the services piece of that is getting to be bigger, what’s the outlook for that part of the business? And can you give us a sense of the mix between services and components at this point?

非常好。 谢谢。 我想知道你是否可以谈谈汽车业务的实力。 看起来服务部分变得越来越大,这部分业务的前景如何? 您能否让我们了解服务和组件之间的混合?

Jen-Hsun Huang

Sure. Thanks, Joe. Our approach to autonomous vehicles comes in basically 2 parts. The first part is a full stack, which is building the architected processor, the system, the system software and all of the driving applications on top, including the deep neural nets. The second part of it, we call that a full stack self-driving car computer. The second part of DRIVE includes an end-to-end AV development system. For those who would like to use our processors, use our system software but create their own applications, we created a system that allows basically shares with them our computing infrastructure that we built for ourselves that allows them to do end-to-end development from deep learning development to the application of AV to simulating that application to doing regression testing of that application before they deploy it into a car. And the two systems that we use there is called DGX for training and Constellation for simulation and what is called Replay. And then the third part of our business model is development agreements, otherwise known as NRE. These 3 elements, full stack computer, end-to-end development flow and NRE project development product development consists of the overall DRIVE business. And so, although the cars will take several years to go into production, we’re seeing a lot of interest in working with us to develop self-driving cars using our development systems and entering into development projects. And so, we’re the number of autonomous vehicle projects is quite large around the world as you can imagine. And so, my sense is that we’re going to continue to do well here. The additional part of autonomous vehicles and where the capability has been derived and is going to seal up more near-term opportunities has to do with things like delivery shuttles, self-driving shuttles and maybe cargo movers inside walled warehouses. Those kinds of autonomous machines require basically the same technology, but it’s sooner and easier to deploy. And so, we are seeing a lot of excitement around that area.

当然。谢谢,乔。我们对自动驾驶汽车的方法基本上分为两部分。第一部分是完整堆栈,它构建了架构处理器,系统,系统软件和顶部的所有驱动应用程序,包括深度神经网络。第二部分,我们称之为全栈自动驾驶汽车电脑。 DRIVE的第二部分包括端到端AV开发系统。对于那些想要使用我们的处理器,使用我们的系统软件但创建他们自己的应用程序的人,我们创建了一个系统,它基本上可以与我们共享我们为自己构建的计算基础设施,允许他们从中进行端到端的开发。深度学习开发应用AV来模拟该应用程序在将应用程序部署到汽车之前对该应用程序进行回归测试。我们在那里使用的两个系统称为DGX用于训练,Constellation用于模拟和所谓的重放。然后,我们业务模型的第三部分是开发协议,也称为NRE。这三个要素,全栈计算机,端到端开发流程和NRE项目开发产品开发包括整个DRIVE业务。因此,尽管这些汽车需要几年的时间才能投入生产,但我们看到很多人有兴趣与我们一起使用我们的开发系统开发自动驾驶汽车并进入开发项目。因此,我们可以想象全世界的自动驾驶汽车项目数量非常庞大。所以,我的感觉是我们将继续在这里做得好。自动驾驶汽车的附加部分以及能力已经产生并将要封锁更多近期机会的部分与交付航天飞机,自动驾驶穿梭机以及可能在有围墙的仓库内的货物搬运机等事物有关。这些类型的自动机器需要基本相同的技术,但部署起来更快更容易。因此,我们看到围绕该领域的很多兴奋。

会议主持员

Your next question comes from the line of Aaron Rakers with Wells Fargo.

你的下一个问题来自Aaron Rakers和Wells Fargo的系列。

Aaron Rakers

Yes, thanks for taking the questions and congratulations on the improved performance. At your Analyst Day back a couple of months ago, you had highlighted the installed base opportunity for RTX. And I think at that point in time, you talked about 50% being Pascal base, 48% being pre-Pascal. You also alluded to the fact that you were seeing a positive mix shift higher in terms of the price points of this RTX cycle. So, I’m curious, where do we stand on the current product cycle? And what are you seeing currently as we go through this product cycle on the Turing platforms?

是的,感谢您对改进的性能提出问题和祝贺。 在几个月前的分析师日,您已经突出显示了RTX的已安装基础机会。 我认为在那个时间点,你谈到50%是Pascal基础,48%是帕斯卡前。 您还提到了这样一个事实,即您在RTX周期的价格点上看到了正向混合转移。 所以,我很好奇,我们在当前的产品周期中处于什么位置? 当我们在图灵平台上经历这个产品周期时,您目前看到了什么?

Jen-Hsun Huang

We launched well, first of all, the answer is that RTX adoption is faster than Pascal’s adoption if you normalize to time 0 of launch. The reason for that is Pascal launched top to bottom on the same day. And as you guys know, we weren’t able to do that for Turing. But if we did that for Turing, the adoption rate is actually faster. And to me, it’s a rather sensible. And the reason for that is because Pascal was basically DX12. And Maxwell was DX12. And Turing is the world’s first DXR, the first ray tracing GPU, brand-new functionality, brand-new API and a lot more performance. And so, I think it’s sensible that Turing’s adoption is going to be rapid. The second element of Turing is something that we’ve never talked about before. We’re mentioning it more and more because it’s such an exciting book market for us is notebooks. The install base of Pascal has a very, very little notebook in it. And the reason for that is because, in the past, we were never able to put a high-performance gaming GPU into a thin and light notebook until we invented Max-Q. And in combination with our energy efficiency, we were able to we’re now able to put a 2080 into a laptop, and it’s still beautiful. And so, this is effectively a brand-new growth market for us. And with so few people and so few gamers in the world that are able to game on a laptop, I think this is going to be a nice growth market for us.
And then the new market that we introduced and launched this last quarter is called RTX Studio. And this is an underserved segment of the market where consumers, enthusiasts, they could be artists that are working on small firms, they need powerful computers to do their work. They need powerful computers to do rendering and high-definition video editing. And yet it’s underserved by workstations because workstations are really sold on a B2B basis into large enterprises. And so, we aligned all of the OEMs and created a whole new line of notebooks called RTX Studio. And the enthusiasm has been great. We’ve launched 27 different laptops, and I’m looking forward to seeing the results of that. This is tens of millions of people who are creators. Some of them professionals, some of them hobbyists. And they use Adobe suites, they use Autodesk in their suites and some of them use SolidWorks and some of them use all kinds of renders, like blender. And these are 3D artists and video artists, and this digital content creation is the modern way of creativity. And so, this is an underserved market that we’re excited to go serve with RTX Studio.

我们发布得很好,首先,答案是如果你标准化为发布时间0,RTX采用比Pascal采用更快。原因是Pascal在同一天从上到下推出。正如你们所知,我们无法为图灵做到这一点。但如果我们为图灵做到这一点,采用率实际上更快。而对我来说,这是一个相当明智的。其原因是因为Pascal基本上是DX12。而麦克斯韦则是DX12。图灵是世界上第一款DXR,第一款光线追踪GPU,全新功能,全新的API以及更多性能。因此,我认为图灵的采用速度很快是明智的。图灵的第二个元素是我们以前从未谈过的东西。我们越来越多地提到它,因为对我们来说这是一个令人兴奋的图书市场是笔记本电脑。 Pascal的安装基础中有一个非常非常小的笔记本。其原因在于,在我们发明Max-Q之前,我们过去从未能将高性能游戏GPU放入轻薄笔记本中。结合我们的能源效率,我们现在能够将2080放入笔记本电脑中,它仍然很漂亮。因此,这对我们来说实际上是一个全新的增长市场。世界上很少有人能够在笔记本电脑上玩游戏的人很少,我认为这对我们来说是一个不错的增长市场。

然后我们在上个季度推出并推出的新市场名为RTX Studio。这是一个服务不足的市场,消费者,爱好者,他们可能是在小公司工作的艺术家,他们需要强大的计算机来完成他们的工作。他们需要功能强大的计算机来进行渲染和高清视频编辑。然而,工作站却没有为它提供服务,因为工作站实际上是以B2B为基础出售给大型企业的。因此,我们调整了所有OEM,并创建了一个全新的笔记本电脑系列,称为RTX Studio。而且热情非常高涨。我们推出了27款不同的笔记本电脑,我期待看到它的结果。这是数千万创作者。其中一些是专业人士,其中一些是业余爱好者。他们使用Adobe套件,他们在套件中使用Autodesk,其中一些使用SolidWorks,其中一些使用各种渲染,如blender。这些是3D艺术家和视频艺术家,这种数字内容创作是现代创作方式。因此,这是一个服务不足的市场,我们很高兴能够与RTX Studio一起服务。

会议主持员

And your last question comes from the line of Stacy Rasgon with Bernstein Research.

而你的最后一个问题来自Stacy Rasgon与Bernstein Research的合作。

Stacy Rasgon

Hi guys. Thanks for taking my questions. I have two for Colette. My first question is on data center. So, I know you say that you have a broad-based growth except for a few hyperscalers. But you only grew at 3% sequentially, about $20 million. That doesn’t sound like broad-based growth to me unless like did the hyperscalers get worse or are they just still so much bigger than like the rest of it? I guess, what’s going on in data center? How do I wrap my head around like broad-based growth with relatively minimal growth observed?

嗨,大家好。 谢谢你回答我的问题。 科莱特我有两个。 我的第一个问题是关于数据中心。 所以,我知道你说除了少数超大规模之外你还有广泛的增长。 但是你只有3%的顺序增长,大约2000万美元。 这对我来说听起来不像是广泛的增长,除非像超大规模的人变得更糟或是否仍然比其他人更大? 我想,数据中心发生了什么? 如何以宽幅增长的方式包裹我的头,观察到相对最小的增长?

Colette Kress

So, to answer your question here, Stacy, on what we refer to when we’re discussing the broad-based growth is the substantial expansion that we have on the types of customers and the industries that we are now approaching. As you know, even a year ago, we had a very, very small base in terms of industry-based hyper excuse me, industry-based AI workloads that they were using. Over this last quarter, we’re continuing to see strong growth as we roll out all different types of AI solutions, both across the U.S. and worldwide, to these overall customers. Our hyperscalers, again, a couple of them, not necessarily growing, some of them are flat and some of them are growing depending on whether or not that’s for cloud instances or whether or not they’re using it for internal use. So, we believe that our continued growth with the industries is important for us for the long term to expand the use of AI and we are just really pleased with what we are seeing in that growth this quarter.

所以,在这里回答你的问题,Stacy,就我们讨论广泛增长时所提到的内容而言,我们对客户类型和我们正在接近的行业进行了大幅扩展。 如你所知,就在一年前,我们在基于行业的超级借口,他们正在使用的基于行业的AI工作负载方面有一个非常非常小的基础。 在上一季度,随着我们在美国和全球范围内为这些整体客户推出所有不同类型的人工智能解决方案,我们将继续保持强劲增长。 我们的超大规模制造商,其中一些,不一定增长,其中一些是平的,其中一些正在增长,这取决于云实例是否适用于它们是否用于内部使用。 因此,我们相信,我们对行业的持续增长对我们来说对于我们长期扩大人工智能的使用非常重要,我们对本季度增长的看法非常满意。

会议主持员

I’ll now turn the call back over to Jensen for any closing remarks.

我现在将把这个电话转回Jensen的任何结束语。

Jen-Hsun Huang

Thanks, everyone. We are happy with our results this quarter and our return to growth across our platforms. Gaming is doing great. It’s great to see NVIDIA RTX reinvigorating the industry. GeForce has several growth drivers. Ray traced games continue to gain momentum. A large number of gaming laptops are rolling out, and our new Studio platform is reaching the large underserved community of creators. Outside a few hyperscalers, we’re seeing broad-based growth in data centers. AI is the most powerful technology force of our time and a once-in-a-lifetime opportunity. More and more enterprises are using AI to create new products and services while leveraging AI to drive ultra efficiency and speed in their business. And with hyperscalers racing to harness recent breakthroughs in conversational AI, we see growing engagements in training as well as interactive conversational inference. RTX, CUDA accelerated computing, AI, autonomous vehicles, the work we’re doing is important, impactful and incredibly fun. We’re just grateful there is so much of it. We look forward to updating you on our progress next quarter.

感谢大家。我们对本季度的业绩以及我们在平台上的回归增长感到满意。游戏很棒。很高兴看到NVIDIA RTX重振整个行业。 GeForce有几个增长动力。雷追踪游戏继续获得动力。大量的游戏笔记本电脑正在推出,我们新的Studio平台正在覆盖大型服务欠缺的创作者社区。除了一些超大规模的人之外,我们在数据中心看到了广泛的增长。人工智能是我们这个时代最强大的技术力量,也是千载难逢的机遇。越来越多的企业正在利用人工智能创造新的产品和服务,同时利用人工智能来提高企业的效率和速度。随着超大规模人员竞相利用最近在会话AI方面的突破,我们看到越来越多的人参与培训以及互动式会话推理。 RTX,CUDA加速计算,人工智能,自动驾驶汽车,我们正在做的工作是重要的,有影响力和令人难以置信的乐趣。我们非常感激有这么多。我们期待在下个季度向您介绍我们的进展情况。

会议主持员

This concludes today’s conference call. You may now disconnect.

今天的电话会议结束了。 您现在可以断开连接。

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