同一 GPU，多出 50% 产出：为什么运营能力至关重要 | SemiAnalysis Oliver Kennon

简要说明Brief Description

同一款 GPU，输出提升 50%：为什么算子至关重要——Oliver Kennon，SemiAnalysisSame GPU， 50% More Output: Why the Operator Matters — Oliver Kennon， SemiAnalysis

目录Table of Contents

• 简介
  Intro
• 计算资源是大宗商品市场。
  Compute is a commodity market
• GPU 小时是投入，智能是产出
  GPU hours are the input, intelligence is the output
• 同款 B200，输出量提升约 50%
  Same B200, ~50% more output
• 什么才算有用的情报
  What counts as useful intelligence
• 使用 ClusterMAX 为运营商评级
  Rating operators with ClusterMAX
• 租赁价格与生产成本
  Rental price vs. production cost
• 你的成本定位是相对的
  Your cost position is relative
• 对可比计算能力进行评分
  Grading comparable compute
• 计算期货可以对冲哪些风险
  What compute futures can hedge
• 要点
  Takeaways

简介Intro

主持人：感谢大家今天抽出时间来到这里。我叫 Oliver Cannon，是 SemiAnalysis 的研究分析师。嗯，时间和注意力是我们每个人最宝贵的东西。当然，除非你是 OpenAI 或 Anthropic 的早期投资者。嗯，话不多说，我们开始吧。并非所有 GPU 的部署方式都一样。GPU 的表现取决于操作者的水平。嗯，购买硬件只是获得了潜力。实际能交付多少有用的工作，取决于运营能力。这就引出了今天我将和大家一起探讨的问题：谁能以最低成本提供你所需的智能？成本很重要。嗯，在今天的演讲中，我想向大家展示运营能力为何以及如何改变成本，以及这对计算这一大宗商品市场为何重要。Host: Thank you everyone for taking the time to be here today. My name is Oliver Cannon. I'm a research analyst at SemiAnalysis. Um time and attention are the most valuable things any of us have. Uh unless of course you're an early stage investor in OpenAI or Anthropic. Um with that, uh let's get into it. Uh not all GPUs are deployed the same. A GPU falls to the level of the operator. Um buying hardware gives you potential. Uh operating capability is what determines how much useful work you actually are able to deliver. Uh that leads us to the question I will attempt to answer with all of you today. Who can produce the intelligence you need at the lowest cost? Cost matters. Uh who uh in today's presentation I want to show you guys why and how operating capability changes that cost and why it matters in the commodity market that is compute.

计算资源是大宗商品市场的一部分Compute is a commodity market

大宗商品市场中什么最重要？嗯，很多科技投资者或技术人员可能不熟悉这个框架。嗯，简单介绍一下。在大宗商品市场中，生产者竞相交付可比的产出。嗯，较低的生产成本意味着价格下跌时有更大的竞争空间。这就是成本曲线所展示的内容：生产者按照交付相同可用工作成果所需的支出排列。如果两家生产者获得相同的价格，交付这项工作成果时花费较少的一家就能留下更多收益。很简单，对吧？对我们来说，智能是我们关注的经济产出。最终，我们的北极星指标将是以所需质量成功完成任务的单价。嗯，不幸的是，我们现在还做不到这一点。要把它标准化相当困难。嗯，如今，每个 token 的价格是一个实用且有用的近似指标，尤其适用于工作负载和质量要求可比的情况，也能让我们更接近衡量实际交付的产出。What matters in a commodity market? Um a lot of tech investors or technologists may not be familiar with this framework. Um so a little crash course. Uh in a commodity market producers compete to deliver comparable output. Uh a lower production cost gives more room to compete when prices fall. Uh that's what a cost curve shows. Producers lined up by what they spend to deliver the same usable work. If two producers earn the same price, the one spending less to deliver the work gets to keep more of it. Pretty simple, right? For us, intelligence is the economic output we care about. Eventually, our North Star metric will be price per successfully completed task at the required quality. Uh unfortunately, we're not quite there yet. That's a pretty hard thing to standardize. Um today price per token is a useful practical proxy, especially for comparable workloads and quality requirements, and gets us closer to delivered output.

呃，比经常被引用的那个价格要接近得多——你们这些搞金融的肯定都听说过 GPU 每小时的美元价格。嗯，或者按我们的看法，这是一种成本。我们把 GPU 小时视为投入的衡量指标，而不是产出的衡量指标。Uh much closer than the often quoted uh all you finance bros out here will have heard this uh GPU uh dollar per hour price. Um or as we view it, a cost. Um We view GPU hours as a measure of input, not output.

GPU 小时是投入，智能是产出。GPU hours are the input, intelligence is the output

成本与价格的区别。客户支付的价格不同于生产方完成这项工作的成本。GPU 小时是投入，智能是产出。在这张幻灯片上，我们可以直观地展示创造有用智能所需的投入。对于这次演示，我们从硬件和电力（GPU）开始。软件和运维可以看作是前两项投入的乘数，最终产出则是有用的智能。运营方负责选择并部署为模型提供服务的软件，并确保请求在系统中顺畅流转。机器可能已经就绪，但你仍然需要有人让它们产出客户真正能用的响应。关键在于你能获得多少有用的工作成果，以及这些成果是否符合指定任务的要求。即使使用同类芯片，转化效率也可能不同。The cost versus price distinction. Customer prices are different from the producer's cost of doing the work. GPU hours are the input, intelligence is the output. Uh on this slide, we can visualize the inputs needed to create useful intelligence. Uh we start with hardware and electricity, GPUs uh for this presentation. Software and operations can be thought of as a multiplier on those first two inputs, with the end product being useful intelligence. The operator is who chooses and implements the software that serves the model and keeps requests moving through the system. The machines might be available, but you still need someone to make them deliver responses customers can actually use. What matters is how much useful work you get and whether it meets your requirements for the specified task. The conversion can differ with the same kind of chip.

让我们看看这个经过测量的示例。Let's look at that in the measured example.

同样的 B200，输出量提升约 50%Same B200, ~50% more output

这里有一个来自我们公开的 InferenceX 计算器的具体例子。大家都可以去看看，网址是 semi-analysis.com。嗯，两种估算都采用 B200 GPU，运行同一个模型，输入大约 8,000 个 token，输出 1,000 个 token，这是相当标准的比例。P50 的目标是每位用户每秒生成 100 个输出 token。嗯，服务栈就是负责处理模型响应的软件。在 P90 场景下，其中一个服务栈每秒大约能生成 200 个输出 token，另一个则能生成约 300 个。这差别很大，取决于软件、设置以及正在处理的请求数量，而这些都是运营方做出的选择。同一款芯片、相同的交互性目标，输出却多了约 50%。这可不是个小数目。那么，这对买家的账单意味着什么？这类智能服务实际上会让客户付出多少成本？Here's a concrete example from our InferenceX public InferenceX calculator. You guys can all check it out, semi-analysis.com. Uh both estimates use B200 GPUs serving the same model with roughly 8,000 input tokens and 1,000 output tokens, a pretty standard ratio. The P50 target is 100 output tokens per second for each user. Uh the serving stack is the software handling the model's responses. In the P90 scenario, one of these stacks gives about 200 generated output tokens per second, while the other one gives about 300. That's a big difference and comes down to the software, settings, and number of requests running. All choices made by the operator. Same chip, same target interactivity, about 50% more output. That's a lot. So, what does that mean for the buyer's bill? What does that intelligence actually cost the customer?

在这个例子中，我们假设两种部署的每小时费用相同，而且都满足你的要求。输出量大约是原来的 1.5 倍，因此你每获得一个合格 token 所需的租用费用就相应降低了。也就是说，同样一小时的费用，能换来更多可用且有用的输出。For this example, let's assume the hourly bill is the same for both deployments and both meet your requirements. At roughly 1 and 1/2 times the output, your rental spend per acceptable token is proportionally lower. That is to say, the hour buys you more usable and useful output.

什么才算有用的情报What counts as useful intelligence

什么才算有用的智能？有用的智能必须能完成你雇它来做的工作。很简单，对吧？答案需要足够好，能胜任任务；需要足够快地给出；还需要在你需要时随时可用。你的信息也必须始终处于你们约定的访问边界之内。也就是说，它必须安全。这是一项常被忽视的要求，芯片的性能指标并未将其纳入考量，但它对芯片能否投入使用、是否真正有用至关重要。在我们发表的安全研究中，我们发现监控访问权限会暴露其他客户的日志和指标。这是一个案例，而且不幸的是，这类情况相当常见：GPU 的速度很快，而我提到的这个案例中，它们快得惊人，这反而让整件事更令人难过。呃，可这项服务依然彻底失败了。What counts as useful intelligence? Useful intelligence has to do the job you hired it to do. Pretty simple, right? The answer needs to be good enough for the task, arrive quickly enough, and be available when you need it. Your information also needs to stay within the access boundaries you've agreed upon. AKA, it needs to be secure. This is an overlooked requirement that is not captured in the chip's performance metrics, but it is essential for a chip to be usable and useful. In our published security work, we found monitoring access that exposed another customer's logs and metrics. This is a case, and an unfortunately common one at that, where the GPUs were fast, and in the case I'm referencing, really, really fast, which makes the whole thing sadder. Uh and the service still failed horrifically.

嗯，确保集群安全需要持续投入，达到承诺的性能也一样。在大多数情况下，这项责任由运维方承担。总得有人让系统在生产环境中正常运行。假设现在有一个速度更快的服务更新可用，就需要有人在客户的工作负载上对它进行测试，检查输出是否仍符合要求，然后决定何时、何地以及如何部署。这意味着要检查实际响应、在负载下进行测试，并确保保留回退到先前版本的方式，以便在更新部署后出现问题时能够回退。部署完成后，还需要有人负责维护，并在出现问题时作出响应。这项工作可能由你的服务提供商、你自己的团队，或双方共同承担。无论如何，你都需要明确谁负责，以及他们是否确实有能力做好这项工作。Um ensuring cluster security takes ongoing work, just as delivering the promised performance does. In most cases, it is the operator that owns that responsibility. Someone has to make the system work in production. Suppose a faster serving update becomes available. Someone has to test it on the customer's workload, check whether the output still meets the requirements, and then decide when, where, and how to deploy it. That means checking the actual responses, testing it under load, and keeping a way to return to previous version if the update causes problems once it's deployed. Once it is deployed, someone has to maintain it and respond when issues come up. That work may sit with your provider, your own team, or both. Irregardless, you need to know who's responsible and whether they can actually do it.

使用 ClusterMAX 对运营商进行评级Rating operators with ClusterMAX

那么，我们如何评估运营方？嗯，SemiAnalysis 的 ClusterMax 目前已经发展到 3.0 版本，用来评估托管 GPU 集群的运营能力，目前覆盖了两百多家，而且还在增加。如果你想加入 ClusterMax 4.0，欢迎联系我们。嗯，我们会考察资源配置、可靠性、监控和安全性。实操测试、文档以及客户反馈，都有助于我们了解服务的运行情况——不仅是在一切顺利的时候，也包括机器发生故障时。我想借此机会重申，ClusterMax 的评级适用于托管集群，与裸金属服务不同，两者差异很大。嗯，在裸金属服务中，更多运营工作通常由客户承担。在这里套用托管服务评级，并不意味着评级自动涵盖服务提供商销售的所有产品。各个等级是相对于同行而言的。So, how do we assess the operator? Uh SemiAnalysis ClusterMax, we're in version 3.0 at this point, uh assesses operating capability on managed GPU clusters, couple hundred, uh growing. Please reach out if you want to be on ClusterMax 4.0. Um we look at provisioning, reliability, monitoring, and security. Uh hands-on testing, documentation, and customer's feedback help us understand how the service runs, not just on a good day, but also when a machine fails. I want to take the chance to reiterate that ClusterMax ratings apply to managed clusters, uh different from bare metal, very different. Um with bare metal, more operating work usually sits with the customer. Uh applying a managed service rating there does not automatically cover every product a provider sells. The tiers are relative to peers.

有点绕口。呃，我说得很到位，对此挺满意的。呃Little bit of a tongue twister. Uh I nailed it, pretty happy with that. Uh

嘉宾：定价和可用性也是这项评估中的重要因素。呃，这些指标并不直接衡量令牌输出，呃，但却极其重要。显然，非常重要。呃，评估有助于我们了解谁能够交付。现在我们可以进入下一个问题。Guest: and pricing and availability are also important factors in this assessment. Uh these are metrics that do not directly measure uh token output, uh but are incredibly important. Obviously, very important. Uh assessment helps us understand who can deliver. Now we can move on to the next question.

租赁价格与生产成本Rental price vs. production cost

生产有用的智能究竟要花多少钱？我们先从计算费用以及生产者仍需承担的其他成本说起。租赁价格和生产成本并不相同。在我们的例子中，GPU 提供商出租 GPU 使用时长，推理服务生产者购买这些时长并出售响应。租赁费用是提供商的收入，也是推理服务生产者的投入成本。GPU 所有者要支付设备、电力、场地和支持服务的费用。推理服务生产者的全部成本包括租金，以及租赁费用之外的软件、员工和其他服务支出。如果推理服务生产者改为拥有自己的 IT 设备，我们就要将设备投资分摊到其使用寿命期间，并计入运营成本。生产者收取的费用和支出的成本是两个不同的数字。What does it actually cost to produce useful intelligence? Let's start with a compute bill and other costs that producers still has to cover. Rental price and production cost are different. In our example, GPU provider the GPU provider rents out hours, the inference a producer buys those hours and sells responses. The rental bill is revenue to the provider and an input cost to the inference producer. The GPU owner pays for the equipment, power, space, and support. The inference producer's full cost includes rent plus whatever software, staff, and other services it pays for beyond that rental. If the inference producer owns its own it equipment instead, we spread the equipment investment over its working life and add running costs. What the producer charges and what it spends are separate numbers.

要理解有效智能的成本，我们需要知道这些支出实际产生了多少有用的工作。呃，更好的运营能降低有效智能的成本。B200 的对比说明了产出量为何重要。相同的付费时长内，更多可接受的回答可以让推理服务提供商的租赁费用分摊到更多有用的工作上。对于拥有自有 GPU 的服务提供商，同样的道理也适用：设备投资和运行成本可以分摊到更多产出上。统计同一时期内提供服务所需的全部支出，然后除以客户实际能够使用的产出，就能得出可比工作负载下每个可接受 token 的成本。更好的运营也需要投入。测试更新、安全地部署更新，呃，以及让更新持续正常运行，都需要人力和资金。To understand the cost of useful intelligence, we need to know how much useful work the spending actually produces. Uh better operations lowers that cost of useful intelligence. The B200 comparison showed why the amount of output matters. More acceptable responses from the same paid hours can spread the inference producer's rental expense over more useful work. For a producer that owns its GPUs, the same logic spreads the equipment investment and running costs over more output. Count all the spending needed to deliver the service over the same period, then divide by the output customers can actually use. That gives us cost for acceptable token for a comparable workload. Better operations also require investment. Testing an update, deploying it safely, uh and keeping it working take people and money.

额外的产出必须足以证明额外支出的合理性。嗯，当团队随着软件和工作负载的变化反复开展这项工作时，这种优势可能持续存在，但一次出色的基准测试并不能证明这一点。你必须每天都赢得这种优势。每个新的工作负载仍然需要进行验证，但据我们观察，可重复的运营流程能够支撑持久的成本优势，也能带来价格优势。不过，即便你持续改进，竞争对手也不会停滞不前。The extra output has to justify the extra spending. Uh this advantage can persist when a team repeats that work as software and workloads change, but one good benchmark doesn't establish it. You have to earn it every single day. Uh each new workload still needs checking, but a repeatable operating process, from what we've observed, can support a lasting cost advantage and a price advantage, too. Uh even if you do keep improving, though, your competitors are not sitting still.

你的成本地位是相对的。Your cost position is relative

呃，你在成本曲线上的位置是相对的。这一页在所有幻灯片中，应该能清楚地说明商品化框架在整个讨论中的重要性。嗯，你在成本曲线上的位置取决于竞争对手能做到什么，而不仅仅取决于你自己。这是一个相对指标。我们来看一个简单的假设例子。我们用虚构的成本单位来比较同一项任务和相同的质量要求。呃，我喜欢把这些单位想象成金条。呃，你每成功完成一项任务的全成本从10降到了8。同期，你竞争对手的成本从6降到了12。你进步了。太棒了。但你们双方都有进步。在这个例子中，你起初是成本更低的生产者，最终却变成了成本更高的生产者。只要客户认为这两项服务同样令人满意，这就给了竞争对手更大的降价空间。Uh your position on the cost curve is relative. This slide, out of all the slides, should make clear the importance of the commodity framework in all of this. Um your position on the cost curve depends on what your competitors can deliver, not just yourself. It's a relative measure. Here's a simple hypothetical. We're comparing the same task, the same required quality, using made-up cost units. Uh I like to imagine bars of gold. Uh your full cost per successful task falls from 10 to 8. Uh in the same period, your competitor's falls from 6 to 12. You improved. That's awesome. But both of you did. And in this example, you started as the cheaper producer and ended as the more expensive one. That gives the competitor more room to lower its selling price, provided customers find the two services equally acceptable.

嗯，变得更好并不意味着你就会自动领先。运用大宗商品框架，会让你意识到，必须相对地比较每单位有效工作的成本，也就是将自己的产品与竞争对手能够提供的产品进行比较。在今天这样的时期，这种比较很容易被忽视：需求远远超过供应，而且在可预见的未来仍将如此。嗯，未来两到三年的情况都很明朗。很好。稀缺性可能会掩盖这些差异。当需求旺盛、供应受限时，成本迥异的生产商都能找到客户。强劲的营收会让这些差异显得没那么紧迫。如果你是 CEO，当你可以去建造一座大型新数据中心时，为什么还要把宝贵的时间、精力和资金花在巩固自己在成本曲线上的位置上呢？Uh getting better does not automatically move you ahead. Applying the commodity framework makes you realize that you have to compare your cost per useful work on a relative basis. Comparing your own product with what your competitors can deliver. That comparison can be easy to overlook in moments like today, where demand far outstrips supply and will for the foreseeable future. Uh two to three years visibility. Good. Scarcity can obscure those differences. When demand is strong and supply is constrained, producers with very different costs can find customers. Strong revenue can make those differences feel less urgent. If you're a CEO, why would you focus on your precious time and energy and money shoring up your spot on the cost curve when you can be building a massive new data center?

呃，在这样的时刻，重要的是退一步看问题，保持一点客观视角。呃，从历史的角度来看。人类历史上的每一种大宗商品，供需都经历过波动。只是我们现在还看不出这种波动何时会出现，并不意味着这轮周期会有所不同。呃，除非你是彻头彻尾的 AI 最大主义者——我们当中确实有人是。所以，我完全愿意承认自己可能错了。我每天都会犯错。嗯，但我想说，如果你的企业背负着债务，就尤其需要对此保持警惕。主动建立成本优势，呃，也许没有扩大营收那么光鲜，但如果你想在一个商业周期中活下来，这一点至关重要。呃，这就是为什么你应该趁着还有机会、趁着风和日丽、还能融资的时候，建立自己的运营能力和成本优势。Uh in moments like this, it's important to step back and have a little perspective. Uh take a little historical perspective. Uh every commodity in the history of humanity has had swings in supply and demand. Just because we don't see when that will be yet does not mean that this cycle will be any different. Uh unless you're a total AI maxi, which some of us are. So, I'm very open to being wrong. I'm wrong every day. Um but I would say if you're a business with leverage, you need to be especially conscientious of this. Being proactive with building your cost advantage uh might be not be as sexy as making your top line bigger, but it matters deeply if you want to survive through a business cycle. Uh that's why you want to build your operating capability and cost advantage while you have the opportunity uh when the sun is shining, when you can raise capital.

呃，当客户有更多选择、价格承压时，它们就更加重要。那么，我们如何明确买家实际能获得什么呢？Uh they matter even more when customers have more choices to make and prices come under pressure. So, how do we make clear what uh the buyer actually gets?

对可比算力进行评分Grading comparable compute

可比的算力需求需要明确的等级。现在，我们来看看智能生产者购买的算力。我们已经看到，同一款芯片在不同的运行方式下会产生不同的结果。比如，两个 B200 GPU 小时服务的报价可能包含不同的服务，也可能将不同的运维工作留给客户。商品化框架需要一种方式来描述这些差异，让买家知道哪些服务可以视为可比。这正是等级的作用。等级描述特定的算力服务，以及买家可以对它抱有什么预期。它并不意味着所有任务中的智能都可以互换。定义好这项服务之后，我们就可以探讨它的租赁价格能说明什么。租赁价格基准衡量的是什么？租赁价格基准概括了其所界定市场内 GPU 小时的价格。Comparable compute needs clear grades. Now, let's look at the compute uh that an intelligence producer is buying. We've already seen that the same chip can deliver different results depending on how it's operated. Uh in two offers for B200 GPU hours can include different services and leave different operating jobs with the customer. A commodity framework needs a way to describe these differences so buyers know which services they can treat as comparable. That's the job of a grade. The grade describes a particular compute service and what its buyers can expect. It doesn't make intelligence interchangeable across every task. Once we've defined that service, we can ask what its rental price tells us. What does a rental price benchmark measure? Uh a rental price benchmark summarizes the price of GPU hours within its defined market.

它并不是生产者的生产成本，也不是对所交付智能水平的衡量。我们需要知道这些价格是如何形成的。广告报价和已完成的交易并不是一回事。服务范围、合同条款、采样了谁的价格以及这些价格如何汇总，都会影响这个参考点。呃，运营质量可能会影响这些价格，但指数并不会单独衡量运营质量。运行一个租赁参考指数，并不能告诉你部署实际上能产生多少有用的工作成果。若要将它用作对冲工具，你需要了解它与自己实际支付或赚取的租金有多大程度的对应关系。It isn't the producer's production cost or a measure of intelligence delivered. We need to know where those prices come from. An advertised offer and a completed trade are not the same thing. Service scope, contract terms, uh whose prices are sampled and how they're combined all shape that reference point. Uh operating quality can influence those prices without the index measuring it separately. Running a rental reference doesn't tell you how much useful work your deployment will produce. To use it as a hedge, you need to understand how it tracks the rent you actually pay or earn.

算力期货可以对冲哪些风险What compute futures can hedge

呃，算力期货能对冲什么？最近大家对算力期货非常关注。对于不了解的人来说，期货合约会为未来交割或现金结算设定标准化条款。现金结算取决于一个价格基准。买卖双方都面临租赁价格的不确定性。回想一下服务栈和监控故障。评估系统由谁运行，有助于描述买家获得的服务。这有助于我们定义等级，让买家知道哪些算力服务可以相互比较。基准还需要追踪他们实际支付或赚取的租赁费用。这就是我提出这一观点的原因。对运营方进行评级，是构建可信算力期货市场的重要基础。评级有助于这项工作，但它本身并不能定义期货等级、建立可靠的基准，也无法创造足够的交易量，让买卖变得轻松。Uh what can compute futures hedge? There's been a lot of excitement lately about compute futures. For all of you that don't know, a futures contract sets a standardized term for future delivery or cash settlement. Cash settlement depends on a price reference. Buyers and sellers both face uncertainty about rental prices. Think back to the serving stacks and the monitoring failure. Assessing who runs the system helps describe the service buyers get. That helps us define grade so buyers know which compute services are comparable. The benchmark then needs to track the rent they actually pay or earn. That's why I make this argument. Rating the operator is an essential building block for building a credible compute futures market. A rating informs that work. It doesn't by itself define a futures grade, establish a sound benchmark, or create enough trading to buy and sell easily.

这也未必能让对冲有效。租赁价格对冲可以帮助抵消租赁价格的变动，但无法弥补部署失败，也不能确保客户会付款。收费高于指数本身并不意味着对冲效果不好。关键在于参考指标的变化是否能跟上你的租金变化。两者关系出现意外变化，就会带来实际的价格风险。这些工具可以帮助你了解自己买的是什么，并管理租赁价格风险。对于情报产品的生产者来说，租金只是成本的一部分。运营方仍须将这些算力转化为客户真正能用的工作成果。至此，我们又回到了起点。It also doesn't necessarily make a hedge effective. A rental price hedge can help offset rental price changes. It does not fix a failed deployment or ensure you that your customer pays. Charging more than the index does not by itself make it a bad hedge. What matters is whether changes in the reference track changes in your rent. Unexpected changes in that relationship leave real price risk. Those tools can help you understand what you're buying and manage the rental price risk. For an intelligence producer, rent is one part of the cost. The operator still has to turn that compute into work that the customer can actually use. This brings us full circle.

要点Takeaways

所以，GPU 的表现最终会受限于操作者的水平。那么，谁能以更低的成本提供你所需的智能？以最低成本？能够反复满足你的要求，并以最低的单项成功完成任务总成本做到这一点的生产者。把交付服务所花费的一切都计算在内，再除以客户实际能够使用的产出。运营能力会影响这个等式的两端：你花费多少，以及你能交付多少额外的有用工作。此外，你的处境还取决于竞争对手的处境。即使你有所进步，如果竞争对手进步得更快，你仍可能落后。这就是为什么要在整个周期中了解运营者，而不是只看某个时间点。呃，尤其是在客户有更多选择、价格压力不断加大的时候。总之，如果你刚进来，只想听个简短版，那就是这样。So, a GPU falls to the level of the operator. So, who can produce the intelligence you need at the lower cost? At the lowest cost? The producer who can repeatedly meet your requirements at the lowest full cost per successfully completed task. Count everything spent to deliver the service, then divide by the output that the customer can actually use. Operating capability affects both sides of that equation, what you spend and how much more useful work you can deliver. Also, your position depends on that of your competitors. You can improve and still fall behind if your competitors improve faster than you. That's why understanding the operator matters through the cycle, not just at one point in time. Uh especially when customers have more choices and prices come under more pressure. In conclusion, if you just walked in and want the TLDR, this is it.

GPU 会降至运营商的层级。助力打造一个信息更充分的计算市场。女士们、先生们，非常感谢大家今天抽出时间。我们在 SemiAnalysis 深知，市场不会自然而然地变得信息充分、高效。我们希望积极参与其中，成为推动这项工作的伙伴。不过，独舞难成舞。理想情况下，参与者还不止两个。如果您愿意与我们合作、贡献力量或加入团队，我们非常期待您的联系。我们一直在招募优秀人才，欢迎与我们联系。如果这番话引起了您的共鸣，欢迎发送邮件至 sales@semianalysis.com，了解合作机会。如果您有意加入团队，可以发邮件给我：kennon@semianalysis.com，拼写是 k e n n o n。感谢大家收听，祝大家今天接下来的时间过得愉快。A GPU falls to the level of the operator. Help build a better informed compute market. Ladies and gentlemen, thank you so much for your time today. At SemiAnalysis, we know that markets do not become informed and efficient on their own. We seek to be an active engaged partner in that work. It takes two to tango though. Uh ideally more than two actually. Um If you'd like to partner with us, uh contribute, or join the team, we would love to hear from you. We are always hiring great people. Please reach out. Um If this resonated, uh please go ahead and email sales@semianalysis.com uh for partnership opportunities. Uh and if you're interested in potentially joining the team, my email is kennon@semianalysis.com, k e n n o n. Um thank you all for listening and have a fantastic rest of your day.