Muse 成本测算：每 DAU 每月约 20 美元，重度用户 CPU 需求或达 3–5 倍

FUNDA @FundaAI原推发布于 2026.09.28

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Happy to be part of the Peer Review Group! @FredaDuan’s framework makes sense: separate the sandbox from inference, and size physical resources around concurrent usage. But the most interesting part, as we discussed in our earlier tweet on Muse’s ROI, is how the workload itself could evolve. Based on today’s workload, we estimate Muse costs $20 per DAU per month, and expect that to fall to around $10 soon. But today’s workload could also change quickly as users delegate more work. The good news is that if personal agents have that much work to do, they’re likely delivering a lot of value. Today, most tasks are fairly self-contained: book a flight, negotiate a bill. Over time, an agent could manage your inbox, follow a project for weeks, plan a family trip, or keep track of several purchasing and investment decisions. Each task could involve more websites, more files, more tool calls, and several subagents working in parallel. A user might check in a few times a day while the agent continues working in the background. That matters for CPU demand. User count is only one input; how much work each user delegates matters just as much. Heavy users could have several agents running in parallel throughout the day, accounting for a disproportionate share of CPU usage. As tasks become more complex and agents run more frequently and for longer, we think CPU demand for heavy users could reach 3–5x Freda’s baseline over the medium term, while the increase for the average user could be smaller. That range assumes a more mature product and deeper usage, and will depend in part on how many users become heavy users. It will need to be tested against actual usage data. The same applies to DRAM. A VM using 3GB today may need more as it handles more browser tabs, larger files and concurrent tasks. Longer-running tasks also leave more state to retain. The increase will depend on architecture and resource sharing, but we wouldn’t hold memory per user constant as the product evolves. We’re particularly bullish on NAND. A personal agent used over months or years accumulates files, conversation history, preferences and unfinished work. That data stays when the user logs off. When they return, the agent needs to pick up where it left off. CPU capacity can be reassigned once a task finishes. The user’s stored data still occupies space. Someone who has used an agent for two years could have a very different storage footprint from someone who signed up two weeks ago. Frequently retrieved history, personal knowledge indexes and working files are all natural uses for NAND. You can’t apply a two-hour daily activity assumption to that storage requirement. Jensen’s Inference Context Memory Storage announcement at CES earlier this year addressed a related need. It adds a flash tier for reusable KV cache, extending capacity beyond GPU and host memory so agents can reuse context across interactions. Persistent personal data and reusable inference context are distinct workloads, but both add demand for NAND. We think both become more significant as people entrust agents with more of their daily work. There is also memory and storage demand beyond the sandbox. Freda’s VM DRAM estimate covers one part of the system. Training and inference clusters need GPU HBM, host DRAM, and local and shared SSD storage. For a sense of scale, a DGX B200 system has eight GPUs with 1.44TB of HBM, another 2–4TB of host memory, and roughly 30.7TB of local data SSDs, before external storage. Meta’s configuration will differ, but the point is that GPU deployments bring substantial memory and storage requirements of their own. Counting VM memory alone misses that demand. On GPUs, Freda’s estimate of roughly 1GW for 100 million DAU is a useful starting point. Cache reuse, model improvements and custom silicon should keep bringing down the cost of a given task. At the same time, users may ask for more tasks, longer tasks and harder tasks. Total demand depends on how quickly usage grows relative to those efficiency gains. At the same time, Meta still has plenty of room to optimize. It could eventually get costs down to ChatGPT Plus levels. We’ve seen this repeatedly over the past few years: existing workloads get cheaper, and new workloads emerge. Extrapolating from usage at a single point in time tends to miss what comes next. Muse is still very early. Booking flights and negotiating bills may tell us relatively little about how people will use a mature personal agent. The question we care about is how much users will delegate a year from now. Moving from occasional questions to several agents doing useful work every day increases more than token consumption. It adds CPU work, working memory, and a growing stock of personal data and context. Infrastructure demand per user could rise substantially even as each individual operation gets cheaper. That is also how we think about Meta’s compute spending. If Muse, Business Agent and glasses drive repeat usage and more demanding workloads, Meta has a reason to keep expanding inference capacity. It also has a reason to invest more in pretraining and RSI to keep its models competitive. We’d use Freda’s numbers as a baseline and underwrite the investment case against how usage could develop from here. CPU demand could rise above that baseline as users delegate more work, alongside NAND demand that builds as users accumulate data over time. If personal agents retain users and take on a growing share of their work, we think Meta is more likely to be short of compute in 2027 than sitting on excess capacity.

这条是引用着另一条写的：「估算支撑 1 亿 DAU 的 Muse 基础设施规模」