Goldman Sachs: Rampart Model Reveals AI Computing Power Shifting to Edge Devices
Taylor Wilson
Goldman's trading-desk head Rich Privorotsky flagged a 14.7 MB browser-native privacy model called Rampart as a sign that AI compute is migrating from the cloud to local devices — a shift that challenges the market's linear extrapolation of hyperscale demand.
What is Rampart, and why did a Goldman trading desk highlight it?
Rampart is a PII-redaction model — a tool that automatically detects and masks personal data such as names and ID numbers — built by National Design Studio. It is just 14.7 MB, runs entirely in the browser via WebGPU, and sends no data to any server.
Performance: 98.4% recall on a holdout set, with latency in the low milliseconds. This means → a tiny model can now do locally what used to require a cloud-hosted frontier model, and do it fast.
Goldman's One-Delta desk head Rich Privorotsky stressed in his morning note that the point is not Rampart itself — it is the direction: more and more AI tasks are moving from the cloud back to the device.
What exactly is migrating to the edge?
Privorotsky listed tasks that can already run locally: OCR, PII redaction, embeddings, retrieval, routing, intent detection, memory, and small-context summarization. All of these were assumed to need cloud frontier models a year ago.
In plain terms = twelve months ago, the market assumed nearly every AI workload required hyperscale data centers. That assumption is breaking down — a large share of "mid-to-low-tier" tasks can run on phones, browsers, or local servers.
The most critical capability here is routing — the mechanism that decides which model should handle a given request. As routing improves, calls to the most expensive frontier models drop. The frontier model increasingly becomes a "high-end reasoning layer," not the entire AI stack.
What does this mean for cloud-giant valuations?
Privorotsky posed the key question: if the market is willing to extrapolate every new AI workload into hyperscale cloud demand, it should equally factor growing edge intelligence into its valuation framework.
Put simply = the question is no longer "cloud or local" — it is "how many workflows actually reach a cloud model?" That share may be lower than current pricing implies.
This reflects a forming valuation tension: the faster edge capabilities expand, the more the growth slope of hyperscale cloud demand needs recalibrating.
Where do the mega-cap tech stocks stand right now?
On technicals, Google reclaimed its 100-day moving average, Amazon held its 200-day moving average, both bouncing sharply from oversold levels. Microsoft rallied from its year-to-date low, but Privorotsky sees its outlook hinging more on rationalizing capex against free cash flow.
Apollo chief economist Torsten Slok pointed to the deeper issue: "There is no sign of margin improvement outside the tech sector."
This means → today's AI valuations rest entirely on a promise — that margins at the other 493 companies in the S&P 500 will eventually rise because of AI. That promise has yet to show up in the data, and the coming earnings season is the test window.
Content is for reference only, not financial advice.