Zelos builds a 15,000-GPU cluster to train its self-driving brain
An autonomous-driving company answers the long-tail problem with compute, while the agent layer gets a gate that rules before the action runs and communications teams get a way to watch what AI says about them.
Zelos, the Suzhou RoboVan maker founded by ex-Baidu autonomous-driving engineers, says it has built the industry's first "10,000-card" L4 compute cluster — nearly 15,000 accelerators in total — built to train its APEX multimodal foundation model. CTO Zhuang Li disclosed the figure on September 17, a week after CEO Kong Qi announced the company's shift toward what it calls city-level physical AI. APEX is described as moving from the tens of billions of parameters toward the hundreds of billions, fed by 270 million kilometers of real L4 operating mileage from more than 30,000 vehicles across 300-plus cities in over 20 countries. It is the first time an L4 autonomy company has put its training infrastructure at that scale, and it reframes where the driving race is being fought: not in the vehicle's sensor stack but in the data center behind it.
The argument Zelos is making is a flywheel, not a flex. Real urban freight throws up long-tail conditions that rules cannot enumerate and simulation cannot reproduce — unmarked rural roads, temporary construction barriers, irregular obstacles, pedestrians and vehicles mixing without order, lighting that flips under bad weather. A stronger base model cuts operating cost, cheaper operations win more customers, and more running vehicles return more of the scarce physical-world data that trains the next base model. That loop only closes if a company owns both the real fleet and the foundation model, which is exactly the position Zelos is claiming: general model labs have no physical-world data, and most autonomy companies have too little real operating data to feed one.
The deployment architecture is worth a look, because it is a bet that a car alone cannot be smart enough. On 5–30 km/h complex roads, an on-device VLA model handles real-time inference while a cloud VLA model is called in for understanding; at 0–5 km/h — the last-meter mess of blocked lanes and wrong-way traffic — a third layer, a "safety agent," takes over decisions like rerouting, turning around or pulling over, with the distillation path running a better on-device model back down after every base-model upgrade. The company's tell is that its VLA is not forced to render decisions in human language before acting; it treats the step as a pre-verbal "intent," closer to a flinch than a sentence. Hold the scale numbers at company-reported, not audited — the cluster size, fleet count and mileage all come from Zelos itself.
Arcjet launched Agent Runtime Security, which checks each agent action against policy before it executes and logs the decision as audit evidence. Platforms can feed the system through the OpenTelemetry tooling they already run, or via Anthropic's Compliance API for Claude customers, and each agent then appears in an inventory with its prompts, tool-call parameters and security decisions stitched into one workflow per session. Policies cover prompt-injection detection, personal-data redaction, bot detection and rate or spend caps, run on Open Policy Agent, and can be changed without redeploying the application; Arcjet returns a decision your code can use to halt the call, ask a human, or explain the refusal to the agent, with a second check after the call to verify the result. Founder and CEO David Mytton's framing is that a dangerous outcome usually builds out of "a series of steps that look perfectly reasonable on their own" — which is the same case for keeping the gate in the call path rather than in the prompt, as we argued in How to — decide what an AI agent may do. It lands next to StackGen putting one governance plane under every production agent: the observability vendors are converging on the same architecture, and the differentiator will be whether the enforcement point sits inside the agent's process or outside it.
PeakMetrics launched AI Perceptions, a service that regularly asks ChatGPT, Gemini, Claude, Grok and Perplexity the questions a company cares about and scores how the answers portray it. Communications teams define prompts covering their brand, products, competitors and industry issues, then grade responses on favorability, trust, competitive positioning, message adoption and purchase intent — the generative-engine-optimization problem stated as a reputation metric rather than a traffic one. The platform also tracks which pages a model cites when it invokes web search, and folds the results into PeakMetrics' existing narrative monitoring, which assigns bot-detection scores to social accounts; the company says automated activity typically accounts for 20% to 30% of the content in a customer workspace and can spike during a crisis. Pricing starts at $99 a month. The honest limit: you can see the answer and sometimes the citation, but not the weighting that produced it — which makes this a monitoring tool that tells you a narrative shifted, not yet a control.
What to watch: whether other autonomy firms disclose comparable training scale, and whether Arcjet's pre-execution gate holds up in production agents that already call dozens of tools per run.
Would you rather own the base model or the fleet, if you could only pick one in the autonomy race? Tell us in the comments.
Sources: Leiphone — 九识建成首个L4万卡集群，无人驾驶进入多模态大模型新范式 · Sina Finance — 九识宣布建成万卡集群 · ReadAI — 九识建成首个L4万卡集群 · SiliconANGLE — Arcjet launches runtime security to track and control AI agents in production · Arcjet — Introducing Agent Runtime Security · SiliconANGLE — PeakMetrics tracks brand reputations across five top AI platforms · PeakMetrics — AI Perceptions launch
