VMO2 Finds AI-RAN Does Not Need GPUs

Virgin Media O2 has tested a Nokia RAN running on an Nvidia GPU at a busy site in Paddington, central London, Light Reading reported on 28 October. Rob Joyce, VMO2's director of mobile access engineering, said AI-based channel quality estimation raised spectral efficiency by 20%. He also said a GPU is not required to do this and described Ericsson's silicon as very good. Ericsson CEO Per Narvinger has said about 60,000 base stations operated by its customers already run an AI successor to link adaptation on existing ASICs, with a gain of around 10%.

VMO2 has no GPU RAN in the £700 million tranche of network spending announced earlier this year, and Joyce favours a distributed RAN. Centralising GPU baseband would require CPRI or eCPRI links of 25 Gbit/s each, about 3 of them or 75 Gbit/s, to a site that has 10 Gbit/s of fibre today. In his words, a distributed deployment means putting additional baseband GPU resources at each site, which is the cost that has to be weighed against the gain.

The 20% and the 10% cannot be compared. They come from different vendors, different algorithms and different scales: 1 trial site against a deployed base of 60,000. Light Reading's report gives no baseline, test duration or site count for the 20%. I made the case in Ericsson and Nokia at odds on AI-RAN silicon that the decision to use AI in the RAN is separate from the decision to buy GPUs, and VMO2's engineering lead now says the same. That keeps the position from Is AI-RAN the future of telco? intact: the commercial case for GPU RAN depends on benefits beyond radio algorithms that ASICs can also run, and no operator has published a baseline or a cost per site to show it. For Nokia customers the choice is narrower than the technology case suggests, because Nokia is no longer developing new technology with Marvell and its roadmap runs through GPUs.

Ericsson Says Autonomy Does Not Need Perfect Data - I disagree

Lucky La Riccia, head of cloud software and services for EMEA at Ericsson, told Fierce Network at Ericsson's OSS/BSS Summit in Zurich that operators do not have to wait for clean data before pursuing autonomous networks. In Fierce's report of 22 October, his advice is to understand the limits of the data, put controls around how autonomous systems use it, and accept some imperfection: "Maybe it's okay to have some unstructured data." Ericsson favours a manager-in-the-loop model in which a person can oversee an AI agent, halt or review its actions, and approve them to continue. Autonomy should be pursued domain by domain, starting where business value is highest, and La Riccia said no customer will reach Level 4 across a network within the next year. The operator examples are Telia, which is starting with a common network inventory view for orchestration and provisioning, and Swisscom, which is starting with a cloud-native core and an intelligent automation platform that includes closed-loop automation in the radio access network.

Probabilistic models, deterministic networks

I read the data question differently. AI is a probabilistic process: a model returns the most likely answer with a degree of confidence, not a determined result. A telecom network is operated to deterministic outcomes. A handover completes or it drops, a slice meets its service level or it does not, a configuration change succeeds or it fails. A closed automation loop connects the two, because a probabilistic output triggers a deterministic action on a live network.

Imperfect data widens that gap at every stage. It produces inaccurate readings of network state, weaker predictions built on those readings, and lower confidence in the recommended action. In an automated loop the errors compound, because each action changes the network state that the next prediction reads. The value of the AI falls, a human has to stay in the loop to catch the errors, and the operator ends up further from Level 4. On this reading, manager-in-the-loop is the control that compensates for data quality. It is not a stage on the way to autonomy. It is the same pattern as Deutsche Telekom's RAN Guardian in Deutsche Telekom Extends Agentic AI Beyond RAN: autonomous action on routine events, with a human approving every critical change.

The way to narrow the gap between probabilistic and deterministic outcomes is data. Full-fidelity data, meaning complete counters, traces and state records and not sampled aggregates, lets a model read the network as it is. Long time windows let the operator measure how often predictions were right across seasonal cycles, faults and rare events, and calibrate confidence against outcomes. That measured accuracy is the evidence for deciding which actions can run without approval, so human intervention in the loop falls as the track record builds. Without it, the approval step has no basis for being removed.

Cross-domain loops raise the requirement further, because one agent's output becomes another's input. They need an agreed definition of state, a statement of who is authorised to change what, and a record of what was done, which is the argument in Agentic AI: Autonomous Agents Governance and Enforcement and in DTW Ignite 2026: The API Is Not Enough. Domain-by-domain sequencing is sensible for delivery, and it points to a rule for choosing the first domains: those where the operator already holds full-fidelity data over long windows. A common inventory view such as Telia's is in effect a data quality investment, and it is the kind that moves an operator toward Level 4.