AI workloads to test mobile network capability

Few industries have stayed immune from the effects of artificial intelligence (AI), and the global mobile comms industry has certainly seen fundamental changes. Indeed, a study from Ookla has found that AI has changed “what a good mobile network looks like” and that the service metric the industry has relied on for over two decades – peak download speed – no longer wholly predicts performance.

The report was based on Speedtest Intelligence 5G data from 2025 across 22 markets and 86 operators in North America, Europe, Asia-Pacific, the Middle East and Latin America, which measures upload capacity, latency under load and the quality of the path to the cloud.

The report also aimed to rebuild a download-led scorecard around what AI actually asks of a network, where current 5G mobile networks are ready and where they fall short. It looked at whether an AI application feels instant or breaks, depending in large part on how much a network can upload, how it holds up under load and how consistently it reaches the cloud. On these measures, different networks were found to have come out on top.

Fundamentally, the report showed that the networks topping the download charts were often not those best prepared for AI traffic. It highlighted that the intrinsic change that AI brings about is less about raw capacity, which operators have expanded for years, than about the shape of the traffic – heavier on upload, always on and bursty, rather than download-led and session-based.

Furthermore, Ookla stressed that AI traffic was not one thing. That is to say, text chat, conversational voice, multimodal and augmented reality (AR) vision, generated video and agentic activity each load the network differently, and most of these uses lean on parts of the network that download speed never tested.

Drilling deeper into the data – and as a proof point of how download speed is an unreliable guide to AI readiness – the study found that different markets lead in terms of latency that AI applications depend on.

Measured against the AI workload thresholds, current 5G was regarded as being in reasonable shape for the traffic that dominates now and short of the bar for what is coming. The metrics that decide AI performance – upload capacity, latency under load, and the path to the cloud – follow a different order, and the gap widens as adoption shifts towards heavier use cases like conversational voice and multimodal AI.

Specifically, 18 of 22 markets met the Speedtest AI text target of under 50ms on multi-server latency, and 13 met the conversational voice target of under 40ms, led by Singapore at 24.6ms and the UAE at 31.1ms. The four that missed the AI text target sit just outside it – South Korea at 53.0ms, India at 51.6ms, the US at 50.5ms and Spain at 50.2ms.

What Ookla called the harder ceiling was AR and multimodal vision, where no market reached the sub-10ms target, and only Singapore cleared even the looser 30ms minimum. In aggregate, the report stated that the most demanding modality remains beyond what current 5G delivers, even in the most advanced deployments.

As regards specific geographical findings, the report showed the European region led the dataset for low cloud infrastructure latency, giving users in these markets what was seen as the most latency headroom for inference, leaving more room for AI model processing before the delay degrades the user experience. 

Sweden, Norway and Finland tended to show more consistent uplink performance, aided by complementing time division duplex (TDD) mid-band with frequency division duplex (FDD) low-band spectrum, with the latter sitting in the top tier for baseline latency at 33.3ms. Norway held one of the steadiest connections to the cloud in the dataset.

The UK ties for the lowest latency degradation ratio in the dataset (3.7 times) when the network is fully utilised, while France had an upload gap. The country allocates just 5.81% of its network throughput to the uplink, placing it in the lowest tier of the dataset alongside the US, Brazil and the UAE. It also records a latency degradation ratio of 7.3 times under peak demand.

Spain sits in the bottom tier for baseline responsiveness, one of only four markets in the dataset to miss the text-LLM (large language model) latency target, at 50.2ms. Italy allocated 9.23% of its network capacity to upload and records a multi-server latency of 49.6ms, sitting just under the 50ms target required for text-based LLMs.