Asia-Pacific mobile networks were designed for video streaming, not for artificial intelligence. And it shows. Text-based large language model workloads run without a hitch, but shift to applications demanding consistent upload and low latency – conversational voice AI, multimodal vision, autonomous agents – and the gaps in an infrastructure still heavily skewed toward download become glaring. Ookla’s latest 5G readiness report, covering 22 markets (nine in Asia-Pacific), dismantles traditional metrics and exposes a critical point: upload is no longer an afterthought; it’s the real stress test for mobile AI.

Upload: the missing piece in 5G design

The asymmetry is structural. Typical consumer traffic (streaming, browsing) is over 90% downlink, but modern AI workloads push the uplink/downlink split toward 50/50 for conversational and agentic AI, while augmented reality and multimodal vision can drive uplink share close to 40%. The problem is that most deployed 5G networks use TDD spectrum, where uplink and downlink share the same frequency band in different time slots: raising the uplink allocation for AI means stealing downlink capacity unless operators coordinate market-wide. Ookla’s numbers show a patchy landscape. Indonesia stands out with a 23.9% uplink share and a median upload speed of 26.38 Mbps, helped by combined FDD and TDD spectrum use. South Korea, despite a low uplink share (7.5%), records a median upload speed of 45.27 Mbps – enough to clear the 20 Mbps threshold Ookla sets as a target for AR and multimodal AI. Other advanced economies, like Singapore (7.9% share, 30.25 Mbps) and Australia (7.3%, but aided by FDD low-band), perform decently, while the Philippines (8.8%) and India (7.5%) lag. Malaysia, with 34.78 Mbps, is well positioned, but speed alone isn’t enough when jitter and cell load come into play.

Latency under load: where networks stumble

Latency divides markets far more than megabits. Singapore leads with 24.6 ms multi-server latency, the only market in the dataset to dip below the 30 ms threshold required for multimodal vision. Malaysia follows at 33.0 ms, Australia at 33.7 ms. Further back, South Korea (53.0 ms) and India (51.6 ms) don’t even meet the 50 ms minimum for text-based LLMs. But the real watershed is latency under load: when the connection is saturated, degradation relative to normal latency paints a critical picture. Singapore, the idle-latency champion, records a 9.2x degradation factor, as dense urban demand competes for cell resources during peak hours. Thailand does worse, with an 11.4x ratio and a median loaded latency of 960.3 ms – a figure that would render any voice assistant or AR application unusable. Such spikes are not trivial: for an AI agent that must react to real-time commands, hundreds of milliseconds of delay break the interaction and push toward architectures where inference does not depend on the mobile network.

Cloud or edge? Inference close to the user

Ookla doesn’t stop at the access network: it also measures latency and jitter on the path from the operator edge to cloud endpoints, where most inference runs today. The choice of cloud provider changes the game: in Australia, latency ranges from 69.3 ms with AWS to 165.9 ms with Oracle Cloud Infrastructure – a 96.6 ms gap that on a multimodal AI session is the difference between fluidity and frustration. In Southeast Asia, OCI is consistently the farthest in latency terms. And while median latency is manageable, 90th-percentile jitter tells another story: the Philippines hits 34.9 ms, Malaysia 33.1 ms, values three to six times the median in many markets. This means that in worst-case moments, timing stability for inference becomes unpredictable, nullifying even the best cloud. The answer, flagged by the report itself, is edge inference: running models on local hardware or proximity servers, cutting out the variability of the mobile network and cloud routing. For those evaluating on-premise LLM deployment, this scenario is not just technical: it means deterministic latency, independence from cell congestion, and – for regulated industries – control over data residency, a critical factor when data traverses public networks and foreign clouds. It is no coincidence that Ookla’s analysis points to three investment areas: rebalancing links toward upload, reducing latency, and treating cloud peering as network infrastructure. Edge computing is no longer a futuristic option, but the structural answer to the gap AI is carving between today’s networks and tomorrow’s workloads.