According to Ookla's latest report titled 'Beyond Download Speed: Benchmarking 5G Mobile Networks Against AI Workloads,' India ranks ninth globally on headline 5G download speeds but performs poorly on the critical network characteristics for AI applications. The report tested 86 operators across 22 markets against actual AI workload conditions, revealing that India lands in the bottom tier on upload capacity and latency, two network characteristics increasingly critical for artificial intelligence applications. The report concludes that download speed alone is no longer an adequate measure of network quality in the AI era, as factors including upload capacity, latency under load, cloud connectivity and jitter have much greater impact on user experience than peak throughput.
India's upload performance shows significant room for improvement despite recent gains. As reported by Ookla, India allocates 7.53% of its 5G throughput to upload, delivering a median upload speed of 15.75 Mbps. This falls short of the 20 Mbps target the report sets for AI modalities like augmented reality and multimodal vision, a threshold only ten of the 22 markets studied managed to clear. However, India's upload share actually grew by 1.53 percentage points between 2023 and 2025, while 12 of the 22 markets studied saw their upload share shrink or stay flat during the same period. The report notes that upload capacity is becoming far more important because AI traffic behaves differently from conventional internet usage, as AI workloads require prompts, documents, images and sensor data to be uploaded before cloud models generate responses. The gap in absolute upload speeds varies dramatically, with e& UAE leading at 57.50 Mbps median upload speed, more than four times faster than any US carrier, while the US sits at the bottom for upload allocation at 5.1%.
India's latency performance presents another significant challenge for AI applications. According to Ookla's measurements, India's multi-server latency stands at 51.6 ms, placing it in a group of just four markets alongside South Korea (53 ms), the US (50.5 ms), and Spain (50.2 ms) that miss the sub-50 ms target for text-based AI chat and AI agents. Only 18 of the 22 markets meet the AI text target of under 50ms on multi-server latency, while 13 meet the conversational voice target of under 40ms. Singapore leads at 24.6ms, followed by the UAE at 31.1ms. However, augmented reality and multimodal vision applications present far more challenging requirements, as no market reaches the sub-10 millisecond target for AR and multimodal version, and only Singapore clears the looser 30ms minimum. The report identifies that once cloud transit, inference processing, and server queuing are added to the network's latency contribution, the cumulative delay in these markets approaches the boundary where users perceive degradation.
The performance gap extends beyond mobile networks to cloud connectivity, creating additional delays for AI applications. As reported by Ookla, India's median latency to reach cloud servers runs to 114 ms for Amazon Web Services, 109 ms for Microsoft Azure, 121 ms for Google Cloud, and 158 ms for Oracle Cloud Infrastructure. This compares unfavorably to regions like Europe and East Asia, where South Korea reaches AWS in just 40 ms, Germany in 42 ms, and the UK in 44 ms. India also trails regional markets, with Singapore reaching AWS in 74 ms and Indonesia in 63 ms, despite facing similar distances to major cloud regions. The connection path from network edge to cloud infrastructure has become a critical bottleneck, with Australia facing a gap of 96.6ms between fastest and slowest cloud provider within the same market, sufficient to push voice and agentic applications past perceptible delay thresholds. Brazil faces particular challenges, with median cloud latency of 149.7 to 163.6ms across all four providers, linked to infrastructure concentrated in São Paulo and limited direct peering arrangements.
The telecommunications industry recognizes the urgency of addressing these challenges as AI-powered services like ChatGPT, Gemini and Copilot become mainstream. At the Mobile AI Industry Summit held during MWC Shanghai in June, telecom executives reached what Tech Wire Asia described as an explicit consensus that improving uplink is now the single most urgent priority for mobile networks. Huawei unveiled a solution called GigaUplink specifically to address this gap, while Ericsson projects that under medium AI adoption scenarios, additional AI traffic alone could make uplink demand three times higher by 2031 compared with 2025. For text-based AI applications, all 22 markets studied meet minimum requirements, but concerns remain about future AI modalities, as not one of the 86 operators studied worldwide currently meets the target for multimodal AI. The report emphasizes that connection stability presents another challenge, with high jitter affecting conversational AI interactions where even small fluctuations can make interactions feel less natural. To further explore performance under stress, Ookla has calculated degradation ratios ranging from 3.7 times baseline in the United Kingdom to 11.4 times in Thailand, with the UAE pairing a mid-range degradation ratio with the lowest median loaded latency of any market at 288.4ms.
