India's 5G Networks Can't Yet Support LLMs Despite Strong Upload Gains, Says Report

India's 5G networks may offer competitive speeds, but they still fall short on one of the most important metrics for artificial intelligence: latency. According to a new report from Ookla Research, India is among only four of the 22 markets studied that fail to meet the sub-50 millisecond latency target recommended for text-based large language models (LLMs), recording a multi-server latency of 51.6 milliseconds.

The report, Beyond Download Speed: Benchmarking 5G Mobile Networks Against AI Workloads, argues that traditional speed tests are no longer enough to judge network quality. As AI-powered services such as ChatGPT, Gemini and Copilot become mainstream, factors including upload capacity, latency under load, cloud connectivity and jitter will have a much greater impact on user experience than headline download speeds.

Despite ranking ninth among the 22 markets on download speeds, India sits in the bottom tier for baseline latency, suggesting that faster internet speeds do not necessarily translate into a better AI experience. Text-based LLMs rely heavily on how quickly prompts reach cloud servers and how fast responses begin streaming back to users, making latency a more meaningful benchmark than peak throughput.

Interestingly, India's network performs considerably better when it comes to handling congestion. Ookla found that India records a latency degradation ratio of 4.0x when connections are fully utilised, making it one of the stronger-performing markets under stress. A lower degradation ratio indicates that the network maintains responsiveness even when demand spikes, an increasingly important metric as AI traffic grows.

The report also paints a mixed picture of India's upload performance. While Indian operators allocate only 7.53% of total 5G throughput to upload, resulting in a median upload speed of 15.75 Mbps, the country has recorded the second-highest growth in upload allocation among all 22 markets studied. India's upload share has increased by 1.53 percentage points between 2023 and 2025, reflecting operators' efforts to prepare networks for AI applications that increasingly rely on upstream data.

According to Ookla, upload capacity is becoming far more important because AI traffic behaves differently from conventional internet usage. Unlike video streaming, where most data flows towards users, AI workloads require prompts, documents, images and sensor data to be uploaded before cloud models generate responses. Emerging use cases such as voice AI, multimodal assistants and AI glasses are expected to place even greater pressure on uplink capacity.

The report also looks beyond mobile networks themselves. Since most AI inference runs on cloud platforms, the quality of the connection between telecom networks and cloud providers has become another critical factor. In India's case, cloud infrastructure latency varies considerably depending on the provider, ranging from 108 milliseconds on Microsoft Azure to 158 milliseconds on Oracle Cloud Infrastructure (OCI), indicating that the cloud ecosystem can influence AI responsiveness as much as the mobile network itself.

Connection stability presents another challenge. India records a median cloud jitter of 6.7 milliseconds, but the 90th percentile worst-case jitter rises to 25.7 milliseconds. High jitter can introduce inconsistent delays, particularly affecting conversational AI, where even small fluctuations can make interactions feel less natural.

Overall, Ookla concludes that download speed alone is no longer an adequate measure of network quality in the AI era. While India's 5G ecosystem continues to improve, particularly in upload performance, the report suggests that reducing latency and improving cloud connectivity will be just as important if operators want to support the next generation of AI-powered services.