How ChatGPT Serves 700M Users: An Inside Look at Large-Scale AI Inference

August 16, 2025

The ability of services like ChatGPT to handle 700 million weekly users, while a similar model is nearly impossible to run locally, isn't due to a single magic trick. Instead, it's a multi-layered solution combining immense capital investment, economies of scale, and a deep stack of sophisticated engineering optimizations.

The Power of Scale: Money and Utilization

The most straightforward factor is financial. Companies like OpenAI operate with tens of billions of dollars in funding, allowing them to build and operate data centers filled with thousands of high-end, data-center-grade GPUs (like NVIDIA's H100s). This is a scale of hardware that is simply inaccessible to individuals.

Beyond just owning the hardware, the key is utilization. A personal computer running an LLM sits idle most of the time, making the cost-per-query astronomical. A large-scale service, however, amortizes the cost of this expensive hardware over millions of users, running it at near-full capacity 24/7. This is a modern, distributed application of the classic time-sharing principle.

The Core Technical Strategy: Batched Inference

The most significant technical advantage comes from batched inference. The primary bottleneck in LLM inference is not raw computation but memory bandwidth—the time it takes to move the model's weights from high-capacity VRAM to the GPU's fast on-chip cache.

Instead of processing one user query at a time (loading weights, computing, repeating), these systems group hundreds or thousands of requests into a single "batch." The necessary model weights are loaded from VRAM once, and the computation is then performed for all requests in the batch simultaneously. While this can slightly increase the latency for the very first token, it multiplies the overall throughput by orders of magnitude, drastically lowering the effective cost per query.

Advanced Optimization Techniques

Several other powerful optimizations are layered on top of batching to further enhance efficiency and speed:

  • Model Parallelism and Sharding: Today's largest models are too big to fit on a single GPU. They are strategically split, or "sharded," across many GPUs and even multiple servers. Techniques like tensor parallelism (splitting individual matrix operations), pipeline parallelism (assigning different model layers to different GPUs), and expert parallelism (for MoE models) are used to manage this distribution.
  • Quantization: This is a compression technique where the model's parameters (weights) are converted to lower-precision numerical formats (e.g., from 32-bit floating-point to 8-bit integers). This reduces the model's memory footprint and the amount of data that needs to be moved, directly speeding up inference.
  • Speculative Decoding: This clever method uses a much smaller, faster "draft" model to generate several potential future tokens. The large, powerful model then validates this sequence of tokens in a single parallel step. If the draft model's predictions are correct, multiple tokens can be generated for the cost of one, often resulting in a 2-4x speedup.
  • Mixture-of-Experts (MoE) Architecture: Many state-of-the-art models are MoEs. This means the model consists of many specialized sub-networks ("experts"), and for any given input token, only a small fraction of these experts are activated. This significantly reduces the amount of computation required compared to a dense model of equivalent size.
  • Efficient KV Cache Management: LLMs maintain a "KV cache" that stores the state of the ongoing conversation to avoid redundant computation. Systems like vLLM use advanced techniques like PagedAttention to manage this memory efficiently, preventing waste and allowing for higher batch sizes.

The Systems-Level View

Finally, all these techniques are supported by robust, large-scale systems engineering. This includes everything from custom hardware like Google's TPUs, high-speed interconnects like NVLink and InfiniBand that link GPUs, and sophisticated orchestration software like Kubernetes to manage the vast cluster. Intelligent load balancers route incoming requests to available compute resources, ensuring the entire system runs as efficiently as possible.

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