How OpenAI Serves Millions of Requests for GPT Models

Do you know that OpenAI’s GPT models handle millions of requests daily? But have you ever wondered how they manage such an enormous scale while maintaining high efficiency and low latency? This blog delves into the architecture, load balancing, caching mechanisms, and mathematical models that power OpenAI’s large-scale AI inference.

Architecture Overview

OpenAI’s infrastructure is built for two things: speed and reliability. Here’s a simplified look at the key components:

1. Distributed Compute Clusters
   GPT models run on thousands of GPUs (like NVIDIA A100s or H100s) spread across global data centers. These clusters are organized into pods, each handling slices of incoming requests.
2. Load Balancers
   Before a request hits the model, it passes through intelligent load balancers. These distribute traffic evenly to prevent any single server from being overwhelmed. Imagine a busy restaurant host seating guests across tables to avoid chaos.

   OpenAI likely uses a combination of:

   1. Global Load Balancers (GLBs): Distribute traffic across multiple data centers based on proximity, latency, and server load.
   2. Local Load Balancers: Within each data center, these direct requests to the least busy inference server.
   3. Autoscaling Mechanisms: Dynamically allocate additional GPU instances based on request spikes.
3. Model Parallelism & Sharding
   Giant models like GPT-4 are split into smaller pieces (shards) and distributed across multiple GPUs. For example, if a model has 1.7 trillion parameters, it might be divided into 8 GPUs, each handling ~200 billion parameters. This reduces latency and allows parallel processing.
4. Quantization and Optimization
   To save memory and speed up inference, OpenAI likely uses quantization—converting model weights from 32-bit floating points to 16- or 8-bit values. This cuts computational costs without major accuracy losses.
5. Caching Frequent Requests
   Common prompts (e.g., “Explain quantum physics”) are cached. If 1,000 users ask the same question, the system serves the cached response instead of recomputing it.
6. Autoscaling with Kubernetes
   During peak traffic, Kubernetes spins up additional containers to handle the load. When demand drops, it scales back to save costs.

The Math: Calculating Costs, Throughput, and Latency

Imagine OpenAI serves 10 million requests per day for GPT-4. Here’s how the numbers might play out:

1. Requests Per Second (RPS)

• 10,000,000 requests/day ÷ 86,400 seconds/day ≈ 116 RPS
  But traffic isn’t uniform—peak hours could see 3-5x this rate (~350-580 RPS).

2. GPU Power Required

• Assume each GPT-4 inference takes 3 seconds on an A100 GPU.
• To handle 116 RPS: 116 requests × 3 seconds = 348 GPUs needed at any moment.
• With batching (processing multiple requests together), efficiency improves. If batches of 4 are used:
  348 GPUs ÷ 4 ≈ 87 GPUs. Add redundancy (say 20%), and you’d need ~105 GPUs.

3. Latency vs. Throughput Tradeoff

• Users expect responses in 2-5 seconds. To keep latency low, OpenAI limits batch sizes and uses faster GPUs.
• If a GPU handles 30 tokens/second, a 500-token response takes ~17 seconds. But with parallelism and optimized kernels (custom code), this drops to ~5 seconds.

4. Cost of Serving

• A100 GPU cost: ~$2/hour on cloud platforms.
• For 105 GPUs running 24/7:
  105 × $2\times 24=$5,040/day or ~$151k/month.
• Add networking, storage, and staff costs—this likely hits $200k-$500k/month. However, reserved instances and custom hardware (like OpenAI’s own servers) reduce expenses.

Caching for Faster Responses

To reduce redundant computations, OpenAI leverages caching:

• Token-Level Caching: Stores previously generated token sequences to avoid recomputation.
• Response Caching: Frequently asked queries (like “What’s the weather today?”) are cached.
• Vectorized Search Indexes: Store common user queries and retrieve precomputed responses efficiently.

Challenges: How OpenAI Stays Ahead

1. Latency vs. Cost
   Smaller batches mean lower latency but higher costs. OpenAI uses dynamic batching—grouping requests that arrive around the same time.
2. Model Updates Without Downtime
   Rolling updates let them deploy new models incrementally. For example, 10% of servers get the update first, followed by the rest if tests pass.
3. Handling Failures Gracefully
   If a GPU fails, the system reroutes traffic to healthy nodes. Monitoring tools like Prometheus track performance in real-time.
4. Regional Load Balancing
   Users in Europe are routed to EU servers; Asian traffic goes to Singapore. This reduces latency and complies with data laws.

Conclusion

OpenAI’s GPT models serve millions of requests daily through a sophisticated mix of distributed computing, caching, load balancing, and GPU-optimized inference. By continuously refining its architecture and leveraging mathematical optimizations, OpenAI ensures fast and cost-effective AI services at scale.