Traditional load testing tools (like JMeter or Locust) measure throughput in requests per second (RPS). While this is fine for standard APIs, it's a poor metric for LLM serving. For LLMs, you must measure tokens per second and Time to First Token (TTFT).
If you don't account for token economics, your load tests will fail to predict your real production performance.
Why LLM Load Testing Is Different
1. Token-Based Scaling
A request with a 10-token prompt and 10-token completion is fundamentally different from a 1,000-token prompt. Traditional tools treat them as identical "requests."
2. Non-Deterministic Latency
Inference time depends on output length, batching strategy, and KV cache utilization. Your load tests must simulate realistic prompt and completion lengths to be useful.
3. GPU Saturation
Once a GPU hits its VRAM limit, latency explodes non-linearly. Your load tests should help you find this "cliff" to set your autoscaling targets.
Final Takeaway
Load testing for LLMs requires a token-aware approach. By simulating realistic request shapes and monitoring token-based performance metrics, you can build an inference fleet that scales predictably and reliably.
Need help building a realistic load testing strategy for your LLM endpoints? We help teams design token-aware load tests, identify performance bottlenecks, and right-size their inference fleets. Book a free infrastructure audit and we’ll review your load testing path.
