LLM Load Testing

LLM load testing is the process of sending concurrent requests to a language model API under controlled conditions to measure how latency, token throughput, and error rates change under realistic traffic volumes.

What It Is

A model that feels fast in a developer script often behaves very differently in production. That gap exists because development tests usually run one request at a time, while production means dozens or hundreds of concurrent requests competing for the same inference infrastructure. LLM load testing exists to close that gap before it becomes a user-facing problem.

Traditional HTTP load testing measures one thing: how long a server takes to send back a complete response. LLMs break that model. Responses stream — the first token can arrive within a fraction of a second while the complete answer takes several seconds more. The two have different implications for users: slow first-token delivery feels like the system is frozen; slow token-by-token delivery just feels sluggish. Load testing captures both by tracking three distinct metrics:

• Time to First Token (TTFT): how long the user waits before any output appears — determines perceived responsiveness
• Tokens per second: the throughput rate of the streaming response — determines how long a long answer takes to complete
• p99 latency: the response time at the 99th percentile — exposes the worst-case experience for 1 in 100 requests

Think of it like stress-testing a restaurant kitchen. Timing the chef on a single order shows best-case speed. Running fifty simultaneous orders reveals which station becomes the bottleneck, which orders get cold, and what the experience looks like for the last table served. The load test is that second experiment — the one that reflects actual conditions.

The goal is not a single number. TTFT that looks acceptable at ten concurrent requests may become unacceptable at fifty. Token throughput may degrade gradually. p99 latency often spikes sharply at a particular concurrency level — that spike marks the system’s real breaking point. The shape of the degradation curve matters as much as any individual measurement.

These three metrics are the core of what the parent article on LLM load testing covers in depth. They only tell a meaningful story when measured across a range of concurrency levels, not in isolation.

How It’s Used in Practice

The most common scenario: a team has selected a hosted LLM API for a customer-facing feature — document summarization, code assistance, or a chat interface — and needs to know whether the model’s infrastructure can handle their expected traffic before launch. They run a load test at their anticipated peak request rate, then at two to three times that rate, and measure TTFT, throughput, and error rates at each level.

The results answer concrete questions: Can the API sustain the team’s concurrent users? At what point does TTFT cross the threshold where users start abandoning the feature? Does the provider rate-limit before or after quality degrades? Teams running self-hosted models use the same method to determine how many inference replicas the deployment needs.

A second use case is provider or model comparison. When choosing between two hosted models for the same task, a load test under production-like conditions gives results that playground benchmarks cannot — namely, which model degrades more gracefully as traffic increases.

Pro Tip: Start your load test at roughly twice your expected peak traffic, not at your average. Average traffic hides the concurrency spikes that actually degrade user experience. Run each concurrency level for at least five minutes before recording metrics — queuing effects can take a few minutes to stabilize.

When to Use / When Not

Common Misconception

Myth: If a model responds quickly in the API playground or a developer test script, it will respond quickly in production.

Reality: Playground calls run in isolation against a shared inference server with little competition for resources. Production sends many requests at once. Latency under single-request conditions is almost always lower than latency under realistic concurrency — often by a wide margin. The playground result measures best-case speed; the load test measures what actual users experience during peak hours.

One Sentence to Remember

The number that matters is not your average response time in a quiet test — it is your p99 latency at peak concurrent load, because that is the number your slowest user encounters every single day.

FAQ

Q: What concurrency level should I use when starting an LLM load test? A: Begin at your expected peak concurrent requests, then increase to two or three times that level. The point where TTFT and p99 latency begin to degrade sharply is your system’s effective capacity boundary.

Q: How does LLM load testing differ from traditional API load testing? A: Traditional load testing records a single total response time. LLM load testing also tracks TTFT and per-token throughput separately, because streaming responses have two distinct latency points that affect user experience in different ways.

Q: Do I need specialized tools to run LLM load testing? A: Standard HTTP load testing tools can send requests and measure total response time. LLM-specific tools like GenAI Perf also parse the streaming response to extract TTFT and token-by-token throughput — metrics that general-purpose tools do not capture out of the box.