---
type: concept
x-llmwiki:
  schemaVersion: "0.1"
  contentHash: 1c575f173d8642ff2fd339d74b87d8751f703557eb2de790d5192849750aa132
  pageDirectory: concepts
  sources:
    - conduct-your-own-llm-endpoint-benchmarking-databricks-on-aws.md
  confidence: 0.9
  provenanceState: extracted
  freshnessStatus: unverified
  aliases:
    - throughput-plateau-in-provisioned-throughput-systems
    - TPIPTS
  citations:
    - file: conduct-your-own-llm-endpoint-benchmarking-databricks-on-aws.md
title: Throughput Plateau in Provisioned Throughput Systems
description: The phenomenon where endpoint throughput plateaus as concurrent requests increase beyond provisioned capacity, causing latency to grow unbounded while throughput stagnates.
tags:
  - llm-serving
  - capacity-planning
  - performance
timestamp: "2026-06-19T09:22:06.959Z"
---

# Throughput Plateau in Provisioned Throughput Systems

**Throughput Plateau in Provisioned Throughput Systems** refers to the point at which increasing the number of concurrent requests to a [LLM endpoint](/concepts/model-serving-endpoint.md) no longer yields a proportional increase in throughput (tokens per second). This plateau occurs because the endpoint’s [Provisioned Throughput](/concepts/provisioned-throughput.md) capacity—the number of workers and parallel requests it can handle simultaneously—has been reached. Beyond this limit, additional requests simply queue, causing latency to rise without a corresponding increase in overall throughput.

## How the Plateau Occurs

When an [LLM serving endpoint](/concepts/model-serving-endpoint.md) is provisioned with a fixed capacity (e.g., tokens per second), there is a maximum concurrency level the system can process in parallel. Up to that level, increasing the number of concurrent queries raises both throughput and latency, because multiple requests can be processed simultaneously. However, once the provisioned capacity is saturated, throughput stops increasing—it plateaus. Any further concurrent requests are queued, which increases the total latency (time to first token and overall response time) but does not add to the throughput. ^[conduct-your-own-llm-endpoint-benchmarking-databricks-on-aws.md]

## Example

In the Databricks benchmarking harness, when testing a provisioned throughput endpoint, the throughput‑versus‑latency graph shows that as the number of parallel requests grows, throughput initially increases and then plateaus. The source material shows that with the tested endpoint, throughput plateaued at approximately 8,000 tokens per second. Beyond that point, latency continued to rise due to queuing, while throughput remained flat. ^[conduct-your-own-llm-endpoint-benchmarking-databricks-on-aws.md]

## Implications for Workload Design

- **High‑throughput use cases** (e.g., batch inference, offline processing) are well‑suited to saturate the endpoint near its plateau, accepting higher latency in exchange for maximum tokens per second.
- **Low‑latency use cases** (e.g., real‑time applications) should send fewer concurrent requests, staying below the plateau region to keep latency low, even though overall throughput is below the maximum.

Understanding the throughput plateau is essential for [capacity planning](/concepts/scale-to-zero-and-gpu-capacity-planning.md) and [latency–throughput tradeoff](/concepts/latency-throughput-trade-off.md) decisions. ^[conduct-your-own-llm-endpoint-benchmarking-databricks-on-aws.md]

## Related Concepts

- [Provisioned Throughput](/concepts/provisioned-throughput.md) – The reserved inference capacity measured in tokens per second.
- [Time to First Token (TTFT)](/concepts/time-to-first-token-ttft.md) – A key latency metric affected by concurrency levels.
- [Time per Output Token (TPOT)](/concepts/time-per-output-token-tpot.md) – Determines how fast users perceive model output.
- [LLM Inference Performance Engineering](/concepts/llm-inference-prefill-and-decoding.md) – Best practices for balancing latency and throughput.
- [Model Units in Provisioned Throughput](/concepts/provisioned-throughput.md) – Alternative provisioning model (for models that use model units rather than tokens per second).

## Sources

- conduct-your-own-llm-endpoint-benchmarking-databricks-on-aws.md

# Citations

1. [conduct-your-own-llm-endpoint-benchmarking-databricks-on-aws.md](/references/conduct-your-own-llm-endpoint-benchmarking-databricks-on-aws-bf37642a.md)
