# Load Balancing and Scaling LLM Serving

DevFeed: [Load Balancing and Scaling LLM Serving](<https://devfeed.tech/articles/load-balancing-and-scaling-llm-serving-19903.md>)

Original publisher: [Read original article](<https://www.digitalocean.com/blog/load-balancing-scaling-llm-serving>)

Author: Mohammad Ashar Khan

Published: 2026-04-15T19:03:31Z

Content type: article

Language: en

Sources: [DigitalOcean](<https://devfeed.tech/sources/digitalocean.md>)

Topics: [Load Balancing](<https://devfeed.tech/topics/load-balancing.md>), [Caching](<https://devfeed.tech/topics/caching.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [Routing (disambiguation)](<https://devfeed.tech/topics/routing.md>), [Latency](<https://devfeed.tech/topics/latency.md>), [sglang](<https://devfeed.tech/topics/sglang.md>), [vllm](<https://devfeed.tech/topics/vllm.md>), [Concurrency](<https://devfeed.tech/topics/concurrency.md>), [TensorRT](<https://devfeed.tech/topics/tensorrt.md>)

Tags: [caching](<https://devfeed.tech/tags/caching.md>), [concurrency](<https://devfeed.tech/tags/concurrency.md>), [efficiency](<https://devfeed.tech/tags/efficiency.md>), [engineering](<https://devfeed.tech/tags/engineering.md>), [http](<https://devfeed.tech/tags/http.md>), [inference](<https://devfeed.tech/tags/inference.md>), [latency](<https://devfeed.tech/tags/latency.md>), [llm](<https://devfeed.tech/tags/llm.md>), [load-balancing](<https://devfeed.tech/tags/load-balancing.md>), [performance](<https://devfeed.tech/tags/performance.md>), [round-robin](<https://devfeed.tech/tags/round-robin.md>), [sglang](<https://devfeed.tech/tags/sglang.md>), [tensorrt](<https://devfeed.tech/tags/tensorrt.md>), [vllm](<https://devfeed.tech/tags/vllm.md>)

## AI overview

This article explains why scaling LLM serving requires load-balancing strategies that account for prompt-prefix caching. It describes how naive round-robin routing reduces cache-hit probability as the number of replicas grows, and introduces cache-aware routing approaches intended to preserve cache efficiency, control costs, and improve performance. It also discusses inference engines including vLLM, SGLang, and TensorRT, along with prefill and decode processing.

## Source excerpt

Load balancing for LLMs is fundamentally different from load balancing for traditional services like web servers, APIs, or databases. Prompt caching is the reason. Prompt caching typically cuts input token costs by 50-90% and can reduce Time to First Token (TTFT) latency by up to 80%, but those gains assume your request lands on the replica that already has the relevant prefix cached. Under naive round-robin load balancing across N replicas, that probability is 1/N. The cache hit rate that made caching so attractive at one replica degrades almost linearly as your fleet grows. Solving this requires rethinking how requests are routed at the infrastructure level. This article covers the load balancing strategies and specialized routers that preserve cache efficiency at scale, starting with why standard approaches fall short and progressing to precise, cache-aware routing techniques. Inferencing engines To achieve large-scale inferencing, we use inference engines. These engines simplify the complexities of serving LLMs and offer improved resource utilization on the underlying GPUs. They also enable higher concurrency and allow for customization to suit diverse inference workloads, such as real-time chat completions and long-form document summarization. Noteworthy engine options include vLLM, SGLang, and TensorRT. The inferencing process is largely consistent across different engines. Sending an HTTP request to an engine initiates a standard sequence of steps. Prefill Phase: The input prompt is first converted into token IDs using the model's tokenizer. Requests are grouped into batches for efficient concurrent processing by the engine. During this initial processing, special Key (K) and Value (V) tensors are computed. This phase concludes after the first forward pass, resulting in the generation of the first output token. Decode Phase: This phase involves an auto-regressive loop, continuing until an end-of-sequence token is generated or the maximum sequence length is re