Red Hat maps vLLM and SGLang to different inference workloads

Red Hat has published a detailed engineering comparison of vLLM and SGLang, arguing that neither engine is a universal winner and that platform teams should test against their own traffic.

Different paths to the same bottleneck

Both projects manage the key-value cache that inference servers retain between a model’s prefill and decode stages. That cache can consume substantial accelerator memory, and the way a serving engine schedules and stores it affects concurrency, latency and throughput.

The comparison traces vLLM’s design to PagedAttention, which divides the cache into fixed-size blocks instead of reserving large contiguous regions. SGLang began from a different workload: applications that repeatedly send common prefixes, including agents, multi-turn chat and extraction pipelines. Its RadixAttention organizes cached prefixes in a radix tree so related requests can reuse prior computation.

The distinction is less absolute now. Red Hat notes that both projects support continuous batching, prefix caching, chunked prefill, structured output, speculative decoding, quantization and distributed execution. Both also target NVIDIA and AMD accelerators, while the article characterizes vLLM’s documented support for TPU, Intel GPU, CPU and Apple hardware as more established.

Benchmarks narrow the gap

Red Hat cites Modal’s LLM Engineer’s Almanac, which tested vLLM, SGLang and TensorRT-LLM across multiple models and input/output shapes on NVIDIA hardware. In the sample reported for Llama 3.1 8B on one H100, vLLM led slightly on a 1,024-input/128-output workload, SGLang led on a 128-input/1,024-output workload, and the engines were nearly even on a 2,048/2,048 workload.

Those results do not settle the choice. The benchmark used largely unrelated text prompts, so it did not exercise the repeated-prefix behavior that matters to chat and agent systems. It also measured a single replica with default configurations.

What platform teams should test

The practical recommendation is to benchmark the intended model, accelerator and request mix rather than adopt a headline throughput number. Long-input retrieval workloads, output-heavy code generation and prefix-rich agent traffic stress different parts of the serving path. Feature timing and operational compatibility can also matter as much as a small throughput advantage.

That is a useful corrective for teams selecting the inference layer beneath an OpenAI-compatible endpoint. The API may make engines look interchangeable, but the scheduler, cache strategy and hardware matrix still shape production behavior.