vLLM vs SGLang vs Ollama: which LLM inference engine (2026)
Use vLLM or SGLang to serve open models to many users on GPUs: both support continuous batching, efficient KV cache management, quantization and OpenAI-compatible APIs. Use Ollama, built on llama.cpp, to run models locally on laptops and small servers with minimal setup. Hugging Face's Text Generation Inference (TGI) is in maintenance mode and recommends vLLM, SGLang or local engines.
Checked October 2026. Benchmark on your own model and traffic before choosing.
Comparison
| vLLM | SGLang | Ollama | |
|---|---|---|---|
| Target | GPU serving at scale | GPU serving at scale, structured and multi-call workloads | Local and small-scale use |
| Key techniques | PagedAttention, continuous batching, prefix caching | RadixAttention prefix sharing, continuous batching | llama.cpp with GGUF quantized models |
| API | OpenAI-compatible | OpenAI-compatible | Simple REST API, OpenAI-compatible endpoint |
| Multi-LoRA | Yes | Yes | Limited |
| Hardware | NVIDIA, AMD and others | NVIDIA, AMD and others | CPU, Apple Silicon, consumer GPUs |
| Setup | Moderate | Moderate | Very easy |
What the techniques mean
PagedAttention stores the KV cache in fixed-size blocks, like virtual memory pages, so less memory is wasted and more requests fit on a GPU. RadixAttention keeps cached prefixes in a tree, so requests that share a system prompt or earlier turns reuse computation. Continuous batching adds and removes requests at every generation step, so short answers don't wait for long ones.
When to choose which
- Production GPU serving: vLLM or SGLang, benchmarked on your workload.
- Many calls sharing long prefixes (agents, structured generation): test SGLang.
- Laptops, on-prem prototypes, edge devices: Ollama, or llama.cpp directly.
- Air-gapped deployments: any of them, with models and containers mirrored internally.
How to benchmark
Use your model, your prompt lengths and your output lengths, and measure throughput (tokens per second across all users), time to first token at p50 and p95, and how many concurrent requests fit before latency breaks your target.
From the floor
In the edge inference session, I showed Ollama as the way to get open-source models onto your own machine: you search for a model like Llama 3.2, pick a version such as the 1 billion parameter one, and run it locally, which is where most builders start before they need a GPU serving engine.