The right inference server for a rented GPU depends on who is calling it, and the four projects compared here split cleanly along that line: two are built for an application calling an API, and two are built for a person running a model with the least possible setup. Facts below were checked against each project's own documentation on 28 September 2026, and none of the throughput claims are DeployGPU's own measurements.
What each project actually is
vLLM is a Python-based inference engine and OpenAI-compatible server built around PagedAttention, a memory manager that treats GPU memory for the KV cache the way an operating system treats paged virtual memory, splitting it into fixed-size blocks instead of reserving one contiguous slab per request. It ships as a pip package and as the vllm/vllm-openai Docker image, and it runs on NVIDIA and AMD GPUs among other hardware.
SGLang is also a Python-based OpenAI-compatible server, built around RadixAttention, which caches the KV cache of shared prompt prefixes in a tree structure so that repeated system prompts or few-shot examples do not get recomputed for every request. Its own documentation lists support across NVIDIA, AMD, Intel Xeon, Google TPU, Ascend NPU and Moore Threads hardware.
Ollama is a single binary that wraps model downloading, quantization format and serving behind one command, ollama run <model>, plus its own REST API and a Modelfile format for customizing a model. It targets running one or a few models on one machine rather than a multi-GPU cluster serving many requests at once, and it is the engine behind the Open WebUI template on DeployGPU.
llama.cpp is a C and C++ inference engine built around the GGUF file format, with 1.5-bit through 8-bit quantization and backends for CUDA, HIP, Metal and plain CPU. Its own README describes the goal as minimal setup across a wide range of hardware, and it includes its own OpenAI-compatible server, started with llama-server, which can download a GGUF straight from Hugging Face with its -hf option.
Where the models come from
vLLM and SGLang load a model repository directly from Hugging Face in its native format: BF16 or FP16 safetensors, plus official or community FP8, AWQ, GPTQ or NVFP4 builds where they exist. Ollama and llama.cpp both use GGUF, a single quantized file at a fixed bit width, which is why an Ollama tag and a vLLM checkpoint for the same model rarely report the same download size. Ollama pulls GGUF files from its own library by tag, ollama pull gpt-oss:20b for example, while llama.cpp downloads a named GGUF repository from Hugging Face directly.
Setting each one up
Ollama needs the least: install the binary, run ollama run <model>, and the weights download on first use. llama.cpp is one step more involved, a compiled binary or a container plus a model file, but still far short of a Python environment. vLLM and SGLang both expect a GPU-enabled Docker image or a Python virtual environment, and both ask you to choose flags such as --tensor-parallel-size and how much of the GPU to reserve before the server is useful for anything beyond a quick test. The vLLM guide covers that setup in full.
Where each one runs
llama.cpp covers the widest range on a single machine: plain CPU inference alongside CUDA, HIP and Metal backends, with quantization from 1.5-bit through 8-bit for fitting a model into limited memory. SGLang's own documentation lists support across NVIDIA, AMD, Intel Xeon, Google TPU, Ascend NPU and Moore Threads hardware, more vendors than either vLLM or Ollama document. vLLM's documentation centers on NVIDIA and AMD GPUs. Ollama runs on NVIDIA, AMD and Apple Silicon, aimed at one machine rather than a fleet of them. None of that changes what a rented GPU VM on DeployGPU can run, since every offer here is an NVIDIA card.
Decision table
| If you need | Pick |
|---|---|
| An OpenAI-compatible API for a script or app, on one GPU or several | vLLM |
| Heavy reuse of shared prompt prefixes, such as long system prompts or few-shot examples | SGLang, though vLLM caches prefixes too |
| A chat interface with no Docker or Python setup | Ollama |
| To run a model on a laptop, or the smallest possible file on disk | llama.cpp |
My own default splits on the same line as the table: vLLM or SGLang the moment something other than a person is calling the model, Ollama the moment a person is chatting directly. None of these projects is exclusive: vLLM and SGLang both cover the same core use case, an API server, and the choice between them is usually about which one has better day-one support for a specific new model such as Qwen or gpt-oss. Ollama and llama.cpp overlap the same way, and Ollama's own engine builds on ideas from the GGUF ecosystem that llama.cpp originated.
Where this fits on DeployGPU
The Open WebUI and Ollama template on DeployGPU gives Ollama with no setup at all, a private browser link and models pulled by tag. vLLM, SGLang and llama.cpp are all do-it-yourself on a plain GPU VM, reached over an SSH tunnel rather than a template link, since DeployGPU has no vLLM template and app links are for a browser, not for API traffic. The vLLM guide covers that setup end to end.
Opens the deploy page on the cheapest 1x 48 GB GPUs offer at 2026-10-16 01:27 UTC: QuantaCloud, $0.48/hr. You can pick another GPU there before paying.
Frequently asked questions
Is vLLM faster than Ollama?
They are built for different jobs. vLLM is built to serve many concurrent API requests efficiently, and Ollama is built to run a model with minimal setup for one user at a time. Comparing raw speed without stating the concurrency and hardware is not meaningful.
Can Ollama serve a production API the way vLLM does?
Ollama does expose an API, including an OpenAI-compatible subset, but it is designed around running a small number of models for a small number of clients rather than the concurrent request handling vLLM and SGLang are built for.
Why do vLLM and Ollama report different file sizes for what looks like the same model?
vLLM typically loads the native BF16 or FP8 checkpoint from Hugging Face, while Ollama pulls a GGUF file at a specific quantization level. Different formats and bit widths produce different file sizes for the same underlying model.
Do I need a GPU for llama.cpp?
No. It runs on CPU alone, though a GPU backend such as CUDA speeds it up considerably. This is one of the reasons it exists alongside GPU-first engines like vLLM and SGLang.
Which of these does DeployGPU support with a template?
Only Ollama, through the Open WebUI and Ollama template. vLLM, SGLang and llama.cpp all run on a plain GPU VM that you set up yourself over SSH.