# vLLM vs Ollama vs TGI: Choosing an Inference Server

URL: https://donedeploy.com/vllm-ollama-tgi/
Published: 2026-08-27

Once you decide to run an open model yourself, the next question is what serves it. Three names come up most often: vLLM, Ollama and Text Generation Inference (TGI). They overlap, but they were built for different jobs, and picking the wrong one tends to show up later as poor throughput or awkward operations. This post compares them on the points that matter in practice.

## What an inference server does

An inference server loads model weights onto hardware, accepts requests over HTTP, schedules them, and streams tokens back. The hard part is scheduling. Good servers use continuous batching: new requests join a running batch instead of waiting for it to finish, which keeps the GPU busy. They also manage the KV cache carefully so many concurrent requests fit in memory.

## vLLM

vLLM came out of research on PagedAttention, a technique that manages KV cache memory in blocks rather than one large reservation per request. In practice that means high throughput under concurrent load.

- Strong choice for GPU serving with many simultaneous users.
- OpenAI compatible API out of the box.
- Supports tensor parallelism across GPUs, many quantisation formats, prefix caching and LoRA adapters.
- Runs mainly on NVIDIA GPUs, with support for AMD and other accelerators that varies by version.
- Exposes Prometheus metrics, which makes it straightforward to monitor on Kubernetes.

The cost is operational weight. vLLM expects a proper GPU, takes time to start, and has many tuning flags (maximum model length, GPU memory utilisation, batch limits) that you should set deliberately.

## Ollama

Ollama is built around developer experience. One command pulls and runs a model, and it handles quantised GGUF files through llama.cpp underneath.

- Excellent on laptops, workstations and small servers, including Apple Silicon and CPU only machines.
- Simple model management with a local library and Modelfiles.
- Offers its own API plus an OpenAI compatible endpoint.
- Fine for a handful of users, internal tools and prototypes.

It is not designed for high concurrency production serving. Its batching and scheduling are simpler than vLLM’s, and it has fewer controls for multi-tenant traffic. Many teams use Ollama on developer machines and vLLM or TGI in production, with the same OpenAI style client code in both.

## TGI

Text Generation Inference is Hugging Face’s production server. It shares many features with vLLM: continuous batching, tensor parallelism, quantisation, streaming and Prometheus metrics.

- Tight integration with the Hugging Face Hub and its model formats.
- Messages API compatible with the OpenAI chat format.
- Mature container images and documentation for deployment.

Before you commit to it for a new deployment, check the project’s recent releases and roadmap, as this part of the ecosystem moves quickly. For existing installations it remains a reasonable option.

## llama.cpp

Worth a mention: llama.cpp is the engine under Ollama and can run directly with its own server. It is the most portable option, running on CPU, Apple Silicon and a wide range of GPUs, and it is useful when you need a small footprint or edge deployment.

## Side by side

vLLM Ollama TGI Best fit Production GPU serving Local and small scale Production GPU serving Concurrency High Low to moderate High Hardware Mainly NVIDIA GPUs CPU, Apple Silicon, GPUs Mainly NVIDIA GPUs OpenAI compatible Yes Yes Yes (Messages API) Ease of setup Moderate Very easy Moderate

## How to choose

1. **Many users, GPUs available:** start with vLLM.
2. **Developers and prototypes, mixed hardware:** Ollama.
3. **Already invested in the Hugging Face stack with TGI running:** keep it, but watch its roadmap.
4. **Edge, CPU only or very small footprint:** llama.cpp.

Whatever you pick, benchmark it on your own prompts with realistic concurrency and context lengths. Published numbers rarely match your traffic shape, and the differences between servers change from release to release.

## Keep the choice reversible

Put a thin gateway in front of the server and call it through an OpenAI compatible client. Then moving from Ollama in development to vLLM in production, or swapping servers later, is a configuration change rather than a rewrite.

DoneDeploy deploys and tunes these servers on Kubernetes and plain VMs, with metrics and alerting wired in. If you want help choosing or benchmarking one for your workload, [get in touch](https://donedeploy.com/lets-talk/).