Ollama, vLLM or llama.cpp: which runtime fits your situation?
llama.cpp is the C/C++ inference engine, Ollama the user-friendly layer around it for local experimentation, and vLLM the production-serving engine for GPUs with many concurrent users. Which one fits your workload.
For local experimentation and straightforward self-hosting, Ollama is usually the most practical choice. If maximum control, broad hardware support or deployment on edge devices matters, llama.cpp is often a better fit. If you are building a production API on GPU servers that must handle many concurrent inference requests, vLLM is generally the logical runtime.
The main difference is not which language model you want to run, but how you intend to serve it. llama.cpp is a C/C++ inference engine, Ollama adds a user-friendly management and API layer around it, while vLLM is designed from the ground up for efficient model serving, high throughput and substantial request concurrency.
llama.cpp: the inference engine close to the hardware
llama.cpp is the most fundamental of the three. The project implements LLM inference largely in C/C++ and aims to run models efficiently across a broad range of hardware. It supports conventional CPUs, Apple Silicon through Metal, NVIDIA GPUs through CUDA and several other GPU backends. Hybrid CPU/GPU inference is supported as well.
An important part of the llama.cpp ecosystem is GGUF. This format is widely used for quantized models, for example with 4-, 5- or 8-bit weights. Quantization allows models to run using less memory, usually with some trade-off in numerical precision — see also what hardware you need to run a language model locally.
This makes llama.cpp particularly useful when hardware constraints matter. A model does not necessarily have to reside entirely on a large data-centre GPU. Inference can run on a CPU, a Mac using unified memory, a compact GPU or a combination of CPU and GPU resources. Apple Silicon is explicitly treated as a first-class platform by the project.
llama.cpp is more than a library. Through llama-server, it can also expose an OpenAI-compatible HTTP server. Even so, its philosophy remains relatively low-level: you gain substantial control over model files, quantization, memory, offloading and hardware backends, but you are also expected to understand and manage more of those details yourself. That makes llama.cpp well suited to embedded and edge deployments, appliances, compact local servers and specialised systems where a full Python-based model-serving stack would be undesirable.
Ollama: making llama.cpp easier to use
Ollama sits one level higher. It uses llama.cpp as a supported inference backend while adding model management, a straightforward CLI, local APIs and configuration tooling. The current Ollama codebase builds against a pinned llama.cpp version and uses llama-server as part of its runtime architecture.
This removes many of the operational details you would otherwise manage yourself when using llama.cpp directly. Downloading, starting and switching models and exposing them through HTTP requires relatively little configuration. That makes Ollama attractive to developers who want to experiment locally with different open models without first assembling a complete inference environment.
For an SME, this can work particularly well for a local RAG prototype, an internal chatbot or an agent running on a developer workstation. Applications can communicate with the Ollama API while the underlying model remains local.
That simplicity is also its limitation. If you need exact control over how the inference engine is compiled, how memory is allocated or how an embedded deployment is structured, using llama.cpp directly puts you closer to the underlying engine. Ollama is also not primarily designed as an equivalent to specialised high-throughput inference engines for heavily loaded production systems: convenience and model management are more central design goals than maximising concurrency across a GPU cluster.
vLLM: built for production serving
vLLM starts from a different premise. The project focuses primarily on GPU-oriented model serving with high throughput. It integrates closely with the Hugging Face ecosystem and provides an OpenAI-compatible API server, but its most important differences are deeper inside the inference engine.
One of its core techniques is PagedAttention. This manages memory used by the attention key/value cache more efficiently, avoiding the need to treat the cache for each request as one large contiguous memory allocation. vLLM also uses continuous batching, allowing new requests to enter the inference workload dynamically instead of waiting for a fixed batch to complete.
This becomes important when many users access the same model concurrently. A single interactive chat running from a laptop creates a very different workload from tens or hundreds of simultaneous inference streams on a central server. In the latter case, scheduling, batching and KV-cache management can become major determinants of overall system performance. vLLM also supports capabilities including prefix caching, several forms of quantization and multiple strategies for parallel inference across GPUs and nodes.
The trade-off is complexity. vLLM makes most sense in environments where GPU infrastructure is available and a model is being operated as a central service. For a small model running on an office PC or an edge device, the architecture is usually unnecessarily heavy.
The difference between speed and throughput
When comparing these runtimes, the word "speed" is often used too loosely. There are at least two separate questions. The first is: how quickly does one user receive tokens? Model size, quantization, memory bandwidth and hardware may matter more here than the runtime choice alone.
The second is: how much inference traffic can an entire server process? At that point, techniques such as continuous batching become much more important. This is the type of workload vLLM is explicitly designed to address.
A runtime that performs extremely well for a single local user is therefore not automatically the best runtime for one hundred API clients. Conversely, an advanced GPU-serving stack offers little benefit if requests are almost never concurrent.
Ollama or llama.cpp directly?
These two are technically more closely related than either is to vLLM. Ollama uses llama.cpp as an inference backend and primarily removes management overhead.
Choose Ollama when your requirement is essentially: I want to start a model locally and let my application communicate with it. Choose llama.cpp directly when the requirement is: I want control over the inference engine, model files, build process, hardware backend and deployment.
For a prototype, Ollama can therefore provide the shorter route. If that same system later evolves into a specialised appliance or edge solution, direct use of llama.cpp may become more appropriate.
Practical rule of thumb
For hobby use, development, local RAG experiments and prototypes, Ollama is usually the easiest option. It provides a usable local API and model management without requiring much infrastructure work.
For edge and embedded systems, CPU inference, Apple Silicon and deployments where detailed control over memory and hardware matters, llama.cpp is the more natural choice. It is the underlying C/C++ inference engine and can be deployed very close to the available hardware.
For a central production API running on GPU infrastructure with many concurrent users, vLLM is usually the stronger starting point. Continuous batching, PagedAttention and its broader serving architecture are specifically intended for this type of workload.
In short: Ollama for convenience, llama.cpp for control and hardware flexibility, and vLLM for scalable GPU serving. The best runtime is not the one with the longest feature list, but the one whose architecture matches your actual inference workload.