Is llama.cpp the same as Ollama?

No. llama.cpp is the inference engine. Ollama is a tool that downloads models, runs them with llama.cpp as its supported back end, and offers its own API and Modelfile settings.

Do I need a GPU to use llama.cpp?

No. It runs on CPUs. A GPU makes it faster, and the hybrid mode lets a model larger than the GPU’s memory run with part of it on the GPU.

Can llama.cpp serve many users?

Yes, to a point. The server has parallel slots and continuous batching switched on by default. Plan memory for each active conversation’s KV cache.

Which model files does it need?

GGUF files. The project’s tools convert models to GGUF and quantise them, and many publishers distribute ready-made GGUF files.

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Private AI glossary · updated 2026-10-16

llama.cpp
The open-source engine that runs models on laptops, mini PCs and servers.

Short answer. llama.cpp is an open-source inference engine, written in plain C and C++ with no dependencies, that runs language models on ordinary hardware: laptops, mini PCs, workstations and servers. It supports Apple silicon, x86 processors and GPUs from NVIDIA and AMD, with Vulkan and SYCL back ends for others. It loads models in the GGUF format, quantised anywhere from 1.5-bit to 8-bit, and can split a model between GPU and CPU when it does not fit in video memory. Its built-in server offers an OpenAI-compatible API, parallel users and continuous batching. Ollama uses it as its back end.

What it does

The project’s stated goal is to run language and vision-language models with minimal setup and state-of-the-art performance on a wide range of hardware, locally and in the cloud. It is built on the ggml library. As of October 2026 its quick start installs a single program and runs a model straight from a Hugging Face repository with the commands llama cli and llama serve.

Three features matter most for local use. Quantisation, from 1.5-bit to 8-bit integers, shrinks a model so that it fits in modest memory. Hybrid CPU and GPU inference lets a model larger than the GPU’s memory run with part of it on the GPU. And the same code runs on very different machines, with Apple silicon described as a first-class target.

Hardware back ends

Back end	Target devices
Metal	Apple silicon
CUDA	NVIDIA GPUs
HIP	AMD GPUs
Vulkan	GPUs
SYCL	Intel GPUs
OpenVINO (listed as in progress)	Intel CPUs, GPUs and NPUs
CANN	Ascend NPUs
Hexagon	Snapdragon

The built-in server

The server turns a model into a network service. By default it listens on 127.0.0.1, port 8080, so it is reachable only from the same machine until you change the host setting. The defaults below are those in the documentation as of October 2026.

Setting	What it controls	Default in the documentation
-c, --ctx-size	Context size	0, meaning taken from the model
-np, --parallel	Number of server slots for simultaneous requests	-1, meaning automatic
-cb, --cont-batching	Continuous batching	Enabled
-ctk, -ctv	Data type of the KV cache for keys and values	f16; q8_0 and q4_0 are among the options
-fa, --flash-attn	Flash attention	Auto
--api-key	Shared API keys, several allowed, comma-separated	None
--metrics	Prometheus-style monitoring endpoint	Disabled
--lora	LoRA adapter files to apply	None

Strengths and what it leaves to you

Strengths: one small program, no cloud account, models run offline once downloaded, and broad hardware support. The llama-bench tool measures speed on your own machine.
Its server documentation lists API keys, TLS certificate options and a monitoring endpoint. It does not list named user accounts, roles or a per-user audit log.
Some server features are marked experimental, such as built-in tools for agents, which the documentation says not to enable in untrusted environments.
The server handles several users on one machine. For many simultaneous users on NVIDIA or AMD GPUs, a serving engine such as vLLM is built for throughput.

Where ANVE stands

ANVE’s models are reachable today through the runtime’s own OpenAI-compatible endpoint, which is Ollama or vLLM. Ollama lists llama.cpp as its supported back end. The governed ANVE API, with a key per user or agent and every request in the audit trail, is not on ANVE yet.

Questions people ask

Is llama.cpp the same as Ollama?: No. llama.cpp is the inference engine. Ollama is a tool that downloads models, runs them with llama.cpp as its supported back end, and offers its own API and Modelfile settings.
Do I need a GPU to use llama.cpp?: No. It runs on CPUs. A GPU makes it faster, and the hybrid mode lets a model larger than the GPU’s memory run with part of it on the GPU.
Can llama.cpp serve many users?: Yes, to a point. The server has parallel slots and continuous batching switched on by default. Plan memory for each active conversation’s KV cache.
Which model files does it need?: GGUF files. The project’s tools convert models to GGUF and quantise them, and many publishers distribute ready-made GGUF files.

What is not real yet

No sealed ANVE unit exists; every image of the appliance on this site is a labelled render. The software runs today on a development machine. No customers yet, no certifications, no delivery date, and the final price is not announced.

Reservations are ₹999 in India or US$12 elsewhere, refundable in full until you confirm an order in writing (terms). Offices that cannot use cloud AI can apply to be one of ten design partners.

llama.cppThe open-source engine that runs models on laptops, mini PCs and servers.