vLLM vs llama.cpp
vLLM and llama.cpp side by side: what each one is, verified plan pricing and install commands where we have them, and the full review of each.
vLLM
High-throughput inference server for LLMs. PagedAttention memory management. The go-to for serious local or self-hosted serving.
llama.cpp
C++ inference engine for LLMs. GGUF format, quantization, CPU and Metal/CUDA support. The foundation most local tools build on.
At a glance
| Field | | L llama.cpp |
|---|---|---|
| Category | Local AI | Local AI |
| Website | github.com | github.com |
| Pricing | vLLM - free and open source (Apache-2.0) | llama.cpp - free and open source (MIT) |
| Install | uv pip install vllm --torch-backend=auto or vllm download-kernels or uv pip install vllm --extra-index-url https://wheels.vllm.ai/rocm/ | curl -LsSf https://llama.app/install.sh | sh or brew install llama.cpp or winget install llama.cpp |
| Models | Serves Hugging Face (or ModelScope) models by name: vllm serve <model>, for example vllm serve Qwen/Qwen2.5-1.5B-Instruct, starts an OpenAI-compatible server. Covers decoder-only, MoE, hybrid state-space, multimodal, embedding and reward models on NVIDIA, AMD and Intel GPUs, TPUs, CPUs, Ascend NPUs and Apple Silicon. | Runs any model in GGUF format, quantized from 1.5-bit to 8-bit, on CPU, Metal, CUDA, HIP, Vulkan, SYCL, WebGPU and other backends. Pass -hf to pull a GGUF straight from Hugging Face, for example llama cli -hf ggml-org/Qwen3.5-0.8B-GGUF, or -m for a local file. llama serve exposes an OpenAI-compatible API. |
| Last verified | October 17, 2026 | October 17, 2026 |
In Depth
vLLM
vLLM is the inference server you reach for when llama.cpp is too small and cloud APIs are too expensive. Originally from UC Berkeley, vLLM pioneered PagedAttention (a KV-cache memory layout inspired by OS virtual memory paging) and continuous batching, which together give it throughput that outperforms most alternatives on the same hardware. It supports most modern model architectures, loads Hugging Face weights directly, and exposes an OpenAI-compatible HTTP endpoint. For any team self-hosting a model on an H100 or consumer GPU with serious traffic, vLLM is the production-grade default. It is less consumer-friendly than Ollama but the throughput gap is meaningful at scale.
llama.cpp
llama.cpp is the C++ inference library from Georgi Gerganov that most of the local-LLM ecosystem is built on. Ollama, LM Studio, Jan, and GPT4All all ship a wrapped or forked llama.cpp at their core. Using it directly gives you the lowest overhead and the most control: quantization down to 2-bit, Metal acceleration on Apple Silicon, CUDA on NVIDIA, Vulkan on AMD, and first-class support for the GGUF weight format that the entire local ecosystem has standardized on. For developers who want to embed local inference in a product rather than wrap an existing tool, llama.cpp is the layer to integrate against. The bundled llama-server binary exposes an OpenAI-compatible HTTP endpoint so any existing tool can swap to a local model with one config change.
FAQ
Answers taken from each tool's verified fact sheet, with the date it was last checked.
How much does vLLM cost?
As of October 17, 2026: vLLM - free and open source (Apache-2.0). Source: vllm-project/vllm README, vLLM quickstart, GitHub releases, v0.25.0 release notes.
How do I install vLLM?
Official install commands as of October 17, 2026: uv pip install vllm --torch-backend=auto or vllm download-kernels or uv pip install vllm --extra-index-url https://wheels.vllm.ai/rocm/.
Which models does vLLM use?
As of October 17, 2026: Serves Hugging Face (or ModelScope) models by name: vllm serve <model>, for example vllm serve Qwen/Qwen2.5-1.5B-Instruct, starts an OpenAI-compatible server. Covers decoder-only, MoE, hybrid state-space, multimodal, embedding and reward models on NVIDIA, AMD and Intel GPUs, TPUs, CPUs, Ascend NPUs and Apple Silicon.
How much does llama.cpp cost?
As of October 17, 2026: llama.cpp - free and open source (MIT). Source: ggml-org/llama.cpp README, Install docs, Server README, GitHub releases.
How do I install llama.cpp?
Official install commands as of October 17, 2026: curl -LsSf https://llama.app/install.sh | sh or brew install llama.cpp or winget install llama.cpp.
Which models does llama.cpp use?
As of October 17, 2026: Runs any model in GGUF format, quantized from 1.5-bit to 8-bit, on CPU, Metal, CUDA, HIP, Vulkan, SYCL, WebGPU and other backends. Pass -hf to pull a GGUF straight from Hugging Face, for example llama cli -hf ggml-org/Qwen3.5-0.8B-GGUF, or -m for a local file. llama serve exposes an OpenAI-compatible API.
