vLLM vs llama.cpp
vLLM is a Python engine for high-throughput, multi-user LLM serving. llama.cpp is a C/C++ engine for running quantized GGUF models locally.
vLLM is an open-source Python library for serving large language models (LLMs) at high throughput to many concurrent users, built around PagedAttention and continuous batching. llama.cpp is an open-source C/C++ engine that runs quantized GGUF models on CPUs, GPUs and Apple silicon with minimal setup. Both can launch an OpenAI-compatible API server.
PagedAttention stores the key-value (KV) cache in fixed-size blocks, 16 tokens each by default in the 2023 vLLM paper. That paper found earlier systems used only 20.4 percent to 38.2 percent of KV cache memory for actual token states. It reports 2x to 4x the throughput of FasterTransformer and Orca at the same latency. vLLM also runs tensor, pipeline and expert parallelism across GPUs. llama.cpp has no dependencies and supports integer quantization from 1.5-bit to 8-bit. It ships CUDA, HIP, Metal, Vulkan and SYCL backends. Its hybrid mode splits a model larger than the GPU's video memory (VRAM) between GPU and CPU.
Run vLLM when a GPU server must sustain throughput across many simultaneous requests. Run llama.cpp on a single workstation or laptop, or when a quantized model is larger than VRAM and must spill into system memory.
Sources
Source | Publisher |
|---|---|
vLLM | |
ggml-org | |
Efficient Memory Management for Large Language Model Serving with PagedAttention | arXiv |
- Publisher
vLLM
- Publisher
ggml-org
Last verified October 7, 2026.
- vllm vs llama.cpp
- llama.cpp vs vllm
- vLLM vs Ollama
- llama-server
- vllm serve
- GGML
- local LLM inference engine
- LLM serving engine
- CPU GPU offload
- tensor split