ggml-org/llama.cpp vs vllm-project/vllm
Compare ggml-org/llama.cpp and vllm-project/vllm using the current verified snapshot: positioning, license, deployment, use cases, limitations, and original sources.
ggml-org/llama.cpp
llama.cpp is a dependency-light C/C++ implementation for running LLM inference across diverse hardware. It provides tools for quantization, benchmarking, and serving, requiring models in the GGUF format.
- License
- MIT
- Deployment
- Refer to project documentation
- Use cases
- Developers and AI engineers who need to run LLM inference via a CLI, API, or library and want to use 1.5-bit to 8-bit integer quantization to reduce memory use. · Users who need to benchmark inference performance, measure perplexity, or constrain output formats using grammars.
- Updated
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vllm-project/vllm
vLLM is a high-throughput, memory-efficient inference and serving engine for large language models. It provides an OpenAI-compatible API server and uses PagedAttention to manage attention key and value memory efficiently.
- License
- Apache-2.0
- Deployment
- Python environment
- Use cases
- Developers, AI engineers, and operations teams needing high-throughput and memory-efficient inference for large language models. · Users requiring distributed inference with tensor, pipeline, data, expert, and context parallelism.
- Updated
- 2026-07-17T04:38:12Z
How to choose
First eliminate options that fail required deployment, license, or use-case constraints; then inspect each detail page for limitations and direct evidence.