llama.cpp Review

llama.cpp is an LLM inference project in plain C/C++ with no dependencies. The README lists a very broad set of backends: BLAS, BLIS, CANN for Ascend NPUs, CUDA for NVIDIA, HIP for AMD, Hexagon for Snapdragon, IBM zDNN, MUSA for Moore Threads, Metal for Apple Silicon, OpenCL for Adreno, OpenVINO (in progress), RPC, SYCL for Intel GPUs, VirtGPU, Vulkan, WebGPU, and ZenDNN for AMD CPUs. On CPUs it uses ARM NEON and Accelerate on Apple, AVX, AVX2, AVX512, and AMX on x86, and supports RISC-V, and a hybrid CPU and GPU mode runs models larger than your VRAM. Quantization spans 1.5-bit to 8-bit including 2-, 3-, 4-, 5-, and 6-bit. The server provides an OpenAI-compatible API with a built-in web UI, parallel and speculative decoding, embeddings, and reranking, and vision-language models are supported.

There is a curl install script for Linux and macOS and a PowerShell script for Windows, a guided setup at llama.app, Docker images, prebuilt binaries on the GitHub releases page, and building from source with the linked build guide. You can download a model directly from Hugging Face with the -hf flag, for example llama cli -hf ggml-org/Qwen3.5-0.8B-GGUF or llama serve -hf ggml-org/Qwen3.5-0.8B-GGUF. Hardware needs depend on the model and quantization you choose, so size by file size plus context memory.

llama.cpp is a toolbox of command-line programs and a server rather than a polished app: you choose the model, quantization, context size, and layer offloading yourself. The built-in web UI in the server gives you a basic chat, and the OpenAI-compatible API lets any client talk to it. This section reflects the README and OpenAlternative; we did not run benchmarks.

• Backends for CUDA, HIP, Metal, Vulkan, SYCL, OpenCL, CANN, MUSA, and more, plus CPU-only inference
• Hybrid CPU and GPU inference for models larger than VRAM
• Quantization from 1.5-bit to 8-bit in the GGUF format
• OpenAI-compatible server with a built-in web UI, embeddings, and reranking
• Parallel and speculative decoding and grammar-constrained (GBNF) output
• Vision-language model support
• Direct downloads from Hugging Face with the -hf flag

• The widest hardware coverage of any inference engine, including unusual accelerators
• Fine control over quantization, memory, and performance
• MIT license and a very large community, with about 131,000 stars
• Many projects build on it, so improvements flow to the whole ecosystem

• Command-line and flag heavy, so it is a steeper start than Ollama or LM Studio
• No model-management layer comparable to higher-level tools, so you handle model files and formats yourself
• Fast development means flags, defaults, and supported architectures change often
• The captured README does not document its limitations or supported model families, which live in separate docs
• Some backends, such as OpenVINO, are still in progress

llama.cpp is free under the MIT license. Your costs are the hardware and electricity, plus model downloads.

LM Studio is a graphical desktop app that wraps local model discovery, download, and chat, and Together AI and Fireworks AI are hosted inference services. llama.cpp is the engine under many such tools. Choose it when you want a headless server, an embedded runtime, or control over performance, and choose Ollama or LM Studio when you want convenience. Ollama is the middle ground: a friendlier wrapper with sensible defaults.

• Developers embedding local inference in their own software
• Users who want maximum control over quantization and hardware use
• Running models on unusual or modest hardware, including CPU-only machines
• Servers that need an OpenAI-compatible endpoint without extra layers

• Beginners who want a one-click chat app
• Teams that want a managed inference service
• Anyone who does not want to manage model files and flags