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ncurses VS llama.cpp

Compare ncurses VS llama.cpp and see what are their differences

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Contents:
  1. ยป Base Details
  2. ยป Videos
  3. ยป Reviews
  4. ยป Alternatives

ncurses logo ncurses

ncurses (new curses) is a programming library that provides an API which allows the programmer to...

llama.cpp logo llama.cpp

LLM inference in C/C++. Contribute to ggml-org/llama.cpp development by creating an account on GitHub.
  • ncurses Landing page
    Landing page //
    2021-09-21
Not present

ncurses features and specs

  • Portability
    ncurses is highly portable and can run on a wide range of Unix-like systems, making it suitable for developing cross-platform terminal applications.
  • Terminal Control
    Provides comprehensive terminal control capabilities, allowing developers to manipulate text, colors, and screen dimensions easily.
  • Wide Adoption
    Being a widely-used library, ncurses has an extensive user base and a wealth of resources and community support available.
  • Enhanced User Interface
    Allows the creation of text user interfaces with features like windows, menus, and forms, improving the interactivity of terminal applications.
  • Extensive Function Set
    Offers a rich set of functions for handling input, output, and special effects in text-mode applications, facilitating complex interface designs.

Possible disadvantages of ncurses

  • Learning Curve
    Has a steep learning curve, especially for developers who are new to terminal-based UI programming.
  • C Programming Requirement
    Primarily a C library, which might not be appealing to developers more comfortable with modern or higher-level languages.
  • Limited to Text Mode
    Bound by the constraints of text mode, limiting the kinds of interactions and graphical representations possible compared to graphical user interfaces.
  • Complexity of Code
    Complex terminal applications can become unwieldy with a large amount of ncurses code, making maintenance challenging.
  • Terminal Compatibility
    While ncurses is designed to be portable, not all terminal emulators may fully support all its features, leading to potential inconsistencies.

llama.cpp features and specs

  • Performance
    llama.cpp is designed to run efficiently on a wide range of hardware, from high-end GPUs to more modest CPUs, making it highly adaptable and performant in various environments.
  • Portability
    The codebase is lightweight and can be compiled across different operating systems including Linux, macOS, and Windows, ensuring wide accessibility and ease of deployment.
  • Ease of Use
    The repository provides comprehensive documentation and examples, making it easier for developers to integrate and utilize the library in their projects.
  • Community Support
    Being an open-source project, llama.cpp benefits from community contributions, which help in its continuous improvement and maintenance.
  • Flexibility
    It allows developers to customize and extend the functionality to better fit specific use cases or integrate with other tools and systems.

Possible disadvantages of llama.cpp

  • Limited Features
    Compared to some other machine learning libraries or frameworks, llama.cpp may have fewer out-of-the-box features, requiring more custom development for certain applications.
  • Complexity for Beginners
    Despite good documentation, users without a solid background in machine learning or programming may find it difficult to fully utilize the libraryโ€™s capabilities.
  • Scalability
    While llama.cpp is designed to be performant, scaling it for very large datasets or extensive tasks might require significant optimization or additional resources.
  • Dependency Management
    As with many open-source projects, managing dependencies and ensuring compatibility with evolving third-party libraries can be challenging.

Analysis of llama.cpp

Overall verdict

  • llama.cpp is an excellent, high-performance open-source project that has become the de facto standard for running large language models locally on consumer hardware with minimal dependencies.

Why this product is good

  • Written in efficient C/C++ with no heavy dependencies, enabling fast inference even on CPUs
  • Supports GGUF quantization allowing large models to run on limited RAM and modest hardware
  • Cross-platform support including Windows, macOS, Linux, and even mobile and embedded devices
  • Hardware acceleration via CUDA, Metal, Vulkan, ROCm, and more
  • Extremely active community and rapid development with frequent updates and broad model support
  • Free and open-source under the MIT license, with a large ecosystem of tools and bindings built around it

Recommended for

  • Developers wanting to run LLMs locally without cloud dependencies
  • Privacy-conscious users who need offline inference
  • Hobbyists and researchers experimenting with quantized models on consumer hardware
  • Applications requiring lightweight, embeddable LLM inference
  • Users with limited GPU resources who need efficient CPU-based inference

ncurses videos

+ Add

Ncurses gameplay of PointandHit linux terminal game

llama.cpp videos

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Local AI just leveled up... Llama.cpp vs Ollama

More videos:

  • Review - AMD Mi50 32GB Speed Test: Ollama vs Llama.cpp (GPT-OSS & Qwen3 Benchmarks)
  • Review - Ollama vs VLLM vs Llama.cpp: Best Local AI Runner in 2026?

Category Popularity

0-100% (relative to ncurses and llama.cpp)

ncurses

llama.cpp

Bookmark Manager
100 100%
Bookmark Manager
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AI
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AI
100% 100
IDE
100 100%
IDE
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LLM
0 0%
LLM
100% 100

User comments

Share your experience with using ncurses and llama.cpp. For example, how are they different and which one is better?
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Social recommendations and mentions

Based on our record, llama.cpp seems to be more popular. It has been mentiond 13 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

ncurses mentions (0)

We have not tracked any mentions of ncurses yet. Tracking of ncurses recommendations started around Mar 2021.

llama.cpp mentions (13)

  • Ask HN: How close are we to local LLM models being useful? What's the impact?
    A good place to browse is the LocalLLaMa subreddit. [0] A good software to start is LM Studio [1]. Another popular alternative is Ollama [2]. A better software when you're used to it all is llama.cpp as it's usually a bit faster and more frequently updated [3]. A good place to get models is HuggingFace, particularly the Unsloth models [4] Most popular models lately to run on "regular" gaming PC's, workstations,... - Source: Hacker News / 26 days ago
  • llama-bench skipped FA on capable GPUs โ€” b9437 corrects it
    Yes, for a local source build: pull the latest commit from ggml-org/llama.cpp and recompile. Tagged binary releases lag the continuous builds. Check the GitHub releases page for a pre-built artifact if you want to skip compilation, but verify the build number includes the b9437 changes before treating it as current. - Source: dev.to / about 1 month ago
  • Introducing LlamaStash: a zero-overhead, terminal-native llama.cpp launcher
    That script grew up. Today I'm releasing LlamaStash, the first public release of a fast, cross-platform, terminal-native launcher for llama.cpp with zero overhead. - Source: dev.to / about 2 months ago
  • How fast is LlamaStash? Overhead, throughput, and a fair comparison with Ollama and LM Studio
    LlamaStash spawns the unmodified upstream llama-server. So three different questions follow from that, and there is a benchmark suite for each. - Source: dev.to / about 2 months ago
  • Why MTP doesn't speed up your llama.cpp inference (and how to actually fix it)
    Last week, I spent two days banging my head against a wall. I had just spun up a fresh llama.cpp build with multi-token prediction (MTP) support, loaded a quantized Qwen3 model, and ran my benchmark suite expecting that sweet 2-3x speedup everyone keeps talking about. - Source: dev.to / 2 months ago
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What are some alternatives?

When comparing ncurses and llama.cpp, you can also consider the following products

FINAL CUT - Library for creating terminal applications with text-based widgets

LM Studio - Discover, download, and run local LLMs

Ratatui - Rust library that's all about cooking up terminal user interfaces (TUIs).

Ollama - The easiest way to run large language models locally

newt - Programming library for color text mode, widget based user interfaces.

Ava PLS - Desktop app for running LLMs locally

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