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98.css VS llama.cpp

Compare 98.css VS llama.cpp and see what are their differences

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98.css logo 98.css

A design system for building faithful recreations of old UIs

llama.cpp logo llama.cpp

LLM inference in C/C++. Contribute to ggml-org/llama.cpp development by creating an account on GitHub.
  • 98.css Landing page
    Landing page //
    2020-04-22
Not present

98.css features and specs

  • Nostalgic Appeal
    98.css provides a nostalgic Windows 98 aesthetic, appealing to users who have an affinity for retro computing and creating a unique visual experience.
  • Lightweight
    The framework is lightweight, making it easy to integrate without significantly increasing page load times.
  • Minimalist Design
    It offers a minimalist and straightforward design, which can be beneficial for projects that require simplicity and less visual clutter.
  • Easy Customization
    While it adheres to a specific retro theme, the CSS can be customized to suit the needs of the developer, allowing for flexible design applications.

Possible disadvantages of 98.css

  • Limited Modern Features
    98.css focuses on replicating the Windows 98 look and feel, which means it lacks support for more modern web design trends and features.
  • Niche Audience
    The retro aesthetic may not appeal to all users and could be inappropriate for certain professional or modern applications.
  • Style Constraints
    The framework’s dedication to the Windows 98 aesthetic can limit creativity, making it difficult to diverge from the retro style if project requirements change.
  • Potential Compatibility Issues
    While lightweight, integrating 98.css with other modern frameworks or libraries may cause compatibility issues or require additional workarounds.

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 library’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

98.css videos

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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 98.css and llama.cpp)

98.css

llama.cpp

Design Tools
100 100%
Design Tools
0% 0
AI
0 0%
AI
100% 100
Developer Tools
100 100%
Developer Tools
0% 0
LLM
0 0%
LLM
100% 100

User comments

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Social recommendations and mentions

98.css might be a bit more popular than llama.cpp. We know about 21 links to it since March 2021 and only 18 links to llama.cpp. 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.

98.css mentions (21)

  • llama.cpp
    Wow it’s aggressively vibe coded. Nothing inherently wrong with that, but it looks a bit amateurish which is funny. I’m still waiting on 98.css to become the standard for vibe coded sites. You don’t have to read docs anyway if you’re just using LLMs! All you have to do is say “use 98.css” and you have a 10/10 site https://jdan.github.io/98.css/. - Source: Hacker News / 22 days ago
  • Slightly reducing the sloppiness of AI generated front end
    There's an entire lightweight CSS lib around the Win9x look as well: https://jdan.github.io/98.css/. - Source: Hacker News / 3 months ago
  • Claude Design by Anthropic Labs
    Nothing screams old school more than 98.css https://jdan.github.io/98.css/. - Source: Hacker News / 5 months ago
  • Celebrate 50 years of Microsoft with the company's original source code
    I had never heard of this but it's description for it's git is what I hope and dream for anytime I go look at a project related to or having a GUI. Reference at https://jdan.github.io/98.css/. - Source: Hacker News / over 1 year ago
  • Recreating History: Building a Windows 98 Disk Defrag Simulator with Modern Web Tech
    TailwindCSS: To style the app, along with 98.css for bringing in the Windows 98 aesthetic. - Source: dev.to / about 2 years ago
View more

llama.cpp mentions (18)

  • llama.cpp
    It's from https://github.com/ggml-org/llama.cpp -- not associated with Meta, it's been around for years, and surely they know about it -- so I would guess either it's not a trademark violation or they don't care. - Source: Hacker News / 22 days ago
  • llama.cpp
    Anything that suggests curl into bash just plain sketches me out. Git clone llama.cpp and build it, it's not hard. https://github.com/ggml-org/llama.cpp/blob/master/docs/build.md literally just a few steps for the basics: git clone https://github.com/ggml-org/llama.cpp cmake -B build cmake --build build --config Release. - Source: Hacker News / 22 days ago
  • llama.cpp
    I was a bit suspicious of the url but it is also listed on llama.cpp github https://github.com/ggml-org/llama.cpp. - Source: Hacker News / 22 days ago
  • Running a 26B MoE on an 8 GB Jetson by streaming experts from SSD
    TurboFieldfare proves the idea beautifully, but it is a bespoke runtime: two supported models, Apple platforms only, custom kernels for everything. I wanted the same idea for the other cheap 8 GB machine on my desk, a Jetson Orin Nano, and I wanted it for any MoE model I could quantize. So instead of porting the runtime, I grafted the idea into llama.cpp, which already runs on the Jetson and already has... - Source: dev.to / about 1 month ago
  • How to Build a Local AI Workspace Like PewDiePie's Odysseus: Hardware, Models, and Cost
    Llama.cpp is a flexible runtime for GGUF models across CPU, CUDA, Metal, and other backends. - Source: dev.to / about 1 month ago
View more

What are some alternatives?

When comparing 98.css and llama.cpp, you can also consider the following products

Tailwind CSS - A utility-first CSS framework for rapidly building custom user interfaces.

LM Studio - Discover, download, and run local LLMs

Windows95 - Windows 95 in Electron. Runs on macOS, Linux, and Windows.

Ollama - The easiest way to run large language models locally

Virtual Windows 98 - Use Windows 98 in your browser

Ava PLS - Desktop app for running LLMs locally

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