llama.cpp vs Vast.ai — Comparison

llama.cpp

LLM inference in C/C++. Contribute to ggml-org/llama.cpp development by creating an account on GitHub.

Getting started with llama.cpp is straightforward. Here are several ways to install it on your machine: Once installed, you'll need a model to work with. Head to the Obtaining and quantizing models section to learn more. The main goal of llama.cpp is to enable LLM inference with minimal setup and state-of-the-art performance on a wide range of hardware - locally and in the cloud. Typically finetunes of the base models below are supported as well. Instructions for adding support for new models: HOWTO-add-model.md After downloading a model, use the CLI tools to run it locally - see below. The Hugging Face platform provides a variety of online tools for converting, quantizing and hosting models with llama.cpp: To learn more about model quantization, read this documentation For authoring more complex JSON grammars, check out https://grammar.intrinsiclabs.ai/ If your issue is with model generation quality, then please at least scan the following links and papers to understand the limitations of LLaMA models. This is especially important when choosing an appropriate model size and appreciating both the significant and subtle differences between LLaMA models and ChatGPT: The XCFramework is a precompiled version of the library for iOS, visionOS, tvOS, and macOS. It can be used in Swift projects without the need to compile the library from source. For example: The above example is using an intermediate build b5046 of the library. This can be modified to use a different version by changing the URL and checksum. Command-line completion is available for some environments. There was an error while loading. Please reload this page. There was an error while loading. Please reload this page.

Vast.ai

Real-Time GPU Pricing

Vast.ai is a GPU compute marketplace founded on one idea: whoever controls compute controls AI. We exist to make sure that power stays distributed. Christian Horne — a fellow thinker and builder who also published on LessWrong — shared Jake's view that the compute scaling thesis had profound implications, not just for AI development, but for who would control it. Both saw the same thing: if whoever controlled the most compute controlled the most powerful AI, then the future of artificial general intelligence would be determined by who had the deepest pockets, not who had the best ideas. On June 28, 2016, they incorporated Vast.ai. The founding thesis fit on a napkin: the world was full of underutilized GPU hardware — in gaming rigs, mining farms, research labs, and small data centers — and the people who needed that compute most couldn't afford the hyperscaler rates. But the motivation was never purely commercial. A world where compute flows freely to thousands of independent researchers is a fundamentally different world than one where it is locked behind the pricing walls of a few incumbents. “A world where compute flows freely to thousands of independent researchers is a fundamentally different world than one where it is locked behind the pricing walls of AWS, GCP, and Azure.” What Jake predicted. What the team built. How the field caught up. Jake Cannell publishes a series of essays on LessWrong arguing that intelligence is fundamentally a function of compute — not clever algorithms or hand-engineered modules. Christian Horne (lahwran), a fellow LessWrong contributor, shares the same conviction. The two become collaborators. AlexNet breaks ImageNet benchmarks by scaling a known neural network architecture on GPUs — exactly as the scaling hypothesis predicted. The deep learning revolution begins. Jake publishes his landmark essay arguing that the human brain is a single, general-purpose learning algorithm — not a zoo of specialized circuits. He predicts AlphaGo two years before it happens and forecasts human-level vision (~2024±3) and language via scaled deep learning. Jake Cannell and Christian Horne incorporate Vast.ai as a Delaware C Corporation. The founding thesis: the world is full of underutilized GPU hardware, and the people who need that compute most can’t afford hyperscaler rates. The market needs a two-sided platform. For two years, Jake and Christian build the marketplace platform end-to-end: host onboarding, search interface, pricing engine, Docker-based instance management — engineered to work across heterogeneous hardware and wildly different network conditions. Vast.ai launches — not with a press release, but the way honest products launch: to friends, family, and a post on Hacker News. GPU compute 3–5x cheaper than AWS, available in seconds, no enterprise contract required. Early independent hosts join the platform. The marketplace concept is validated — developers get cheaper GPUs, hosts monetize idle har

llama.cpp

Vast.ai

llama.cpp

Vast.ai

llama.cpp

Vast.ai