← back
Harnessing the Power of LLMs Locally: Mithun Hunsur
Takeaway
llm.rs offers a Rust-native, library-first alternative to llama.cpp for embedding local quantized LLMs into applications with full control over inference.
Summary
- Mithun Hunsur (Phil Pax, Ambient) maintains llm.rs, a Rust library for local inference of LLMs as an alternative to llama.cpp.
- Trade-offs of local models: smaller (so less general intelligence) but better for focused tasks, lower latency (no full-prompt round trip), zero per-token cost once hardware is owned, full fine-tune freedom, and full privacy.
- Quantization is what makes consumer-hardware inference viable — compresses billions of float params into 4/5/8-bit formats with minor quality loss; smaller weights also let CPUs/GPUs process more per cycle.
- llm.rs principles: library not application, decoupled from any single app, multi-architecture (LLaMA, Falcon, etc.), Rust-native API, multiple backends (CPU/GPU/AMD/Intel), cross-platform; users own loading, sessions, sampling and callbacks.
- Community projects on top: local.ai installer app, llm-chain (LangChain-for-Rust), Floom flowchart workflow builder; speaker's own demos include an LM Discord bot and others.
local-llmsrustquantization
Original description
Discover llm, a revolutionary Rust library that enables developers to harness the potential of large language models (LLMs) locally. By seamlessly integrating with the Rust ecosystem, llm empowers developers to leverage LLMs on standard hardware, reducing the need for cloud-based APIs and services. In this talk, I'll explore llm's key features, including its high-speed inference, support for popular LLM architectures, and its lightweight design. Through practical examples, I'll showcase how llm can be applied in content generation, code completion, and language understanding tasks. Additionally, I'll discuss the challenges of deploying and maintaining LLMs locally, along with best practices and real-world experiences from early adopters. Recorded live in San Francisco at the AI Engineer Summit 2023. See the full schedule of talks at https://ai.engineer/summit/schedule & join us at the AI Engineer World's Fair in 2024! Get your tickets today at https://ai.engineer/worlds-fair About Mithun Hunsur Mithun is a seasoned polyglot programmer and engineer with a passion for exploring the depths of computer science. With experience spanning from the low-level to the highest reaches of software development, Mithun has worked on a diverse range of projects across various industries. During the day, he works on Ambient, an open-source runtime/engine for building high-performance multiplayer games and 3D applications. In his free time, he's a tinkerer at heart, diving into the world of game reverse engineering and modification, low-level and embedded programming, virtual and augmented reality, compiler and language hacking, human-computer interface research, and computer architecture and design. Beyond his work in the tech industry, Mithun also has a creative side, dabbling in photography, writing, and AI art. He brings a unique perspective to his work, combining his passion for technology with his artistic sensibilities to build projects that are both innovative and visually stunning.