Llm | martinuke0's Blog

Illustration of a tiny neural network being compressed for a microcontroller.

Optimizing Small Language Models: Pruning, Quantization, and Deployment for Local Edge Inference

A deep dive into pruning, quantization, and production‑ready deployment of compact LLMs on edge hardware, with code snippets and best‑practice patterns.

Diagram of Rust core communicating with multiple LLM provider APIs.

Implementing Lite-LLM: Architecting Rust-Powered Polyglot Bindings for Multi-Provider LLM Integration and Deployment

A step‑by‑step guide to designing a Rust core that exposes idiomatic bindings for Python, Node.js, and Go, enabling seamless multi‑provider LLM orchestration in production.

Illustration of a tiny neural network on a microcontroller.

Optimizing Small Language Models: Quantization, Hardware Acceleration, and Local Edge Inference Deployment

A deep‑dive into quantization methods, hardware acceleration choices, and edge‑deployment architectures that let engineers run performant LLMs on constrained hardware.

Illustration of Rust code connecting to several large language model APIs.

Implementing Liter-LLM: Architecting Rust-Powered Polyglot Bindings for Multi-Provider Large Language Models

Liter-LLM shows how Rust can serve as a high‑performance bridge to multiple LLM APIs, delivering async safety, unified error handling, and extensible bindings for production workloads.

A microcontroller board next to a tiny neural network diagram.

Optimizing Small Language Models: Quantization, Hardware Acceleration, and Efficient Local Edge Inference

A step‑by‑step guide for engineers who want to run LLMs locally on constrained hardware, covering quantization methods, hardware accelerators, and proven deployment patterns.