Llm | martinuke0's Blog

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.

Diagram of a Rust service routing requests to multiple LLM providers.

Implementing Liter-LLM: Architecting Rust-Powered Polyglot Bindings for Multi-Provider LLM Integration at Scale

A deep dive into the design, Rust implementation, and scaling tricks behind a multi‑provider LLM integration layer that runs in production.

Diagram of Rust and LiteLLM integration.

Architecting LiteLLM with Rust: Building High-Performance Polyglot Bindings for Multi-Provider LLM Orchestration

A deep dive into using Rust to extend LiteLLM, creating fast, type‑safe bindings that let engineers orchestrate multiple LLM providers from a single, performant API.