Blog
Technical deep-dives, tutorials, and analysis on local LLM inference and the future of edge AI.
Embedding LLMs in Your Application: A Guide for Every Language
Most LLM tools force you through HTTP. Here's how to embed models directly in Python, Rust, Dart, Go, PHP, Node.js, C, and Zig — no server, no overhead, no separate process.
How to Run LLMs Locally Without Ollama
mullama is a drop-in Ollama replacement with native bindings for Python, Node.js, Go, PHP, Rust, and C/C++. Install, embed in-process, and run an OpenAI-compatible server — no daemon required.
Run LLMs on Flutter and Dart: Complete Guide to On-Device AI
How to run large language models locally on iOS and Android using llamafu, a Flutter FFI plugin built on llama.cpp. Covers text generation, chat, vision, tool calling, and performance tuning.
LLM Inference in Rust: Building a Modular Runtime with unillm
A deep dive into unillm's three-layer architecture — TensorCore, ModelCore, and WeightLoaderCore — and how Rust's type system enables a modular inference runtime supporting 47 model architectures.
The LLM Inference Stack: From Silicon to API
LLM inference is a full-stack problem. Most companies solve one layer. Cognisoc is building all five — from bare-metal unikernel to on-device mobile inference.
The Cost of Cloud LLM APIs vs Local Inference: A TCO Analysis
A detailed total cost of ownership analysis comparing cloud LLM APIs from OpenAI, Anthropic, and Google against local inference. With real pricing data and three deployment scenarios, we show when local inference costs 5-20x less — and how to make the switch.