Inference

A microcontroller board beside a tiny neural network diagram.

Optimizing Small Language Models for Local Edge Inference: Techniques, Constraints, and Production Deployment Patterns

Learn practical techniques to squeeze LLMs onto edge hardware, manage resource limits, and apply proven deployment patterns.

A laptop screen displaying a GPU shader visualizing quantized tensors.

Implementing WebGPU-Accelerated Quantization: A Deep Dive into High-Performance Local LLaMA Inference

A step‑by‑step guide that shows engineers how to combine WebGPU shaders with LLaMA’s GGML backend to achieve low‑latency, high‑throughput inference on a laptop GPU.

Illustration of a Rust crate connecting to several LLM provider APIs.

Implementing Liter-LLM: Architecting Rust-Powered Polyglot Bindings for Multi-Provider Inference and Production-Ready Pipelines

A step‑by‑step guide to designing a Rust inference engine, exposing it to multiple languages, and wiring it into a fault‑tolerant, observable production workflow.

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.

A laptop screen showing a GPU shader visualizing quantized Llama weights.

Implementing WebGPU-Accelerated Quantization for Local Llama Inference: Architecture, Performance, and Production Deployment

A deep‑dive into building a WebGPU‑powered, quantized Llama inference pipeline for edge devices, with real‑world benchmarks and deployment guidelines.