Three Ways to Speed Up LLM Inference Compared: Quantization, Pruning, and KV Cache Optimization
A deep dive into three core techniques—Quantization, Pruning, and KV Cache optimization—that solve the inference speed and cost problems of deploying LLMs in production. We cover the principles, trade-offs, and practical optimization strate
How to Avoid the LLM Operating Cost Bomb: Comparing 3 Optimal Architecture Patterns from Quantization to the Edge
Skyrocketing inference costs are becoming a growth bottleneck for LLM services. This guide comparatively analyzes three proven architecture patterns—Quantization, Model Caching, and Edge Deployment—and presents an optimal cost-reduction roa
The Key to Running LLMs on the Edge: An In-Depth Guide to Model Compression (Quantization & Pruning)
You can run LLMs on smartphones and IoT devices without depending on the cloud. This guide dives deep into the principles of quantization and pruning, and presents the latest toolchains and workflows you can apply in production.
[Practical Guide] Leave the Cloud Behind: Deploying Edge AI Models with TFLite and ONNX
Feeling the limits of cloud-based AI? This guide lays out a practical roadmap for actually deploying trained models to edge devices like Raspberry Pi and Jetson, covering the A-to-Z process of compressing and optimizing models with TFLite a
Compressing Models for Edge AI: Quantization, Pruning, Distillation, and a Jetson Deployment Flow
Finding it hard to deploy AI models on low-power edge devices outside the cloud? This guide walks you systematically from the core principles of model compression (quantization and pruning) through a practical deployment pipeline using TFLi
The Practical Barriers to LLM Services: Cost and Speed, and How Model Compression Solves Them
The massive operating costs and slow inference speeds that come with the explosive growth of LLMs are the biggest obstacles to production adoption. This post defines why model compression is necessary and clearly outlines three core optimiz
Improving AI System Performance: Diagnosing Bottlenecks and Optimizing Layer by Layer
Deployed an AI model to production only to find it slow or prohibitively expensive? This guide systematically diagnoses system bottlenecks and walks through practical, step-by-step optimization strategies across code, models, and infrastruc
[Part 2] Make Your Edge AI Models This Lightweight: A Practical Optimization Guide from Quantization to Deployment
Hitting performance bottlenecks when moving AI models from the cloud to edge devices? This guide covers model compression (quantization and pruning) with TFLite, ONNX, and more—from theory through hands-on optimization and deployment on Jet
Edge AI Deployment, Part 1: Model Compression and On-Device Inference
Cloud AI is powerful, but latency, privacy, and cost make it a poor fit for real-time devices. This first installment covers when to choose edge AI, how to pick hardware and runtimes, and what to verify before you ship on-device inference.