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
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.
Advanced GPT-4 Prompt Engineering: Constraints, Few-Shot, CoT, and RAG Together
Learn advanced prompt engineering techniques that systematically unlock an LLM’s reasoning—going far beyond simple questions. From eliciting thought processes with CoT to generating answers grounded in internal company data with RAG, this g
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
Beyond PoC to Production: A Technical Roadmap for Reliably Serving LLMs in Cloud Environments
Reliably operating LLMs under real traffic—beyond simple API calls—requires sophisticated architecture. This guide covers the latest serving strategies and MLOps pipeline practices for minimizing inference latency and cost on AWS, GCP, and
From LLM Performance Measurement to Cost Optimization: A Practical Evaluation Framework Every Engineer Should Know
This guide solves the dilemma of focusing only on Accuracy when adopting LLMs—then hitting cost explosions and slow responses. It presents an integrated framework for measuring production-critical metrics such as Latency, Cost, and RAG eval
[Advanced Guide] From Diagnosing RAG Performance Degradation to HyDE and Re-ranking: Production-Level Optimization Strategies
Diagnose the root causes of why RAG systems underperform in production, and get a complete rundown of advanced techniques beyond naive retrieval. We cover immediately applicable architecture improvements—including HyDE, re-ranking, and sema