LLM Optimization

A deep dive into moving beyond naive RAG by implementing structural chunking, hybrid retrieval, and Bayesian hyperparameter optimization to achieve 95% recall.

An in-depth analysis of 355 million tokens across thousands of AI coding sessions reveals that 97.6% of context window usage is pure noise. Learn how to optimize your LLM costs and efficiency.

A deep dive into profiling PyTorch attention mechanisms and implementing production-grade agentic skills for self-hosted LLM environments.

An in-depth technical exploration of profiling the Attention mechanism in PyTorch, focusing on performance bottlenecks, CUDA kernels, and optimization strategies for modern LLMs like DeepSeek-V3 and Claude 3.5 Sonnet.

While the Model Context Protocol (MCP) ecosystem has exploded to over 13,000 servers, the hidden token overhead and security risks present significant challenges for enterprise AI implementation.

Learn how to use QLoRA to shrink a 7B parameter model from 15GB to 5.4GB, enabling fine-tuning on consumer-grade 16GB GPUs like the NVIDIA T4.

Discover nine high-leverage strategies to reduce LLM infrastructure costs by 50-90% without sacrificing quality, including semantic caching, model cascading, and prompt compression.

Learn how to optimize LLM costs through strategic output limiting, system prompt refinement, and intelligent model routing using n1n.ai.

A deep dive into the evolution of Parameter-Efficient Fine-Tuning (PEFT), comparing LoRA with emerging champions like DoRA, GaLore, and VeRA to determine the best strategy for modern LLMs.

Google has released Gemma 4 models with Quantization-Aware Training (QAT), enabling 4-bit and 2-bit weights that maintain high accuracy for edge device deployment.

A technical deep dive into profiling PyTorch models, transitioning from standard nn.Linear layers to high-performance fused MLP kernels for LLM efficiency.

A deep dive into Gemma 4's Quantization-Aware Training (QAT), testing the 12B model on a GTX 1080 Ti to achieve 16k context on 8GB VRAM.