LLM Optimization

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.

A comprehensive guide to transitioning from manual prompt engineering to programmatic optimization using DSPy and high-performance LLM APIs.

Discover how Cappy, a 360M parameter scorer, outperforms 175B parameter models and enhances multi-task LLM performance through efficient regression modeling and weak supervision.

As AI agents handle larger context windows and more complex workflows, the traditional JSON format is proving inefficient. Discover how ULMEN, a new encoding notation, optimizes token usage and semantic validation for modern LLM infrastructures.

Discover how Proxy-Pointer RAG eliminates the high cost and latency of traditional entity and relation extraction in enterprise GraphRAG systems using advanced LLM API strategies.