Llm-Optimization

Learn how to build a high-performance speculative decoding engine from scratch, mastering KV-cache management and token-tree verification to optimize LLM inference.

A hands-on guide to building a sliding-window prompt compressor from scratch in pure Python, using a learned importance scorer to evict low-relevance tokens and keep attention focused within a fixed context budget.

How to ship small language models to local edge devices without falling into the usual latency, memory, or quality traps. Covers quantization, llama.cpp, batching, and the operational metrics that actually matter.

Crafting Precision Retrieval Tools: Elevating AI Agents with Smart Database Interfaces In the rapidly evolving landscape of AI agents, the ability to fetch precise, relevant data from databases is no longer a nice-to-have—it’s the cornerstone of reliable, production-ready systems. While large language models (LLMs) excel at reasoning and generation, their effectiveness hinges on context engineering: the art of curating just the right information at the right time. This post dives deep into designing database retrieval tools that empower agents to interact seamlessly with structured data sources like Elasticsearch, addressing common pitfalls and unlocking advanced capabilities. Drawing from real-world patterns in agent development, we’ll explore principles, practical implementations, and connections to broader fields like information retrieval and systems engineering. ...

From Precision to Efficiency: How TurboQuant is Reshaping AI Model Compression The relentless growth of large language models has created a paradox in artificial intelligence: the more capable these systems become, the more computational resources they demand. As context windows expand to accommodate longer conversations and documents, the memory footprint of key-value caches grows proportionally, creating a bottleneck that affects both speed and cost.[1] Google Research has introduced TurboQuant, a breakthrough compression algorithm that challenges conventional wisdom about the trade-off between model precision and efficiency.[2] Rather than accepting the conventional reality that compression means degradation, TurboQuant demonstrates that dramatic reductions in memory usage—up to 6x compression—can be achieved without sacrificing accuracy.[1][3] ...