Local LLM Data Pipeline Performance
Real benchmarks from a three-stage data ingestion pipeline - comparing Llama 3.1 70B, Qwen 2.5 14B, and Llama 3.1 8B on relationship extraction, category assignment, and portfolio curation, with the performance metrics that actually matter for production selection
Performance benchmarks from a local LLM data pipeline - covering three-stage ingestion (relationship analysis, category assignment, portfolio curation), head-to-head model comparisons across parse rate, consistency, precision, token usage, and execution time, optimal model selection for RTX 5070 hardware, the quality-speed-cost tradeoff matrix, and recommendations for Qwen2.5-Coder 32B as the best overall balance.
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Real benchmarks from a three-stage data ingestion pipeline - comparing Llama 3.1 70B, Qwen 2.5 14B, and Llama 3.1 8B on relationship extraction, category assignment, and portfolio curation, with the performance metrics that actually matter for production selection
Your LLM can write poetry but can't reliably add two numbers - hybrid architectures solve this by routing each subtask to the system that actually handles it well
The CLAUDE.md that powers a production hybrid routing system - complexity-based scoring from 1-10, automatic model selection across four tiers (local Qwen through cloud Opus), contextual RAG embeddings that improve retrieval by 5-10%, and the architecture that achieves 95-99% cost savings versus all-cloud
A production-grade routing system that cuts LLM costs by 95-99% - complexity scoring routes simple queries to free local Ollama models while sending complex reasoning to Claude, with RAG semantic search, real-time monitoring, and 10 MCP tools for Claude Desktop integration
The complete guide to running local LLMs on an RTX 5070 with 12GB VRAM - model recommendations by task type, inference engine comparisons, quantization strategies, Claude Code integration patterns, and the multi-model architecture that handles everything from free coding assistance to privacy-first document processing