Practical guides, benchmarks, and best practices for implementing LLM infrastructure on-premise. No marketing fluff—just technical depth.
Regulation (GDPR, AI Act, NIS2), use cases for banking/healthcare/defense, hidden cloud costs (egress, lock-in), and TCO comparison cloud vs on-prem over 3 years.
Production benchmarks comparing latency, throughput, and filtering capabilities. Decision matrix for choosing the right vector database based on scale, budget, and feature requirements.
Performance benchmarks on H100 GPUs, throughput/latency analysis, concurrency scaling, and decision matrix for choosing the right serving engine for your workload.
GPU memory requirements for Llama 3 models, quality trade-offs between full fine-tuning and LoRA/QLoRA, cost analysis, and production deployment code examples.
Connect any LLM to any server, build custom agents that browse the web, search Airbnb, control Blender. Complete guide with code examples and production patterns.
Revolutionary approach - encode text chunks as QR codes in video frames, achieve 50-100× compression vs vector databases, retrieve in <100ms with constant 500MB RAM.
Hybrid search (keyword + vector), re-ranking and query expansion, chunk strategies (fixed vs semantic), and guardrails anti-hallucination.
Quality/speed/memory trade-offs, benchmark Llama 3 70B in 4/8/16-bit, practical tools (AutoGPTQ, llama.cpp).
Metrics (latency TTFT/TBT, throughput, quality), distributed tracing (OpenTelemetry), log analysis, alert rules.