7 posts
What is model serving? A guide to the serving layer: from managed APIs to self-hosted inference servers like vLLM, TGI and Ollama; batching, streaming, concurrency, throughput and latency trade-offs.
Small language model or large model? An enterprise decision framework in light of task-based selection, the cost-performance balance, and the hybrid architecture trend.
What is quantization? Quantization is a model-compression technique that represents a model with fewer bits to save memory and gain speed, at the price of a measurable quality loss. INT8, INT4, PTQ, QAT and more.
What is a GPU? A GPU is a processor that performs parallel computation with thousands of cores and forms the heart of AI hardware. VRAM, CPU vs GPU, training vs inference in this guide.
How to size hardware for an on-premise LLM: a practical guide to VRAM math, quantization, concurrent users, GPU count and server sizing for enterprise deployments.
On-prem LLM deployment guide: hardware requirements, GPU and VRAM, quantization savings, the serving stack, and on-prem vs API total cost of ownership calculation.
An enterprise decision matrix between self-hosted LLM and API: ~500M tokens/day break-even, H100/H200/B200 GPU cost, quantization impact, KVKK + BDDK + ITAR/EAR constraints, AI sovereignty strategy, and three anonymized Turkish sector cases (banking, healthcare, SMB) on hybrid architecture. 2026 reference guide for Turkish enterprises.