5 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.
In self-hosted LLMs, bill and latency come from the serving layer. Manifold throughput via PagedAttention, continuous batching, speculative decoding, and quantization.
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.
A 2026 snapshot of the Turkish open-source LLM ecosystem: Trendyol-LLM, Cosmos-Llama, KanarYa, Kumru AI, the TÜBİTAK BİLGEM domestic model, and the T3 AI Baykar defense model. Detailed decision guide covering MMLU-TR and TUMLU benchmarks, licensing, tokenization gap, VRAM requirements, self-hosting needs, and which model to pick for which use case.
Detailed comparison of the three most powerful 2026 open-weight LLM families — DeepSeek (V3 + R1), Qwen (2.5 + 3), and Meta Llama (4). Architecture (MoE vs dense), benchmarks (MMLU, HumanEval, GSM8K), Turkish performance, license (MIT vs Apache vs Llama Community), cost (self-hosted vs API), hardware (VRAM, GPU), fine-tune friendliness, ecosystem (Hugging Face, vLLM, Ollama), KVKK / data sovereignty advantages. Use cases for Turkish enterprises.