Analysis
Small Models or Large Models? Which Way the Trend Is Heading
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.
31 min
94 articles
LLMs & Model Technology
You cannot design the right architecture without understanding the model layer. This cluster runs from transformer architecture and context windows through fine-tuning, LoRA and quantization, to the open-vs-closed decision and the rise of small models.
Definition
- LLMs & Model Technology
- A large language model (LLM) is a neural network trained on very large text corpora that generates language by probabilistically predicting continuations; in enterprise settings it is the shared substrate for question answering, summarization, classification and code generation.
- Wikidata: Q115349137
What this cluster covers
- Transformers & attention
- Context windows & token economics
- Fine-tuning, LoRA, QLoRA
- Quantization & compression
- Open vs closed models
- Small language models
Narrow by format
Recent
Latest in this cluster
Explainer
What Is Quantization? The Cost of Shrinking a Model
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.
30 min
Field Note
Field Note: The Realities I Encountered in On-Premise Deployments
On-premise deployment experience: a field note where timeline, hardware procurement, network constraints and update burden differ from the plan. With a readiness checklist.
24 min
Explainer
What Is a GPU? Why Is It Needed in Enterprise AI?
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.
30 min
Explainer
What Is a Multimodal Model? Processing Image and Text Together
What is a multimodal model? An AI model that processes image, text, and audio together in a single model. Vision models, document understanding, the OCR difference, and use cases.
30 min
Deep Dive
On-Premise and Sovereign AI Infrastructure: From Setup to Operations
How to build on-premise AI infrastructure? Hardware, model serving, scaling, monitoring, updates and the real operational burden — an enterprise decision guide.
30 min
Guide
On-Premise LLM Setup: A Hardware Sizing 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.
9 min
Guide
How to Read Model Comparisons? An LLM Benchmark Reading Guide
How to read LLM benchmark scores? A practical guide to reading model comparisons with an eye on data contamination, real performance, and the evaluation limit.
6 min
Article
Setting Up an LLM Evaluation Set: Designing the Golden Question Set
A guide to setting up an eval set: designing the golden question set, scoring rubric, human evaluator agreement, acceptance threshold and regression testing for LLM evaluation.
9 min
Article
Where Do Open Source Models Stand in Enterprise Use?
Where does an open source LLM stand in enterprise use? License terms, closed-model comparison, operational load, and in which scenario it makes sense.
6 min
Comparison
SFT, DPO and RFT: Which Fine-Tuning Method to Choose in 2026?
I explain from the field the differences between SFT, DPO and RFT and when to use each: the fine-tuning decision from demonstrations to rewards, with KVKK notes.
18 min
Comparison
August 2026 Model Comparison: Claude Opus 5, GPT-5.6, Gemini 3.6, DeepSeek V4
I compare the leading LLMs as of August 2026 through an enterprise buyer's eyes: capability, cost, latency and KVKK data residency, with practical picks.
18 min
Comparison
Frontier Model Comparison, August 2026: GPT-5.x, Claude, and Gemini 3.x
The August 2026 frontier landscape: neck-and-neck on SWE-bench Verified, separation on SWE-bench Pro. Which model for which job, benchmark literacy, and the Turkish-performance criterion.
15 min
Deep Dive
Fine-tuning or RAG in 2026: LoRA, QLoRA, Distillation, and a Decision Framework
“Fine-tuning or RAG?” is a false dilemma. The 2026 sequence: Prompt → RAG → Fine-tune → Distill. LoRA/QLoRA, small language models, distillation, and the KVKK-sensitive self-host decision.
15 min
Comparison
Fine-Tuning or RAG in 2026? A Decision Framework with RFT, LoRA, and Small Models
RAG for changing knowledge, fine-tuning for stable behavior; often both together. Why LoRA is the default, when RFT, small models, and the KVKK advantage.
15 min
Comparison
Claude Opus 5 vs GPT-5.6 vs Gemini 3.1: The July 2026 Frontier Model Comparison
In July 2026 three major providers released new frontier models. An enterprise-use comparison, multi-model strategy, and a right-choice guide.
15 min
Deep Dive
Fine-Tuning with Synthetic Data: Self-Instruct, Distillation, and the Model Collapse Risk
Synthetic data solved fine-tuning's data bottleneck. A field guide to generation methods, the practical recipe, model collapse, and Turkish data scarcity.
16 min
Deep Dive
LLM Inference Serving Optimization: vLLM, Speculative Decoding, and KV Cache Quantization
In self-hosted LLMs, bill and latency come from the serving layer. Manifold throughput via PagedAttention, continuous batching, speculative decoding, and quantization.
16 min
Comparison
August 2026 Frontier Model Comparison: Claude, GPT-5.6, Gemini 3.1, Grok
There's no single best model. A field guide to the August 2026 landscape, the benchmark trap, and a framework for choosing the right model for your work.
16 min
Comparison
The July 2026 Frontier Model Landscape: Claude Opus 5, GPT-5.6, Gemini 3.x, Grok 4.5 and Kimi K3
July 2026 packed five major models into two weeks. Model selection by use case, a comparison table, and an enterprise framework centered on KVKK and the EU AI Act.
14 min
Explainer
What Is DPO (Direct Preference Optimization)? A Leaner Alternative to RLHF
DPO aligns LLMs to human preferences without a separate reward model or RL. Loss intuition, beta, preference data, RLHF comparison, KVKK, and a Turkish example.
14 min
Comparison
2026 Frontier Model Comparison: GPT-5.6, Claude Opus 4.8, Gemini 3.1 and Beyond
There is no single best model in 2026: how to match GPT-5.6, Claude Opus 4.8, Fable 5, Gemini 3.1 and open models to the job, plus routing and KVKK guidance.
14 min
Deep Dive
Reinforcement Fine-Tuning (RFT), DPO and GRPO: LLM Customization Methods in 2026
Choose SFT, DPO/ORPO/KTO or RFT/GRPO by the data you have. A 2026 fine-tuning guide: method table, RAG comparison, KVKK, and a project checklist.
14 min
Comparison
LLM API Price Comparison July 2026: GPT-5, Claude, Gemini, DeepSeek
The July 2026 LLM API price table, a TCO framework, and cost-cutting levers. Why cheapest isn't always right, plus the KVKK/data-residency dimension.
12 min
Complete index
All 94 articles in this cluster
Grouped by format, newest first within each group.
Comparisons & Decision Guides32
- SFT, DPO and RFT: Which Fine-Tuning Method to Choose in 2026?LLMs
- August 2026 Model Comparison: Claude Opus 5, GPT-5.6, Gemini 3.6, DeepSeek V4LLMs
- Frontier Model Comparison, August 2026: GPT-5.x, Claude, and Gemini 3.xLLMs
- Fine-Tuning or RAG in 2026? A Decision Framework with RFT, LoRA, and Small ModelsLLMs
- Claude Opus 5 vs GPT-5.6 vs Gemini 3.1: The July 2026 Frontier Model ComparisonLLMs
- August 2026 Frontier Model Comparison: Claude, GPT-5.6, Gemini 3.1, GrokLLMs
- The July 2026 Frontier Model Landscape: Claude Opus 5, GPT-5.6, Gemini 3.x, Grok 4.5 and Kimi K3LLMs
- 2026 Frontier Model Comparison: GPT-5.6, Claude Opus 4.8, Gemini 3.1 and BeyondLLMs
- LLM API Price Comparison July 2026: GPT-5, Claude, Gemini, DeepSeekLLMs
- RAG or Fine-tuning? A 2026 Decision Framework: LoRA, QLoRA, GRPO, and Small Language ModelsLLMs
- GPT-5.6 vs Claude Opus 4.8 vs Gemini 3.1 Pro: 2026 Code and Agentic ComparisonLLMs
- GPT-5.6 vs Claude Fable 5 vs Gemini 2.5 Pro vs Grok 4.5: The July 2026 Frontier Model Face-OffLLMs
- Open Source LLM Comparison 2026: Llama, Qwen, Mistral, DeepSeekLLMs
- RAG or Fine-tuning? The 2026 Decision Framework (LoRA, QLoRA, RFT, GRPO)LLMs
- Enterprise LLM Selection in July 2026: Which Model for Which Job?LLMs
- RAG or Fine-Tuning? A Decision Framework and Cost ComparisonLLMs
- July 2026 Frontier Models: GPT-5.x, Claude Sonnet 5, Gemini 3.1 and Grok 4.5LLMs
- The July 2026 Frontier Model Landscape: Which Model for Which JobLLMs
- The June 2026 Model Wave: GPT-5.6, Claude Sonnet 5, Gemini 3.2 and Chinese Models ComparedLLMs
- Claude Code vs Cursor vs Windsurf 2026 — Senior Developer Comparison (1M Context, Composer 2.5, Cascade)LLMs
- ChatGPT vs Claude vs Gemini: A 50-Prompt Real-World Turkish Test and TR-MMLU 2026 ResultsLLMs
- Self-Hosted LLM or API? KVKK + BDDK + Cost Matrix — Enterprise Decision Guide (Breakeven: 500M Tokens/Day)LLMs
- Sora vs Runway vs Kling 2026: Deep Turkish Comparison of AI Video GenerationLLMs
- Midjourney vs DALL-E vs Stable Diffusion vs Flux 2026: AI Image Generation ComparedLLMs
- ChatGPT Alternatives 2026: 15 Tested Real Rivals and When to Use EachLLMs
- DeepSeek vs Qwen vs Llama 2026: Open-Source LLM Comparison — Which Model Should I Choose?LLMs
- Claude Opus 4.7 vs GPT-5: Which is Better? — A 2026 Flagship Model Head-to-Head ComparisonLLMs
- ChatGPT vs Claude vs Gemini 2026: A Detailed Comparison of the Three AI Assistants — Which One is Right for You?LLMs
- Turkish LLM Benchmark 2026: GPT-5, Claude Opus 4.7, Gemini 3, Llama 4 and Local Models — Full ReferenceLLMs
- Vision Transformers or CNNs? A Comparative Analysis of Modern Vision ModelsLLMs
- Open-Source LLM or Closed Model? A Practical Model Selection Guide for EnterprisesLLMs
- RAG or Fine-Tuning? Which Approach Is Better for Which Scenario?LLMs
Concept Explainers28
- What Is Quantization? The Cost of Shrinking a ModelLLMs
- What Is a GPU? Why Is It Needed in Enterprise AI?LLMs
- What Is a Multimodal Model? Processing Image and Text TogetherLLMs
- What Is DPO (Direct Preference Optimization)? A Leaner Alternative to RLHFLLMs
- What Is Stable Diffusion? A Guide to Open-Source Image GenerationLLMs
- What Is Ollama? A Guide to Running LLMs LocallyLLMs
- What Is Text Summarization? Extractive vs Abstractive GuideLLMs
- What Is LoRA? A Guide to Parameter-Efficient Fine-TuningLLMs
- What Is LLM Observability? A Guide to Production Monitoring and TracingLLMs
- What Is LLM Evaluation (Eval)? A Guide to Measurement, Metrics and MethodsLLMs
- What Is Llama? A Guide to Meta's Open-Weight AI ModelLLMs
- What Is the Transformer Architecture? The Foundation of Modern AILLMs
- What Is Gemini? Google's Multimodal AI ModelLLMs
- What Is DeepSeek? A Guide to the Open-Source Reasoning ModelLLMs
- What Is Claude? A Guide to Anthropic's AI AssistantLLMs
- What Is RLHF? A Guide to Reinforcement Learning from Human FeedbackLLMs
- What Is a Context Window?LLMs
- What Is an Open-Source LLM? Local Deployment, Licensing and Llama GuideLLMs
- What Is Reinforcement Learning? A Guide to Reward, Agent, and EnvironmentLLMs
- What Is a GPU (Graphics Processing Unit)?LLMs
- What Is GPT? A Guide to the Generative Pre-trained TransformerLLMs
- What Is Fine-Tuning?LLMs
- What Is Deep Learning? Neural Networks and Layered ArchitectureLLMs
- What Is Generative AI? How It Works and Where It Is UsedLLMs
- What Is a Token? Tokenization and Cost in AILLMs
- What is an LLM? How Large Language Models Work — 2026 ReferenceLLMs
- What is FLUX.1? 2026 Black Forest Labs Image Model Deep Technical Turkish GuideLLMs
- What is Claude AI and How to Use It? A Comprehensive 2026 Guide to Anthropic's AI AssistantLLMs
Implementation Guides11
- On-Premise LLM Setup: A Hardware Sizing GuideLLMs
- How to Read Model Comparisons? An LLM Benchmark Reading GuideLLMs
- LLM Cost Optimization: A Guide to Prompt Caching, Batching and Model RoutingLLMs
- LLM Evaluation (Eval) Guide: Metrics, Benchmarks, CalibrationLLMs
- How to Prevent LLM Hallucination? Verification Layers in ProductionLLMs
- Enterprise AI Budget Planning: An Item-by-Item Cost Guide (2026)LLMs
- DPO, LoRA, and QLoRA: A Practical Fine-Tuning Guide for 2026LLMs
- Stable Diffusion Local Installation 2026: Zero-to-Professional Deep Turkish GuideLLMs
- Multimodal AI — A Comprehensive 2026 Guide: Models that Understand and Generate Image, Audio, Video, and TextLLMs
- LLM Fine-Tuning: A Comprehensive 2026 Guide to LoRA, QLoRA, DPO, and Modern AlignmentLLMs
- How Large Language Models Work: Transformer, Tokenization, Attention, and the Logic of InferenceLLMs
Analysis & Outlook3
Deep Dives16
- On-Premise and Sovereign AI Infrastructure: From Setup to OperationsLLMs
- Fine-tuning or RAG in 2026: LoRA, QLoRA, Distillation, and a Decision FrameworkLLMs
- Fine-Tuning with Synthetic Data: Self-Instruct, Distillation, and the Model Collapse RiskLLMs
- LLM Inference Serving Optimization: vLLM, Speculative Decoding, and KV Cache QuantizationLLMs
- Reinforcement Fine-Tuning (RFT), DPO and GRPO: LLM Customization Methods in 2026LLMs
- Cutting LLM Inference Cost by 80%: A Three-Layer Optimization Playbook (2026)LLMs
- On-Prem LLM Deployment: Hardware Requirements and Cost CalculationLLMs
- The 2026 Adaptation Order: Prompt → RAG → Fine-tune → Distillation with LoRA/QLoRALLMs
- Reinforcement Fine-Tuning (RFT) in 2026: From Imitation to Verifiable RewardsLLMs
- Reinforcement Fine-Tuning (RFT): GRPO, DPO, and Reasoning Models (2026)LLMs
- LLM-as-a-Judge: Automated Evaluation, Biases, RAGAS's 0.55 Reality, and Human Calibration (2026)LLMs
- Small Language Models and Fine-Tuning: The Path to Cost-Effective Customization in 2026 (LoRA, QLoRA, Distillation)LLMs
- The AI Gateway: The Layer That Governs Your LLM Traffic — Routing, Semantic Cache, and Observability (2026)LLMs
- Kimi K2, GLM and Yi 2026: Can Turkish Companies Safely Use Chinese LLMs?LLMs
- Where Has Modern NLP Evolved? The Transition from Classical NLP to Transformer-Based SystemsLLMs
- The Relationship Between Transfer Learning, Fine-Tuning, and Representation LearningLLMs