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LLM Internals & Inference
What happens inside the model: attention and QKV, inference-time reasoning, KV caches and vLLM, hallucination detection, tree-of-thought, and the economics of tokens.
13 articles
- Geek Out Time: Gradient Boosted Trees(GBT) Using a Hallucination Risk PlaygroundFeb 2026
- Geek Out Time: Tree-of-Thought for LLM ReasoningFeb 2026
- Geek Out Time: Can a Model Detect When It’s About to Hallucinate?Dec 2025
- Geek Out Time: The Economics of LLMs -How Token Pricing Quietly Shapes the ArchitectureDec 2025
- Geek Out Time: Where LLMs “Keep” Their Facts? (VSCode with Colab)Nov 2025
- Geek Out Time: Reducing Hallucination with DoLa in Google Colab (Microsoft Phi-2)Oct 2025
- Geek Out Time: Demystifying vLLM’s KV Cache, Latency & Context Isolation for Faster LLMsJun 2025
- Geek Out Time: LLM Inference Exposed-How It Works And How to Make It Fast on Google ColabJun 2025
- Geek Out Time: LLM Hallucination Experiments — Reflection, Verification, and Ground Truth in…Jun 2025
- Geek Out Time: Simulating LLM Short and Long Memory with FAISS, LangChain, and Google ColabJun 2025
- Geek Out Time: Understanding Inference-Time Reasoning in LLMs with Google Colab and NVIDIA NIMMay 2025
- Geek Out Time: Simulating High-Bandwidth and Cache-Like Memory on Google Colab’s T4 GPUNov 2024
- QKV and Multi-head Attention in LLMNov 2023