AI Knowledge Hub// Latest Tech Posts
Qwen 3.8 (27B) Runs on an 8GB Laptop? Fact-Check [1 comments]
Running Qwen3.8-27B Q4_K_M in an 8GB VRAM environment yields only 0.26 tokens per second, a 333x difference from 86.67 t/s on a 24GB setup. Loading a model onto a GPU is a matter of physics, and current technology cannot get around it.
A complete rundown from the GPU chip makers (NVIDIA/AMD/Intel) to the hidden traits, cooling tech, and 2026 Korean street prices of AIB partners like ASUS/MSI/Gigabyte
The Complete RAG Pipeline Guide — Eight Practical Techniques That Maximize Retrieval Quality [2 comments]
RAG is not just vector search. From chunking quality, hybrid search, rerankers, and contextual retrieval to late chunking — this lays out why retrieval fails in practice and how to fix it.
The Illusion of 'Work Automation' and the Gouging of Premium AI Models — For Ordinary People, Local Is the Answer [1 comments]
One call to OpenAI o1 burns $100. For an ordinary individual, a top-tier reasoning model is a luxury. The smartest combination is to run a local model as the main and use a cost-effective API only when needed.
The complete guide to building a remote server for a personal AI agent that runs 24/7 for 20,000 won a month [1 comments]
A one-stop, hands-on guide to running a cheap VPS with a cost-effective API instead of a heavy local model, guarded 24/7 by Jev MCP guardrails and PM2
Local LLM Fine-Tuning for Beginners — From Full Fine-Tuning to QLoRA: Theory and Hands-On Unsloth Commands [1 comments]
Fine-tuning is not about fixing the whole model — it is about attaching a small adapter. This covers the differences between full fine-tuning, LoRA, and QLoRA, VRAM requirements, a GPU training timetable, seven failure causes and their remedies, data formats, how to install Unsloth, Axolotl, and LLaMA-Factory, and the commands from training through GGUF conversion to running it in Ollama.
A beginner's guide that finishes repository creation, commit, and push using only GitHub Desktop, with no terminal required. It walks in order from the three core concepts — repository, commit, push — to the first upload.
Qwen3.8-9B Distill: A Comprehensive Look at the Overwhelming Champion of Personal Local Environments
Qwen3.8-9B Distill compresses the capability of a 2.4T-parameter giant into 9B. It runs on 8GB of VRAM and delivers performance that surpasses its 9B class, from agentic coding to reasoning. Includes operator field-use benchmarks.
Why AI Cannot Be Controlled: The Nature of the Probability Engine, Jailbreaks, Injection, and the Outer Fence Design [1 comments]
Traditional software is governed by rules, but generative AI works by next-token probability. This piece reviews real failures of jailbreaks, prompt injection, and hallucination, and lays out a triple outer-fence architecture built with NeMo Guardrails and Llama Guard.
Even with the same model loaded, the Mac mini and the RTX 4090 are fast and slow in opposite directions. This covers the architectural difference between unified memory and discrete VRAM, the principle that memory bandwidth determines token speed, measured numbers at the 8B class and the 27B-70B class, and selection criteria by use case.