Qwen Studio

In this video, I'll explain how to use Qwen Studio step-by-step in an easy and beginner-friendly way. In this guide, you will learn: ✔ What Qwen Studio is and its main features ✔ How to create an account and log in to Qwen Studio ✔ Familiarize yourself with the platform's interface and the most important available tools ✔ How to use artificial intelligence to write text and answer questions ✔ Easily create content, summarize texts, and translate them ✔ Use different templates within Qwen Studio ✔ Save conversations and manage projects and files ✔ Tips for getting the most out of Qwen Studio's AI tools After watching this video, you'll be able to use Qwen Studio and easily leverage its AI tools for writing, learning, and productivity. 🔔 Don't forget to like the video 👍 and subscribe to the channel for more AI tutorials, applications, and modern technologies.

Run Alibaba's Qwen 3.8 27B model on desktop CPU alone: file sizes, memory requirements, and the math proving you get ~5 tokens/sec without a graphics card. Alibaba's Qwen 3.8 27B is a 27.8-billion-parameter multimodal model that, after quantization to GGUF format, runs on ordinary desktop system RAM without a graphics card. The four-bit compressed file is 16.5 GB, smaller than many video games, and Unsloth publishes that it needs 16, 19 GB total memory to run, where system RAM counts equally with VRAM. Most people download via Ollama, which defaults to 18 GB, already oversized for a 16 GB machine; the real choice is selecting the right quantization rung from the ladder (6 GB at 1-bit up to 55 GB uncompressed). Speed comes down to one formula: file size divided into memory bandwidth. An Intel desktop CPU reads at 89.6 GB/sec (the published spec); divide that by the 16.5 GB model file and the theoretical ceiling is 5.4 tokens/sec. Real-world testing on a 32 GB i5 laptop showed 1.88 tokens/sec with the four-bit file, while a Raspberry Pi held steady at ~10 GB/sec throughput across all quantizations, proving the bottleneck is pure bandwidth, not compute. Smaller files paradoxically don't always decode faster below four bits because unpacking overhead overtakes read savings. Speculative decoding via a 1.37 GB draft head can boost throughput 41%, but costs memory and is still experimental. For builders and AI-curious developers deciding whether CPU-only inference is viable: yes, a normal desktop runs this 27B model completely offline at roughly human-typing speed. Chapters: 0:00 The 16.5GB file that fits anywhere 1:05 Why desktop RAM beats you think 1:55 Ollama's hidden 18GB trap 2:50 Why file size is all that matters 3:56 The bandwidth ceiling explained 4:51 Intel's surprising memory limit 5:53 The math says 5.4 tokens 6:57 Real laptop speed: 1.88 tokens 8:15 Raspberry Pi's hidden constant 9:24 Why smaller files run slower 10:27 Disk swapping kills everything 11:21 The trick Qwen hides in layers 12:24 One tiny file changes everything 13:38 Five tokens: the honest answer Tools & resources mentioned: - Qwen 3.8-27B (Hugging Face): https://huggingface.co/Qwen/Qwen3.8-27B - Unsloth Qwen 3.8-27B GGUF: https://huggingface.co/unsloth/Qwen3.8-27B-GGUF - Ollama: https://ollama.com - llama.cpp: https://github.com/ggml-org/llama.cpp - Unsloth Qwen 3.8 Setup Guide: https://unsloth.ai/docs/models/qwen3.8 About The Stack The Stack helps you build with AI. Each video takes one tool, model, or workflow and shows how it works in a few focused minutes, with the real benchmarks and real costs. We go deep on Claude Code and Cursor for AI coding, AI agents and MCP servers, the open-source AI tools and GitHub repos most people miss, RAG and vector search, fine-tuning, and running local LLMs on your own machine with Ollama and LM Studio. We compare models like ChatGPT and Claude, test AI automation with Zapier, Make, and n8n, and flag the tools that actually ship. Subscribe for new breakdowns: https://www.youtube.com/@the-stack-ai?sub_confirmation=1 #qwen #localAI #llmperformance #CPUinference #quantization