Run Qwen 3.8 Flash Next (Qwen 4) 180B on CPU Only, No GPU Needed
We ran the 180B Qwen3.8-Flash-Next on one desktop with no GPU at all, then added a single RTX 5090, and measured speed and answer quality against Qwen 3.8 27B.
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This is the only course which finally teaches how to fine-tune your own LLM.
Nicely explain different transformers and models with example notebooks.
the course has been designed in a well structured way from bert model to advanced lora qlora techniques and easy to understand the concept
This Udemy course delivered a structured learning path with clear, up-to-date resources and practical code examples.
The instructor explains in a very organised way which gives us an idea what are the fundamentals that are required.
This was a great course. I picked up a lot of new things, and the instruction was excellent. I'm really glad I enrolled.
As someone who struggled with Blender for months, this course finally made things click for me. The teaching style is simple, clear, and very engaging.
Really good course
This course is well structured, every session is easy to digest and very well explained. Kudos to the lecturer and team.
Great content, comprehensive code documentation, flow and topic selection. Laxmi provides a good learning experience.
I really like this course. It explains AI step by step in a simple and clear way. This should be the first course anyone takes to truly understand AI.
The course is very excellent especially to start learning llms.
This was a comprehensive and enjoyable course with some great projects at the end that helped reinforce the main concepts.
This is the one of the best course in this platform to automate the blender with Claude!!!!!
I have learned a lot in this course, thank you
ótimo curso para iniciar em contruções de agentes!
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A structured, progressive roadmap for developers seeking to master AI agents, covering Python foundations, LangChain, LangGraph, and private multi-agent RAG.
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We ran the 180B Qwen3.8-Flash-Next on one desktop with no GPU at all, then added a single RTX 5090, and measured speed and answer quality against Qwen 3.8 27B.
We read the config files and weight maps of Qwen3.8-Flash-Next and Qwen 3.8 27B to find the four changes that let a 180B model run only 6B of its weights per token.
IBM dropped the Mamba hybrid in Granite 4.2. We measured what that costs against Gemma 4, Qwen 3.8 and Ornith 1.5 on one RTX 5090.

Meet your instructor
I'm Laxmi Kant Tiwari, IIT Kharagpur alumnus, founder with a successful startup exit, and an engineer with 10+ years across industry and academia. Everything here is taught the way real production systems are built.
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I Ran Qwen 3.8 Flash Next (Qwen 4) on CPU Only
Qwen 3.8 27B Uncensored vs Stock: What Abliteration Actually Does
Qwen 3.8 Flash Next (Qwen 4) vs 27B: Qwen 4 Architecture Teardown
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Master Langchain v1, Local LLM Projects, Ollama, DeepSeek, LLAMA 3.2, Complete Integration Guide.
Agentic RAG and Chatbot, AI Agent, DeepSeek, LLAMA 3.2 Agent, FAISS Vector Database.
Build MCP servers & clients with Python, Streamlit, ChromaDB, LangChain, LangGraph agents, and Ollama integrations.
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