Ornith 1.5 9B and 35B-A3B Benchmark on RTX 5090
We benchmarked Ornith 1.5 9B and 35B-A3B against Gemma 4-31B, Muse Glimmer 30B and Qwen 3.8 27B on one RTX 5090, and found that every advertised context length is a fiction.
Code-first AI for production
Learn to build and deploy machine learning, RAG, and AI agent systems through practical, step-by-step projects.
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IIT KGPIIT Kharagpur
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Follow a sequenced roadmap instead of piecing together disconnected tutorials.
A structured, progressive roadmap for developers seeking to master AI agents, covering Python foundations, LangChain, LangGraph, and private multi-agent RAG.
A comprehensive curriculum taking you from zero programming knowledge to professional data manipulation, mathematical visualization, and core ML model building.
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We benchmarked Ornith 1.5 9B and 35B-A3B against Gemma 4-31B, Muse Glimmer 30B and Qwen 3.8 27B on one RTX 5090, and found that every advertised context length is a fiction.
We diffed all 866 GGUF tensors of an uncensored Qwen 3.8 27B against the original and found 131 matrices edited along one shared direction, with no measurable cost to quality.
The famous strawberry problem explained: why models that write essays fail at counting letters, plus the arithmetic, spelling, and rhyming quirks tokenization causes.

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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Full video walkthroughs, free: new tutorials every week.
Qwen 3.8 27B vs Muse Glimmer vs Gemma 4 - DFlash Made Glimmer 3x Faster
Qwen 3.8 27B Speed Settings Explained: MTP, KV Cache and Flash Attention
Qwen 3.8 vs Muse Glimmer vs Gemma 4 Coding Test
In-Depth Courses
Go deeper with complete projects, private repositories, and certificates.
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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What Students Say About Us
“The instructor explained clearly and its examples were easy to implement. I recommend it for introducing yourself into the latest advancements in transformers.”
“This is a nice hands-on video. The contents enhance the knowledge of fine tuning the model. I have also found that the 'theory' parts are very worth to learn.”
“The lecture is well-organized and delivered clearly, and easy to learn.”