Local LLMs Training in Amritsar
Local LLMs & Open-Weight AI Systems — our Local LLMs course in Amritsar, 14 Weeks. Deploy and fine-tune large language models on your own hardware. Master transformer architecture, RAG pipelines, agentic workflows, and production-grade AI system engineering without cloud dependency.
Course Overview
Cloud LLMs are expensive, rate-limited, and your data leaves your machine. This course is about running LLMs locally. On your laptop, on your server, on your hardware. We start with transformer architecture: self-attention, multi-head attention, positional encoding. Not theory for theory's sake, but enough to understand why quantization works and how to choose the right model.
Deployment is the first practical skill. Ollama for quick setup, vLLM for production throughput, TensorRT-LLM for NVIDIA optimization. You will learn GGUF quantization, VRAM management, CPU offloading, and multi-GPU partitioning. By week 3, you will have a local LLM running with a chat interface and benchmarked inference speeds.
RAG is where local LLMs become useful. You will build retrieval-augmented generation pipelines with embedding models, vector databases (ChromaDB, Qdrant, FAISS), and advanced techniques like re-ranking and multi-hop retrieval. Then fine-tuning with LoRA and QLoRA, agentic workflows with LangChain, and multi-modal models with LLaVA. You will fine-tune a model on your own data and build agents that use tools.
The final module is production. FastAPI servers with streaming, rate limiting, and authentication. Monitoring token usage and latency. Edge deployment with ONNX and Core ML. Cost optimization through model routing and caching. The capstone is a domain-specific RAG system with a fine-tuned local LLM, vector store, and monitoring dashboard. Fourteen weeks, and you can build AI systems without depending on any cloud API.
Curriculum Blueprint
Weekly module breakdown covering transformer architecture, RAG systems, fine-tuning, and production AI deployment.
- Transformer architecture: self-attention, multi-head attention, positional encoding, and layer normalization
- Tokenization: BPE, SentencePiece, tiktoken, and how text becomes numbers that models understand
- The LLM landscape: Llama, Mistral, Phi, Gemma, and choosing the right open-weight model for your use case
- Model quantization: INT8, INT4, GGUF formats, and reducing memory footprint for consumer hardware
- Ollama setup: pulling models, running inference, API server, and model management on local hardware
- vLLM and inference optimization: PagedAttention, continuous batching, and throughput vs latency tradeoffs
- Hardware considerations: GPU VRAM management, CPU offloading, multi-GPU partitioning, and Apple Silicon MPS
- TensorRT-LLM acceleration: building engines, kernel auto-tuning, and maximizing inference throughput on NVIDIA
- First project: deploy a local LLM with Ollama, build a chat interface, and benchmark inference speed
Tools Covered
Prerequisites
- Fundamental understanding of Python programming and API concepts
- Basic exposure to machine learning concepts (vectors, embeddings, training)
- Hardware with discrete GPU (8GB+ VRAM) or access to cloud GPU instances
- Absolute commitment to master core underlying software engineering over syntax rules
Free Code Examples
Practise alongside the course with our open, beginner-friendly repositories on GitHub.
View Free Code Examples on GitHubLab Access
- Cloud sandbox environment
- Real production server access
- Local AI model playground
- 24/7 Git repository access
Quick Inquiry
Interested? Drop your details and we will reach out.
Common Questions
The things people ask us on the phone before they enrol — answered the same way we would answer them there.
There is no separate price for it. WebPrims charges one fee — ₹4,500 / month — and it covers any course in the catalogue, so the Local LLMs course works out at about 4 months — around ₹18,000 in total. No admission fee, no registration fee, and the certificate is included.
Monday to Saturday, with batches starting at 11:00 AM, 1:00 PM, 3:00 PM and 5:00 PM. Each slot runs up to two hours — sometimes a session finishes early, never late. That works out at roughly 168 hours of class time across the 14 Weeks. Pick whichever time fits around college or work, and move to another later if your timetable changes; it is the same Local LLMs syllabus in each. We are closed on Sunday.
14 Weeks of taught material, which is about 4 months of Local LLMs classes. That is the pace of the syllabus; how long you actually take depends on how often you turn up, and nobody is pushed to keep up with the batch.
Comfortable Python, and a willingness to meet some maths. You do not need a machine learning background.
Models running on hardware you control — quantised, served through Ollama and vLLM, and understood well enough that you can explain why one answers differently from another.
Not to learn. We have lab machines, and much of the course is about making models run on ordinary hardware — quantisation exists precisely because not everyone has an H100. What you learn transfers to whatever you eventually buy.
Yes. Book a free demo class and sit in a real Local LLMs session — one that was running anyway, not a presentation arranged for you. Write some code on one of our machines, ask the students already in it what it is like, and decide afterwards. Nothing to pay and no obligation.
Something here not answered? Ask us directly or book a free demo class and put it to the Local LLMs mentor in person.
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