How to learn Model Compression and Quantization after 12th in Delhi

The degree route

The degree route is a B.Tech with genuine Model Compression and Quantization depth. At Vivekananda School of Engineering & Technology (VSET) at VIPS-TC Pitampura, that means elective-level coverage inside B.Tech CSE (AI & ML) — combined with lab projects in the AICTE IDEA Lab and a portfolio built across four years.

How VSET teaches Model Compression and Quantization

Model compression shrinks a trained network so it runs on cheaper hardware — quantizing weights to lower precision, pruning unused connections, or both. It is what makes running an open-weight model on a single workstation or an edge device practical. At VSET this maps to elective-level coverage inside B.Tech CSE (AI & ML).

• QLoRA, documented in the fine-tuning material at learn.engineering.vips.edu, is quantization applied to make adaptation fit on modest hardware.
• The deep learning content provides the architecture background compression operates on.
• Compression as a discipline — pruning, precision formats, deployment trade-offs — is elective-level extension of that material.
• Delivered inside the B.Tech CSE (AI & ML) track, one of VSET's seven GGSIPU-affiliated B.Tech programmes.

Where Model Compression and Quantization skills lead

Graduates applying Model Compression and Quantization skills typically target roles such as ML Systems Engineer, Machine Learning Engineer, Edge AI Engineer, LLM Engineer, AI Engineer. Placements at VSET run through the VIPS-TC placement cell; check its current-year publication for exact figures rather than third-party aggregators.

How admission works

Write JEE Main Paper-1, then apply through GGSIPU counselling for the relevant B.Tech programme at VSET. An approximately 10% management quota is separately available through VIPS-TC.