InTech and AI GuildbyParkerWay·2d agoWhat Is Quantization? How Does It Reduce the Size of Large Language Models?Understanding how AI models become smaller, faster, and more memory-efficient without significantly sacrificing accuracy.
Dibika Dahal·3d agoLLM Quantization Explained: How Do We Make Large Language Models Smaller and More Efficient?Understanding how quantization works, why it matters, and how it helps us run large language models on devices with limited resources.A response icon1A response icon1
Andrii Chulovskyi·3d agoFitting an LLM in Memory: A Visual Guide to Weight Optimization TechniquesEvery technique trades something. Here is what each one gives you and what it costs, shown as one shape per technique.
Mohamed Arbi Nsibi·Oct 31Qdrant 1.20: BM25 Without Sparse Vectors, Turbo16 & Turbo8BM25 straight from your payload, a tokenizer that finally understands camelCase, nested filters 7x faster, and two new TurboQuant…
Simrangrover·Oct 29Quantized a Model to INT8. It Got Smaller, but Slower. Why?An experiment with model size, accuracy, inference speed, and what quantization means for modern AI.A response icon1A response icon1
Tanishq Shukla·Oct 28Do you really need a 7B model for tool calling?I compressed one to 1.5B, measured every step, and a standard benchmark caught two of my mistakes.
Hannah Igboke·Oct 26How to Make a 14GB Model Lose WeightCan Mr Jumbo really lose weight and have have a normal life? Let’s find out.A response icon1A response icon1
Kevin Joy·Oct 26Running Real-Time Segment Anything (MobileSAM) and YOLO in the Browser with ONNX Runtime & WebGPUHow to decouple heavy Vision Transformer backbones from ultra-fast prompt decoders to achieve sub-10ms zero-shot mask generation and…
Jalal Mustafa·Oct 23What Is Quantization in AI? 4-Bit vs 8-Bit in Real Numbers (2026)A “4-bit” model is not 4 bits, it rarely loses as much quality as people fear, and the memory you actually need depends on a number most…
InAccredianbyShlokdhanokar·Oct 19LLM Inference Optimization: Cut Cost and Latency in ProductionHow quantization, pruning, KV cache compression and smarter serving make large language models cheaper to run without retraining them from…
