Quantization

Discover how Unsloth leverages NVIDIA's architecture to achieve 2x-4x faster LLM training via 4-bit quantization, FlashAttention-2, and custom CUDA kernels.

Learn how KVQuant uses 4-bit KV cache quantization to reduce LLM memory requirements by 4x, enabling massive models like LLaMA-70B to run on consumer hardware with minimal accuracy loss.

An in-depth technical analysis of Google's TurboQuant framework, exploring how PolarQuant and QJL residuals enable massive context windows through high-ratio KV cache quantization.

Discover NexusQuant, a breakthrough library that compresses KV cache by up to 33x without retraining, enabling 128K context windows on consumer GPUs.

Discover the engineering breakthroughs behind running massive 400B models like DeepSeek-V3 or Llama 3 on mobile hardware using flash offloading and quantization.

Explore how Matryoshka Representation Learning (MRL) combined with int8 and binary quantization can reduce vector database infrastructure costs by over 80% while maintaining high retrieval accuracy.

A comprehensive guide to deploying state-of-the-art Vision Language Models like PaliGemma and Moondream on Jetson Orin platforms using TensorRT and quantization techniques.

The integration of GGML and llama.cpp into Hugging Face marks a pivotal moment for Local AI, enabling seamless transitions between open-source research and consumer-grade hardware deployment.

An exhaustive analysis of 125+ local LLMs including Qwen 3, DeepSeek R1, and GLM-5, categorized by hardware requirements from 8GB RAM to workstation setups.