Qwen-3-Coder-Next is an 80 billion parameter model that requires 159.4GB of RAM to run. Techniques exist to reduce the size of large language models by 4x and increase their speed by 2x.

Unsloth Dynamic v2.0 quantization significantly enhances performance over previous methods, achieving new benchmarks for Aider Polglot, 5-shot MMLU, and KL Divergence. The 2.0 GGUFs allow for running and fine-tuning quantized LLMs with minimal accuracy loss on various inference engines, including llama.cpp and LM Studio.