# Memory-efficient inference

An ultra-low bit quantization model generated based on Qwen/Qwen3-30B-A3B-Base, supporting a 32k context length and suitable for various hardware environments

phi-2 is a text generation model employing IQ-DynamicGate ultra-low bit quantization (1-2 bits), suitable for natural language processing and code generation tasks.

Ultra-low-bit quantization (1-2 bits) language model using IQ-DynamicGate technology, suitable for memory-constrained environments

Ultra-low-bit quantization (1-2 bit) large language model using IQ-DynamicGate technology, supporting multilingual text generation tasks

OlympicCoder-32B is a code generation model based on Qwen2.5-Coder-32B-Instruct, employing IQ-DynamicGate ultra-low-bit quantization technology for efficient inference in memory-constrained environments.

EXAONE-Deep-32B is a 32B-parameter large language model supporting English and Korean, specifically designed for text generation tasks.

A 7.8B-parameter model featuring ultra-low-bit quantization (1-2 bits) using IQ-DynamicGate technology, supporting English and Korean text generation tasks.

Qwen2.5-14B-Instruct-1M is an instruction-tuned model based on Qwen2.5-14B, supporting text generation tasks and suitable for chat scenarios.