IQ3_S quantization
IQ3_S is a quantization format within the gguf standard, designed to balance model fidelity with memory efficiency. It is particularly relevant for running large language models (LLMs) on consumer hardware with limited VRAM.
Key Characteristics
- Memory Footprint: Significantly reduces model size compared to FP16/BF16, enabling deployment on GPUs with constrained resources (e.g., 16GB VRAM).
- Precision Trade-off: Uses mixed-precision techniques to maintain acceptable performance while lowering bit-width.
- Compatibility: Supported by inference engines such as llamacpp, ollama, and Swift (UkisAI).
Recent Benchmarks & Performance Data
Swift 1.5 Qwen3.8-27B GSQ-RCO IQ3_S 16GB LLM Performance Benchmark
A comprehensive evaluation of the UkisAI Swift-1.5-Qwen3.8-27B-GSQ-RCO-GGUF model was conducted to assess IQ3_S quantization viability on local hardware.
- Source: Swift 1.5 Qwen3.8-27B GSQ-RCO IQ3_S 16GB LLM Performance Benchmark
- Hardware Setup: Ubuntu server with rtx-2000-ada (16GB VRAM).
- Model: Qwen3.8-27B with GSQ-RCO quantization.
- Objective: Evaluate performance, latency, and quality retention under strict memory constraints.
- Key Findings:
- Demonstrates viable local inference capabilities for 27B-class models on mid-range GPUs.
- IQ3_S provides a critical balance between speed and accuracy for this specific architecture.
- Highlights the effectiveness of GSQ-RCO techniques in preserving model integrity during quantization.