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ATOM 一个低比特量化大模型的缝合怪 - 知乎 Quantization parameters s and z can be calculated either statically using calibration data or dynamically during inference time Thus, quantization approaches can be classified as static or dynamic
Atom: Low-bit Quantization for Efficient and Accurate LLM Serving Atom significantly boosts serving throughput by using low-bit operators and considerably reduces memory consumption via low-bit quantization It attains high accuracy by applying a novel mixed-precision and fine-grained quantization process
Atom: Low-Bit Quantization for Efficient and Accurate LLM Serving - MLSys We evaluate Atom on 4-bit weight-activation quantization setups in the serving context Atom improves end-to-end throughput by up to 7 73×compared to the FP16 and by 2 53× compared to INT8 quantization, while maintaining the same latency target
原子：用于高效准确的低比特量化的LLM服务 - 论文详情 To maximize LLMs' serving throughput, we introduce Atom, a low-bit quantization method that achieves high throughput improvements with negligible accuracy loss
Atom-1 README. md at main · eltociear Atom-1 · GitHub Atom significantly boosts serving throughput by using low-bit operators and considerably reduces memory consumption via low-bit quantization It attains high accuracy by applying a novel mixed-precision and fine-grained quantization process