Qwen3 30B A3B FP8 dynamic Model Overview Model Architecture: Qwen3MoeForCausalLM Input: Text Output: Text Model Optimizations: Activation quantization: FP8 Weight quantization: FP8 Intended Use Cases: Reasoning. Function calling. Subject matter experts via fine tuning. Multilingual instruction following. Translation. Out of scope: Use in any manner that violates applicable laws or regulations (including trade compliance laws). Release Date: 05/05/2025 Version: 1.0 Model Developers: BC Card Model Optimizations This model was obtained by quantizing activations and weights of Qwen3 30B A3B to FP8 data type. This optimization reduces the number of bits used to represent weights and activations from 16 to 8, reducing GPU memory requirements (by approximately 50%) and increasing matrix multiply compute throughput (by approximately 2x). Weight quantization also reduces disk size requirements by approximately 50%. Only weights and activations of the linear operators within transformers blocks are quantized. Weights are quantized with a symmetric static per channel scheme, whereas activations are quantized with a symmetric dynamic per token scheme. The llm compressor library is used for qua…
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