Unverified50% confidenceFactExact time
A complete inference compute solution does not depend only on the chip itself; engineering capabilities at the software layer—scheduling systems, model compilation optimization, batching strategies, and memory management—determine how much effective throughput the same chip can achieve.
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Long-term
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10/27/2026
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VerifiedAn inference runtime is the execution layer between the model weight files and the final output, responsible for mapping the model computation graph to hardware, including memory allocation, operator fusion, quantized computation, and batch scheduling.79% similarUnverifiedIntelligence per Watt is determined by the combined effect of hardware architecture, quantization strategy, inference framework, and the model itself.76% similarUnverifiedTechnologies at the inference engine layer—such as batching, KV cache management, and quantization—determine the degree to which hardware is fully utilized.76% similarUnverified推理的访存模式高度可预测，每层输入输出形状在编译期完全确定，这是硬件固化方案发挥优势的前提条件76% similarUnverifiedDense models have simpler inference logic and better hardware compatibility in local deployment scenarios, whereas MoE models, despite activating fewer parameters during inference, still require all weights to be loaded into memory, placing even higher demands on hardware.74% similar
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