Unverified50% confidenceFactExact time
On-device inference typically can only run smaller models (on the order of hundreds of millions to billions of parameters), and first-token latency and throughput are strongly device-dependent.
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10/28/2026
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UnverifiedCalling models with 70 billion parameters or more often requires multi-GPU clusters, and single-inference latency and cost are unacceptable for high-frequency decision scenarios.77% similarUnverified推理部署对单次计算规模要求低，但需要应对海量并发请求，更强调吞吐量和延迟的平衡75% similarVerifiedIn code completion scenarios, using lightweight specialized models with smaller parameter counts but faster inference speed is more reasonable than using large models, because this task has extremely high latency requirements and does not require general reasoning capabilities.74% similarUnverified轻量小模型的推理延迟可低至5-20ms，单次推理成本不足大模型的1%，且可本地部署74% similarUnverifiedThe single forward inference latency of a 1-billion-parameter model on modern GPUs is typically in the single-digit millisecond range, whereas hundred-billion-parameter large models may reach tens of milliseconds or higher even after quantization optimization.74% similar
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