Unverified50% confidenceFactExact time
Continuous batching dynamically schedules requests at token granularity, allowing throughput to increase severalfold under real traffic.
1
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50%
Confidence
Long-term
Relevance
10/20/2026
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Related Claims
UnverifiedMillion-level tok/s is generally not the speed of a single model in a single generation, but rather an aggregate throughput performance resulting from the combined effect of batching, multi-request concurrency, and underlying kernel optimizations.69% similarVerifiedConsistency models attempt to compress the multi-step denoising process of diffusion models into 1–4 steps, theoretically capable of improving inference speed by 10–50 times.68% similarVerifiedThe denoising process of diffusion models typically requires tens to hundreds of iterations (called sampling steps), each of which requires a complete forward pass through the neural network.64% similarUnverifiedSpeculative decoding improves generation throughput while guaranteeing that the output is completely identical to that of the original model.64% similarUnverifiedCommon technical approaches for achieving a 200x inference speed improvement include model distillation, dedicated inference chips/NPU acceleration, speculative decoding, and quantization compression.64% similar
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