Unverified50% confidenceFactExact time
In large model training, all compute units within a batch must complete forward computation and backpropagation and synchronously update parameters before the next batch can begin, so in long agent tasks fast units must wait for slow units, leading to reduced GPU utilization.
1
Sources
50%
Confidence
Long-term
Relevance
10/13/2026
First Seen
Sources
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Related Claims
UnverifiedVRAM offloading refers to transferring weights or activations that are temporarily not involved in computation from GPU VRAM to system memory or disk during inference, at the cost of latency incurred when moving them back to the GPU, which results in reduced inference speed.79% similarUnverified连续批处理（Continuous Batching）解决GPU利用率问题，防止序列长度不齐导致的算力浪费77% similarVerifiedWhen model parameter size exceeds GPU VRAM capacity and needs to be offloaded to system memory, inference speed drops sharply from tens of tokens per second to single digits.77% similarUnverifiedNon-deterministic GPU operations are designed to prioritize performance over determinism; forcing a fixed execution order introduces numerous synchronization barriers that significantly reduce parallel efficiency.76% similarUnverifiedGPU-CPU synchronization has unpredictable blocking times under high concurrency, potentially introducing hundreds of microseconds or even millisecond-level additional wait times on certain requests.75% similar
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