Unverified50% confidenceFactExact time
Most large-model APIs have rate limits and concurrency caps, so queuing or errors may occur during group chat peaks.
1
Sources
50%
Confidence
Long-term
Relevance
10/23/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedSetting the concurrency too high easily triggers downstream API rate limiting, causing retries and backoff, which instead lengthens the total time; setting it too low fails to compress end-to-end latency.77% similarUnverifiedContext overflow means the number of input tokens exceeds the model's maximum positional encoding range or the server-side configuration limit, and should be intercepted before the request enters the model.75% similarUnverifiedModel performance bottlenecks may stem from multiple sources, including host-side overhead, communication blocking, or compute unit idleness.75% similarUnverifiedRate limiting at the API layer increases the attacker's data collection cost by setting a maximum number of queries per unit time, greatly reducing the economic viability of distillation attacks.74% similarUnverifiedParallel execution by multiple Agents leads to significant cost increases and API rate-limiting issues, because each inference call consumes large-model API tokens.73% similar
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