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When to Use Batch Inference for AI Workloads
Move delay-tolerant, independent jobs to batches while preserving identifiers, retries, and quality checks.
Move delay-tolerant, independent jobs to batches while preserving identifiers, retries, and quality checks.
简要回答: Batching is appropriate when tasks do not depend on immediate responses and can be processed independently: offline classification, embeddings, backfills, and evaluation runs. Keep interactive and dependent agent loops on synchronous paths.
实施步骤
- Set a service-level deadline for the workload.
- Create stable item IDs and idempotent output writes.
- Group compatible model and configuration requests.
- Reconcile partial failures rather than rerunning an entire batch blindly.
验证清单
- Verify one output per input ID.
- Measure queue time and completion distribution.
- Compare price and operational overhead with synchronous traffic.
常见错误
- Batching multi-step tasks whose next input depends on the prior output.
- Losing item identity in concatenated prompts.
- Ignoring cancellation and data-retention requirements.
实际决策
先交付能够通过上述检查的最小设计。记录测试中使用的模型、提示词、工具、数据集与运行时版本，让后续变化可以被比较而不是被猜测。依赖供应商的限制、模型名称、价格或预览功能之前，请重新检查链接文档；这些细节变化往往快于本文的工程原则。
