Depth-wise batching in recurrent models: how to assess it
A post published on September 2, 2026 describes how overlapping requests at different recurrent depths may improve utilization in models with adaptive compute.
Source: Depth-wise batching para modelos recorrentes (arxiv.org). Text prepared with AI from this source.
What happened and what to do
Published on September 2, 2026, the post presents “depth-wise batching”: overlapping requests that are at different recurrent depths. According to the text, this approach may improve utilization in models with adaptive compute. The post also includes links about Recursive Transformers and MoR.
A company operating recurrent models can assess the approach with a controlled test: record depth, latency, and utilization per request, compare current scheduling with depth-based batching, and monitor effects on quality and cost. Implementation should reflect actual workload patterns and the service’s latency limits.
How the consultancy can help
Wendelmaques can diagnose operations and workload patterns, define a depth-wise batching test, and scope its implementation, instrumentation, and monitoring on the company’s infrastructure.
Next step
Send a short description of the model, workload, and latency limits to receive a scoped proposal.
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