The key takeaway: Asynchronous batch processing enables parallel CPU and GPU work and can increase LLM inference speed by 24 percent without requiring new kernels or models.
Classical continuous batch processing in LLM inference wastes nearly a quarter of GPU time through idle cycles between CPU and GPU. Through asynchronous coordination, both processors can work in parallel and inference speed can be increased by 24 percent.
In synchronous batching systems, the CPU and GPU follow a fixed rhythm: while the GPU performs its forward pass computation and samples new tokens, the CPU sits idle. Once the GPU is finished, the CPU takes over batch preparation (token sampling, request status updates, rescheduling), while the GPU waits inactive for the next batch. This alternation results in neither processor performing productive work at any given time.
A profiling measurement with an 8-billion-parameter model generating 8,000 tokens with batch size 32 demonstrates the problem concretely: the total duration was 300.6 seconds, with 24 percent spent with an idle GPU. These idle periods are not unavoidable but are a consequence of synchronous coordination between the components.
The solution approach lies in asynchronous batch processing: preparation for batch N+1 runs in parallel while batch N is still computing on the GPU. This allows the GPU to remain continuously busy. However, this requires that the CPU can control GPU launch without waiting for results, and that data dependencies are correctly resolved.
The practical benefit is substantial: theoretically, generation time could be reduced from 300 to 228 seconds—a 24 percent speedup without new kernels or model changes, but solely through better hardware coordination. For inference endpoints like the H200 (approximately $5 per hour), this translates into measurable cost savings for longer workloads.
Source: ainews-dev.lumi-systems.io · Published May 17, 2026
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