Model Serving · Software component
Concurrent Inference Request Dispatcher
Software componentModel ServingModelsarc:ConcurrentInferenceRequestDispatcher
A client-side dispatcher that submits many independent inference requests concurrently or pipelined over pooled, multiplexed connections so the inference server can batch them into shared forward passes.
Responsibility. Overlaps independent inference requests to maximize end-to-end request throughput.
Also known as: Async batch request client, High-throughput request pattern, Request pipeliner
Relationships
invokes dependency
Design guidance
- SHOULD submit independent batch-workload requests concurrently so the server can batch them in one forward pass.
- SHOULD bound concurrency to the GPU/model-specific optimum, because exceeding it exhausts GPU memory and crashes inference with OOM errors.
- SHOULD NOT be used for interactive workloads, since batching adds queueing latency and reduces fairness.
Quantitative guidance
As stated by the sources; verify before use.
- 32 sequential requests at 100ms take 3,200ms; concurrent batched processing takes ~300-400ms, an 8-10x throughput improvement (Ch7.2).
- Optimal concurrency: 7B on A100 40GB 16-32; 7B on RTX 4090 24GB 8-16; 70B on A100 80GB 4-8 requests (Ch7.2).
- Pipelining 10 requests raises throughput from 9.5 to 95 requests/s with 5ms serialization and 100ms server time (Ch7.2).
- Throughput optimization adds 50-200ms queueing time per request (Ch7.2).
Classification
- Patterns
- Concurrent async fan-out (gather)Request pipeliningHTTP/2 multiplexing
- Technologies
- Python asyncioAsyncOpenAI clientNVIDIA NIM
- Quality attributes
- Performance efficiency (ISO/IEC 25010)Cost efficiency
- Risks mitigated
- GPU underutilization from sequential request submission
Sources
- Ch7.2: T. Nguyen, "Performance Optimization and Production Monitoring," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 7.2. ISBN: 9798244538229.