Dynamic Batch Size Tuning

Initialization: The engine pre-captures and compiles multiple static CUDA graphs for bucketed shapes during startup.

Step Execution: Scheduler calculates the optimal active sequences.

Hybrid Path: The runtime matches the state to a graph bucket.

Replay & Overlap: The static CUDA Graph is replayed on the GPU. Simultaneously, Just-In-Time (JIT) dynamic operations (like complex cache updates or stochastic sampling) are computed asynchronously on the CPU to prevent stalling.

Asynchronous Capture: If a new shape is encountered, the engine captures a new static subgraph without blocking the active execution.

Dispatch Latency: Reduces inter-kernel switch latency from hundreds of microseconds to near zero.

CPU Offload: Frees host CPU cycles, enabling dense multi-GPU orchestration on single nodes.

Latency Variance: Graph replay guarantees deterministic execution times, flattening extreme tail latency spikes.

TensorRT-LLM and vLLM aggressively utilize piecewise CUDA graph strategies. Without CUDA graphs, inference of small models (where the GEMV computation is exceptionally short) is entirely CPU-bound.

Piecewise Graphing: Graphing only the static transformer layers while executing the highly dynamic sampling heads via eager execution.

Background Capture: Amortizing graph generation costs by capturing new shapes in parallel CPU threads while inference runs on the GPU.

CUDA API: Deep knowledge of cudaStreamBeginCapture and cudaGraphInstantiate.

PyTorch: Utilizing torch.cuda.make_graphed_callables for rapid integration.

TensorRT-LLM: Gold standard for highly optimized graph replay mechanisms.

Candidates are frequently tested on "CPU-bound vs. GPU-bound" bottlenecks. Identifying CPU dispatch overhead as the silent killer of dynamic batching and proposing CUDA Graphs as the fix separates seniors from mid-level engineers.