In multi-turn agentic workloads, agent frameworks frequently edit prompt history by removing previous thinking segments, eliding older observations, or pruning tool outputs. In models employing hybrid-attention architectures—which interleave full attention with recurrent layers (such as gated DeltaNet) or sliding-window attention—context is compressed into recurrent states. Because each recurrent state checkpoint consumes as much memory as hundreds of tokens of standard Key-Value (KV) cache, serving runtimes maintain only sparse checkpoints across the sequence. When an agent modifies or truncates context, any recurrent checkpoint positioned after the edit point is invalidated, forcing the engine to roll back to the nearest preceding valid checkpoint and re-prefill thousands of tokens. On consumer GPUs with limited dense computation throughput, these redundant re-prefills cause substantial latency spikes that can trigger client timeouts.

Context Recomputation and Checkpoint Invalidation in Agentic Serving

The prefilling phase is generally considered a compute-bound process. This is because the parallel computation of self-attention for the entire sequence merges many operations into a single, large one. This approach minimizes data transfers between memory and the processing unit (like a GPU), meaning the primary performance limitation becomes the raw computational power of the hardware, rather than the speed at which data can be moved (memory bandwidth).

Prefilling as a Compute-Bound Process

During prefill, a prompt supplies hundreds to thousands of tokens to each layer. Although each token routes to only $$k$$ experts, the union of selections across the prompt typically activates nearly the entire expert set. When the full model exceeds GPU memory, almost the complete expert pool must therefore be streamed over the CPU–GPU interconnect during each prefill pass. Transfer time consequently scales with the model's total parameter footprint rather than its sparse per-token active path, causing multi-second I/O latency and GPU idle time under on-demand expert loading.

Expert Transfer Bottleneck in Edge MoE Prefill

Learn Before

Related

Learn After