Chunked Prefill
Chunked Prefill employs a segmentation strategy that breaks down Prefill requests into smaller subtasks, which are then batched together with Decode requests. This approach better balances compute-intensive (Prefill) and memory-intensive (Decode) operations, optimizes GPU resource utilization, reduces computational overhead and memory footprint per Prefill, thereby lowering peak memory usage and avoiding out-of-memory issues.
When Chunked Prefill is enabled, the scheduling policy follows these priority rules:
- Decode-first processing: The system prioritizes batching all pending Decode requests to ensure real-time responsiveness for generation tasks.
- Dynamic chunking: When Prefill tasks are pending, the system schedules them within the
max_num_batched_tokens
budget (the maximum token capacity per batch). If a Prefill request exceeds the remaining budget, it's automatically split into compliant chunks.
This mechanism effectively reduces Inter-Token Latency while optimizing memory usage, enabling longer input sequences.
For details, refer to the research paper.
Usage & Tuning
When enabling Chunked Prefill in FastDeploy, adjust performance via max_num_batched_tokens
:
- Smaller values improve Inter-Token Latency.
- Larger values improve Time To First Token.
- Recommended:
max_num_batched_tokens > 8096
. For small models on high-performance hardware, increase this value for higher throughput.
Use Cases
- Long-context inference (e.g., 128K context): Reduces memory peaks to avoid OOM errors.
- Generation tasks: Lowers Inter-Token Latency for faster response.