Popping the GPU Bubble
4 hours ago
- #Pipelined Decoding
- #AI Model Optimization
- #GPU Efficiency
- The GPU often sits idle during AI model inference due to waiting for CPU instructions, a phenomenon called GPU bubbles.
- Text generation is autoregressive, producing one token at a time sequentially, requiring round trips between CPU and GPU.
- Pipelined decoding overlaps CPU and GPU work by launching the next token's GPU forward while the CPU processes the previous token.
- Ping-pong slots involve two buffer sets to prevent collisions, allowing CPU to process results while GPU runs the next forward.
- Forward now, sample later decouples GPU forward passes from sampling dependencies, enabling constrained decoding without special-casing.
- Zombies handle finished sequences by marking them finalized but delaying release until inflight references drop to zero.
- Prefill and decode work share the same pipeline, allowing overlap and preventing serialization bottlenecks.
- Speed gains from pipelining scale with GPU speed; faster GPUs see larger improvements due to reduced bubble impact.
- Pipelining reduces idle time significantly, with observed speedups ranging from ~6% to over 35% depending on hardware and batch size.