SGLang and Meta Collaborate on Advanced CUDA Graph Inference Techniques
ying11231 · x · 2026-08-18
SGLang, in collaboration with Meta AI, released a technical blog detailing advanced techniques for applying CUDA Graph in inference engines. The teams refactored SGLang's support around a common runner/backend interface to systematically address challenges like operator compatibility, graph management, and memory overhead.
Key innovations highlighted include:
- Breakable CUDA Graph (BCG): The new default prefill backend that removes dependencies on torch.compile, enabling faster setup and broader compatibility.
- Experimental Full CUDA Graph for Prefill: Uses request padding to enable full graph capture for dynamic prefill workloads, initially introduced for FA4 and FlashInfer.
- Unified Architecture: A flexible design for managing different graph capture strategies.
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