Benchmarking C++ vs PyTorch for RLHF Reward Model Inference
Venkata Naga Sai Vishnu Rohit Pulipaka · hf · 2026-07-30
In RLHF pipelines, reward model scoring often bottlenecks policy updates. The authors built a native C++ inference engine on ONNX Runtime and benchmarked it against PyTorch eager mode, torch.compile, and FastAPI across CPU and GPU environments.
Key findings:
- CPU: The C++ engine significantly outperformed all baselines, with non-overlapping confidence intervals.
- GPU: The engine beat PyTorch and FastAPI, but torch.compile came out ahead.
Further testing revealed that the speedup primarily stems from the ONNX Runtime rather than the C++ language itself, and that batching strategy mattered more than expected. Additionally, while scoring is relatively fast, optimizing it frees up compute capacity for the rollout generation phase.
More from Infra
- Local AI Boom Could Make Storage Drive Manufacturers a Fortune — cocktailpeanut · 2026-07-30
- Meta Shares Plunge 9% After-Hours as AI Spending Crushes Margins & Cash Flow — ivan_bezdomny · 2026-07-30
- US Commerce Dept Allocates $874M to Accelerate Semiconductor R&D — imjustnewatai · 2026-07-30
- Valar Atomics Founder: Cheap Energy Will Always Create Its Own AI Demand — No Priors · 2026-07-30
- Qualcomm Q3 revenue beats estimates but weak Q4 EPS guide weighs — firstadopter · 2026-07-30
- Sam Altman Understands Why People Don't Want AI Data Centers in Their Backyards — businessinsider · 2026-07-30