Cost Optimization

TL;DR Astra is a multi‑agent LLM system that optimizes existing CUDA kernels instead of generating them from PyTorch. On three production‑relevant SGLang kernels, it delivered 1.32× average speedup (up to 1.46×) without fine‑tuning—just structured zero‑shot prompting. The win isn’t a single trick; it’s a division of labor: testing, profiling, planning, and coding each handled by a specialized agent that iterates toward faster, still‑correct kernels. Why this matters for business readers GPU efficiency is the new gross margin. If your serving stack pushes trillions of tokens per day, a 25–45% kernel‑level speedup compounds into: ...

When teams push large text workloads through LLMs (contract triage, lead deduping, safety filtering), they face a brutal choice: pay for the “oracle” model (accurate but pricey) or accept quality drift with a cheaper “proxy”. Model cascades promise both—use the proxy when confident, escalate uncertain items to the oracle—but in practice they’ve been fragile. SUPG and similar heuristics often over‑ or under‑sample, rely on asymptotic CLT assumptions, and miss targets when sample sizes are small. The BARGAIN framework fixes this by combining task‑aware adaptive sampling with tighter finite‑sample tests to certify targets while maximizing utility (cost saved, recall, or precision). The authors report up to 86% more cost reduction vs. SUPG for accuracy‑target (AT) workloads, and similarly large gains for precision‑target (PT) and recall‑target (RT) settings—with rigorous guarantees. ...