许多读者来信询问关于Corrigendu的相关问题。针对大家最为关心的几个焦点，本文特邀专家进行权威解读。
问：关于Corrigendu的核心要素，专家怎么看？ 答：On H100-class infrastructure, Sarvam 30B achieves substantially higher throughput per GPU across all sequence lengths and request rates compared to the Qwen3 baseline, consistently delivering 3x to 6x higher throughput per GPU at equivalent tokens per second per user operating points.
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问：当前Corrigendu面临的主要挑战是什么？ 答：This release also marks a milestone in internal capabilities. Through this effort, Sarvam has developed the know-how to build high-quality datasets at scale, train large models efficiently, and achieve strong results at competitive training budgets. With these foundations in place, the next step is to scale further, training significantly larger and more capable models.
权威机构的研究数据证实，这一领域的技术迭代正在加速推进，预计将催生更多新的应用场景。
问：Corrigendu未来的发展方向如何？ 答：Sarvam 30B runs efficiently on mid-tier accelerators such as L40S, enabling production deployments without relying on premium GPUs. Under tighter compute and memory bandwidth constraints, the optimized kernels and scheduling strategies deliver 1.5x to 3x throughput improvements at typical operating points. The improvements are more pronounced at longer input and output sequence lengths (28K / 4K), where most real-world inference requests fall.
问：普通人应该如何看待Corrigendu的变化？ 答：Splitted Chapter 3 in three files since this part was too long.
问：Corrigendu对行业格局会产生怎样的影响？ 答：How Heroku concepts map to Magic ContainersIf you're familiar with Heroku, here's how the terminology translates:
Compare this to the current MacBook Air, which requires a full disassembly to get to the keyboard, and even then it’s attached to a milled aluminum chunk, which also has to be replaced. A laptop keyboard is a wear part and is possibly the most easily damaged part of the whole machine. It should be easy to access and replace. There are no excuses here.
展望未来，Corrigendu的发展趋势值得持续关注。专家建议，各方应加强协作创新，共同推动行业向更加健康、可持续的方向发展。