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AI Efficiency

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Sunday, 13 September 2026

Faced with less compute and fewer tokens, Chinese AI labs are tightening the gap with the US by just being more efficient

Fortune 24 ● 9 sources

Chinese AI labs are closing some of the performance gap with U.S. labs by improving efficiency under constrained compute and token budgets, and U.S. agencies have also accused some Chinese companies of obtaining training capabilities via bulk subscriptions and training on model outputs. Stanford estimated Anthropic’s top model was ahead of DeepSeek’s by 2.7% earlier this year, while analysts cite algorithmic work that reduces attention-calculation complexity by an order of magnitude. Enterprises increasingly adopt Chinese open-weight models—such as via rising spending shares—while U.S. frontier models remain ahead on the most complex tasks.

Chip Huyen explains how to cut inference costs without new hardware

The New Stack 1 month ago 16 ● 4 sources

Chip Huyen delivered and then recapped guidance on lowering large language model inference costs by measuring latency and improving batching, caching, and model/service efficiency rather than relying on new hardware. She cites caching hit rates of 90% to 97% from her open-source tool Sniffly. As a result, teams are increasingly prioritizing metrics like TTFT and goodput and using widely available techniques like prompt caching, quantization, and continuous batching to cut per-token costs while preserving model quality.

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