How to Reduce Memory Use in Reasoning Models
Reasoning models can solve harder problems by “thinking” longer—but every extra reasoning step consumes more memory, slows inference, and raises the cost of running them. This article explores two promising ways to break that trade-off: LightThinker, which teaches models to compress their own chains of thought into compact internal notes, and DeepSeek’s Multi-Head Latent Attention, which dramatically shrinks the attention cache. It also considers what could happen if these techniques were combined to build reasoning models that are faster, leaner, and just as capable.
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