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'A concise 2025 guide to the top local LLMs, comparing context lengths, VRAM needs, quant presets, and license implications for practical on-prem deployments.'

Researchers at UC Berkeley and UCSF have developed Adaptive Parallel Reasoning, a novel method that allows large language models to dynamically distribute inference tasks across parallel threads, enhancing reasoning performance without exceeding context window limits.