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User laments massive open-weight LLMs are impractical for local hosting

A Reddit user expresses frustration with the increasing size and complexity of "open weight" large language models, arguing that many are practically unusable for individuals due to their massive parameter counts and context windows. The user notes that while the weights are technically available, running these models locally is impossible without enterprise-grade hardware, defeating the original purpose of self-hosting and community sharing. This trend, exemplified by models like GLM-5.2, has shifted the focus from optimizing models for personal hardware to essentially serving as free marketing for API or cloud-based access. AI

IMPACT Highlights the growing gap between released model sizes and individual user hardware capabilities.

RANK_REASON User opinion piece on the practical limitations of large open-weight models.

Read on r/LocalLLaMA →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

User laments massive open-weight LLMs are impractical for local hosting

COVERAGE [1]

  1. r/LocalLLaMA TIER_1 English(EN) · /u/Mountain_Patience231 ·

    Anyone else completely tuning out these massive "open weight" drops?

    <!-- SC_OFF --><div class="md"><p>Tbh the benchmarks on stuff like GLM-5.2 look insane. 753B params, 1M context, MIT license... everyone is throwing a party on the front page right now. But like... what is actually &quot;local&quot; about this anymore? A 700B+ MoE isn't fitting o…