Open-source LLMs and privacy-friendly inference options discussed

By PulseAugur Editorial · [1 sources] · 2026-07-25 12:00

The discussion highlights the existence of Large Language Models (LLMs) trained on open data and licensed under open-source principles, drawing parallels to traditional Free and Open Source Software (FOSS). Examples provided include models from Apertus and AllenAI's Olmo models. The conversation also touches upon privacy-friendly inference options, such as Venice.ai, or the possibility of self-hosting these models. AI

IMPACT Highlights the availability and accessibility of open-source LLMs and privacy-focused inference solutions.

RANK_REASON The item is a social media discussion about open-source LLMs and privacy, not a primary announcement or release.

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Open-source LLMs and privacy-friendly inference options discussed

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Mastodon — fosstodon.org TIER_1 English(EN) · [email protected] · 2026-07-25 12:00

@ r_alb That is an ignorant statement. There are LMs trained with open data and licenced openly, just like traditional FOSS. E.g. Swiss-based Apertus models and

@ r_alb That is an ignorant statement. There are LMs trained with open data and licenced openly, just like traditional FOSS. E.g. Swiss-based Apertus models and AllenAI Olmo models. One can find privacy-friendly inference providers like Venice.ai to serve them, or self-host. We w…

COVERAGE [1]

@ r_alb That is an ignorant statement. There are LMs trained with open data and licenced openly, just like traditional FOSS. E.g. Swiss-based Apertus models and

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