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Local LLMs struggle with coding tasks, raising proprietary platform concerns

A user found that locally hosted large language models (LLMs) like Qwen and Gemma performed poorly for coding tasks compared to free online models. Despite trying models ranging from 0.8B to 30B parameters, both speed and quality were unsatisfactory. This experience highlights concerns about potential lock-in to proprietary platforms as AI-first coding becomes more prevalent. AI

IMPACT Highlights potential limitations of current local LLMs for coding, suggesting a need for improvement or reliance on cloud-based solutions.

RANK_REASON User opinion piece on the performance of local LLMs for coding.

Read on Mastodon — fosstodon.org →

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

Local LLMs struggle with coding tasks, raising proprietary platform concerns

COVERAGE [1]

  1. Mastodon — fosstodon.org TIER_1 English(EN) · [email protected] ·

    I've been playing around with hosting local # LLMs on my laptop and the results for coding have not been encouraging. I've tried Qwen and Gemma mostly, ranging

    I've been playing around with hosting local # LLMs on my laptop and the results for coding have not been encouraging. I've tried Qwen and Gemma mostly, ranging in size from 0.8B to 30B parameters but all have been beaten in both speed (expected) and quality (unexpected) by even b…