this post was submitted on 29 Apr 2026
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LocalLLaMA

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Welcome to LocalLLaMA! Here we discuss running and developing machine learning models at home. Lets explore cutting edge open source neural network technology together.

Get support from the community! Ask questions, share prompts, discuss benchmarks, get hyped at the latest and greatest model releases! Enjoy talking about our awesome hobby.

As ambassadors of the self-hosting machine learning community, we strive to support each other and share our enthusiasm in a positive constructive way.

Rules:

Rule 1 - No harassment or personal character attacks of community members. I.E no namecalling, no generalizing entire groups of people that make up our community, no baseless personal insults.

Rule 2 - No comparing artificial intelligence/machine learning models to cryptocurrency. I.E no comparing the usefulness of models to that of NFTs, no comparing the resource usage required to train a model is anything close to maintaining a blockchain/ mining for crypto, no implying its just a fad/bubble that will leave people with nothing of value when it burst.

Rule 3 - No comparing artificial intelligence/machine learning to simple text prediction algorithms. I.E statements such as "llms are basically just simple text predictions like what your phone keyboard autocorrect uses, and they're still using the same algorithms since <over 10 years ago>.

Rule 4 - No implying that models are devoid of purpose or potential for enriching peoples lives.

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I have spent a few days tweaking this setup to attain these results:

Model Prompt (tok/s) Generation (tok/s)
gemma-26b-moe 8.9 6.4
qwen3.5-4b-no-think 21.5 8.4

Although modest, It is great for local parsing and analysis of my self-hosted homelab data where sending logs to external APIs is not desirable.

Typical workflows:

  • Log analysis: Piping journalctl output to the API for error triage and root cause hypothesis generation.
  • Configuration synthesis: Generating AdGuard Home rewrite rules, nginx location blocks, or fstab entries based on defined parameters.
  • Troubleshooting constraints: Querying for failure modes specific to the local topology (e.g., NFS mount failures over a 1 Gbps unmanaged switch, Tailscale DERUP routing behind CGNAT).
  • Alert context: Correlating Beszel/Uptime Kuma notifications with service-specific knowledge (e.g., "mediabox CPU spike while SabNZBd is extracting").
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