We Asked 3 VTubers to Test Local LLMs Live - Here's What Broke First
VRAM, quant levels, and prompt fails: quoted with permission from local-LLM VTuber testers, with embeds and links back.
Verdict: false alarm.
Primary source: Creator VOD timestamps, linked with permission
Local-model hype promises privacy and zero cost. Stream-tested numbers show where that promise holds on one PC.
We did not lab-test alone. We quoted people who stream local-LLM tests weekly - and we told them we were publishing.
How we worked (professional way):
- DM’d each creator with claim + timestamp we wanted to quote
- Got written “yes” to quote 30 sec + embed
- Sent draft back before publish with backlink + author box
- Embedded original YouTube/Twitch (no re-upload)
What they found
Tester A (12GB VRAM, Q4_K_M 7B-13B): smooth for chat/RAG; 70B offloads to CPU and stalls. “Chat is fine, agents are not.”
Tester B (24GB + CPU offload): 32B Q4 workable at ~12-18 tok/s; 70B Q3 usable for batch, painful live.
Tester C (Mac 18GB unified): best perf-per-watt for 7B-13B, memory pressure kills multitasking with OBS + VTuber rig.
Bottom line: for live VTuber + local LLM on same machine, 7B-13B Q4 is the sweet spot in 2026. 70B needs a second machine or API fallback.
For a direct comparison of tools under streaming load, see Ollama vs LM Studio on 16GB RAM.
Want to be quoted?
If you test local LLMs on stream, send your VOD timestamp. We embed, link, and share the article with you to repost. See /about#creators.
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