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// 60d agoINFRASTRUCTURE
OpenClaw users share local model stacks
A r/LocalLLaMA thread asks what people are running locally for OpenClaw, and the starter config pairs Qwen3.5-27B for text and vision with Voxtral models for TTS and STT across two GPUs. The replies quickly turn to latency, containerization, and whether an agent with shell access is safe enough to trust.
// ANALYSIS
The interesting part isn’t the exact model lineup; it’s that local OpenClaw is becoming a full assistant stack. Once you add voice, tools, and memory, the real bottlenecks are latency, isolation, and orchestration rather than raw model size.
- –Splitting text/vision and speech across separate GPUs is a pragmatic way to keep the assistant responsive.
- –The safety pushback in the comments is on point: shell and file access need a tight sandbox, not optimism.
- –Qwen3.5-27B reads like the sweet spot here, big enough to feel capable without making local deployment feel absurd.
- –Low-latency STT/TTS is the difference between a usable voice agent and a demo that feels laggy after five minutes.
- –This thread shows local OpenClaw is moving from hobbyist experiment to serious self-hosted AI infra.
// TAGS
openclawagentllmmultimodalspeechgpuself-hosted
DISCOVERED
60d ago
2026-03-29
PUBLISHED
60d ago
2026-03-29
RELEVANCE
7/ 10
AUTHOR
big___bad___wolf