Every published setup that reports this model, with its machines, quantization, runtime and reported results. Use the directory filters below to narrow by quantization, capacity or runtime.
Capacity is the reported total for this setup. Multi-GPU and unified-memory layouts can behave differently.
Rather than chatting with a local model, this user runs local document Q&A, summarisation, translation and grammar correction on an M4 Pro, then built a desktop app to tie the pieces together.
A researcher in applied mathematics is assembling a fully local pipeline to process roughly 20,000 PDFs, comparing PDF extractors, chunking strategies and small summarisation models.
After experimenting, this user argues a real computer-manipulating agent needs at least a Strix Halo plus an oculink R9700, or a DGX Spark plus a second GPU for auxiliary models.
A local deep-research pipeline using GPT-OSS 120B and Gemma 4 12B produces results on par with cloud deep research at a fraction of the token cost.
Rather than one big general model, this setup uses several small, fast ones for narrowly scoped, deterministic jobs — starting with OCR and metadata for a self-hosted Paperless document server.
One small model does double duty: correcting speech-to-text output and acting as the decision-making 'brain' for home automation triggers.
