Close the project with the final reference stack, a delivery checklist and a clear next-steps roadmap.
report-cluster-ai.html in the workspace — interactive document that consolidated the research and originated this course.
The final architecture is a local multimodal pre-processor: Qwen3.6-35B-A3B handles text/code/RAG, Gemma 4 handles vision+audio, DeepSeek-V4-Flash is available on demand for 1M-token context, and RAG/orchestration filter what goes to the cloud.
Think of a lean factory: local machines do the repetitive, cheap work; the external assembly line only steps in when a part needs artisanal finishing. The result is speed, privacy and controlled cost.
Recommendation from report-cluster-ai.html (July 2026): run three models side by side — Qwen3.6-35B-A3B-MLX-8bit (~22–28 GB, ~80 tok/s), Gemma-4-26B-A4B-MLX-4bit (~18 GB) and DeepSeek-V4-Flash-GGUF UD-IQ3_XXS (~103 GB) on demand. That leaves ~78 GB for Qwen + Gemma + buffers/KV-cache. DeepSeek-V4-Flash is reserved for 1M-token context; Qwen3.6-35B-A3B is the default text/code engine.
No choice is perfect. Qwen3.6-35B-A3B is fast and lean, but does not reach 1M context. DeepSeek-V4-Flash reaches 1M, but takes up almost all RAM. Gemma 4 is native multimodal, but lags behind Qwen3.6-35B-A3B on hard code. The handoff documents these decisions for whoever continues the project.
Recorded decisions: default MLX engine; Qwen3.6-35B-A3B as main LLM; Gemma-4-26B-A4B for VLM/audio; DeepSeek-V4-Flash on demand for 1M-token context; RAG with pgvector + bge-m3; orchestration via LangGraph/n8n; Open WebUI frontend; Ollama as a quick fallback.
Para entregar o projeto, verifique: ferramentas instaladas, modelos baixados, servidores subindo, Open WebUI acessível, RAG configurado e documentação de decisões. O handoff garante que outra pessoa (ou você no futuro) consiga retomar sem adivinhar.
Technical checklist: which ollama mlx_lm python3 node npm gh brightdata; models mlx-community/Qwen3.6-35B-A3B-8bit, Gemma-4-26B-A4B-MLX-4bit, GLM-OCR-bf16, Kokoro-82M-bf16, Qwen3-ASR-1.7B-8bit; servers on ports 8080/8081/8000; Postgres + pgvector; Open WebUI at localhost:3000.
# Verifique se as ferramentas estão no PATH which ollama mlx_lm python3 node npm gh brightdata # Teste o servidor de texto curl http://localhost:8080/v1/models # Teste pgvector psql rag_local -c "SELECT * FROM pg_extension WHERE extname='vector';"
Test what you learned with quick exercises.
pip install -U mlx-lm mlx-vlm mlx-audio and validate the install.mlx-community/Qwen3.6-35B-A3B-8bit and measure tok/s on your typical prompt.mlx_vlm.server with Gemma-4-26B-A4B and describe an image.