Developers Test Local AI Code Memory Server Using Ollama and ChromaDB to Avoid Cloud Risks

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Developers concerned about cloud API costs and privacy risks of sharing proprietary code with third-party services have been exploring fully local alternatives for AI-assisted coding. The zerikai_memory tool was tested in local mode, routing all inference through Ollama on-device using either mistral:7b or the newer ornith:9b model, with ChromaDB handling code retrieval via vector search. Hardware tests on an NVIDIA RTX 3050 with 8GB VRAM showed mistral:7b averaged 6.14 seconds per query while ornith:9b averaged 13.39 seconds, with the latter occasionally spilling into slower shared system memory. ornith:9b, released in June 2026 and built for agentic coding tasks, scored 69.4 on SWE-Bench Verified but carried a higher latency cost on consumer-grade hardware. The post-mortem concludes that model choice depends on available VRAM and latency tolerance, and recommends running the included benchmark script before switching models.

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