Navigating the Evolving Landscape of Local AI Inference
After three years immersed in private LLM infrastructure, I remain intrigued by the local AI market. The ecosystem is rapidly evolving with:
- Advanced Systems: DGX Spark, high-end Mac Studios, AMD Strix Halo, and the upcoming DGX Station.
- Efficient Models: Smaller, optimized models and accessible inference engines like llama.cpp and vLLM.
- User-Friendly Frontends: Tools such as Ollama and LMStudio are simplifying local deployments.
Yet, my conversations reveal a disparity: more research is occurring than actual deployment. This raises essential questions for those in the field:
- What’s your setup? Is it personal or team-based?
- What drives your choice? Privacy, regulations, latency, cost, or experimentation?
While I’m skeptical about cost efficiency favoring cloud inference, I’m eager to learn what factors could tip the scale for local AI.
💬 Share your insights below and let’s spark a conversation! What motivates your choice for local AI?
