Vitalik shared his local private LLM solution, emphasizing privacy and security as priorities.

Vitalik Buterin shared his localized, private LLM deployment solution up to April 2026. The core objective is to prioritize privacy, security, and autonomy, minimizing opportunities for remote models and external services to access personal data. This is achieved through local inference, local file storage, and sandbox isolation to reduce the risks of data breaches, model jailbreaking, and malicious content exploitation. On the hardware side, he tested solutions including a laptop with an NVIDIA 5090 GPU, an AMD Ryzen AI Max Pro with 128 GB unified memory, and DGX Spark, using Qwen3.5 35B and 122B models for local inference. The 5090 laptop achieved approximately 90 tokens/s with the 35B model, the AMD solution approximately 51 tokens/s, and the DGX Spark approximately 60 tokens/s. Vitalik stated that he prefers to build local AI environments based on high-performance laptops, while using tools such as llama-server, llama-swap, and NixOS to build the overall workflow.