Vitalik shares a local private LLM solution, emphasizing privacy and security first
By: rootdata|2026/04/02 15:58:10
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Vitalik Buterin shared a post outlining his localization and privatization LLM deployment plan as of April 2026. The core goal is to prioritize privacy, security, and autonomy, minimizing the 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 leakage, model jailbreaks, and malicious content exploitation.
In terms of hardware, he tested solutions including a laptop equipped with an NVIDIA 5090 GPU, an AMD Ryzen AI Max Pro 128 GB unified memory device, and DGX Spark, using the Qwen3.5 35B and 122B models for local inference.
Among these, the 5090 laptop achieved approximately 90 tokens/s with the 35B model, the AMD solution around 51 tokens/s, and DGX Spark about 60 tokens/s. Vitalik indicated that he prefers to build a local AI environment based on high-performance laptops while using tools like llama-server, llama-swap, and NixOS to establish the overall workflow.
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