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Building A16Z's Personal AI Workstation
In the era of foundation models, multimodal AI, LLMs, and ever-larger datasets, access to raw compute is still one of the biggest bottlenecks for researchers, founders, developers, and engineers. While the cloud offers scalability, building a personal AI Workstation delivers complete control over your environment, latency reduction, custom configurations and setups, and the privacy of running all workloads locally.
In the era of foundation models, multimodal AI, LLMs, and ever-larger datasets, access to raw compute is still one of the biggest bottlenecks for researchers, founders, developers, and engineers. While the cloud offers scalability, building a personal AI Workstation delivers complete control over your environment, latency reduction, custom configurations and setups, and the privacy of running all workloads locally. While we are still in the process of testing full NVIDIA GPUDirect Storage (GDS) compatibility, it could allow GPUs to fetch data directly from NVMe drives, enabling direct-memory access (DMA).
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