PabloAnalyst @PabloAnalyst
Tech/Consumer/Electronics Share the ideas. DYOR. @Infocaseworld @DukeU @DukeFuqua Shinjuku-ku, Tokyo Joined June 2024-
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The result? Affordable, open-source models with flexible deployment options and greater control over data.
The future may well belong to open-source LLMs. Running these models locally can be expensive, but that doesn't mean everyone needs to host their own. Third-party providers can spread infrastructure costs across users and offer low-cost inference APIs at scale.
The real test of on-device AI won't be how large a model your laptop can run. It will be whether an affordable device can reliably handle everyday tasks without killing battery life. On-device AI wins when people stop thinking about the model and start relying on the assistant.
Apple is tackling another bottleneck: context. An assistant that can find the right email, message, or document is more useful than one that simply scores higher on benchmarks. China is also encouraging AI agents across retail, operations, and logistics.
DeepSeek is opening up inference infrastructure. NVIDIA is expanding local compute. Apple is making personal context more accessible. The next breakthrough may not be a smarter model.
mitte.ai @mitte_ai
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