deep learning
deep learning at Beyond Market Intelligence is a file of 147 stories. The newest of them: “From AI Research to Visual Data: Andrew Dai on the Next Frontier”, “Your Local LLM Costs Less Than You Think, and Size Isn't the Reason”, and “Stop Wasting Compute: Smarter Ways to Speed Up LLM Inference”. A $300M pre-seed valuation before a single product launch. The cheapest local model isn't the smallest, and the priciest isn't the biggest. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work… The list below is every deep learning story on Beyond Market Intelligence, newest first.
From AI Research to Visual Data: Andrew Dai on the Next Frontier
A $300M pre-seed valuation before a single product launch. That is the kind of conviction Andrew Dai brings to the table, built on a decade of shaping the AI systems that inform today's most talked-about tools. He sees visual AI as the next major frontier, and it is hard to argue with that trajectory. This is a story about betting big on what comes next, not just iterating on the past.
Your Local LLM Costs Less Than You Think, and Size Isn't the Reason
The cheapest local model isn't the smallest, and the priciest isn't the biggest. That's the takeaway from a hands-on measurement of eight models running on a single RTX 3090, where actual GPU electricity costs were tracked in euros per million tokens. It's a grounded, useful reality check for anyone exploring local AI. If you're weighing similar tradeoffs in your own workflow, our piece on verifying your AI's understanding offers another practical angle worth your time.
Stop Wasting Compute: Smarter Ways to Speed Up LLM Inference
Scaling LLMs isn't about adding GPUs. It's about removing wasted work from every request. That distinction shapes our latest guide, which lays out 12 practical ways to cut latency and inference costs without chasing hardware. Each tactic targets the inefficiencies that quietly slow production systems. If you're ready to move past brute-force scaling, this is where you start. For a deeper look at how distributed systems underpin these choices, our guide on unlocking distributed training algorithms pairs well with the conversation.