NVIDIA, DeepSeek, and the Future of LLMs—Why GPUs Are Still King

NVIDIA’s Wild Ride: Market Chaos and Recovery

Just this week, NVIDIA’s stock dropped 20% on news that shook the AI market, only to rebound almost 10% overnight. But what does this tell us about the future of AI and large language models (LLMs)?

The core reason NVIDIA GPUs are so valuable is simple: they make mathematical vector computations fast. Without GPUs, training massive AI models would be orders of magnitude slower, making the entire industry impractical.

Why DeepSeek’s Breakthrough Matters

DeepSeek made headlines not just for the scale of its 14.8 trillion token training run, but for a hardware optimization breakthrough that allowed them to use GPUs far more efficiently.

What Did DeepSeek Do?

  1. Maximized GPU Utilization with FP8 Precision – DeepSeek’s “Sharpie Pen” approach to FP8 precision allowed their GPUs to process more calculations per second without sacrificing output quality. Lower precision meant faster throughput and lower energy costs.
  2. FP8 Quantization + Efficient Scheduling – By optimizing their GPU scheduling pipeline, they ensured no GPU cycles were wasted, allowing each processor to stay at peak efficiency throughout training.

By keeping GPUs fully loaded at all times and reducing computation overhead, DeepSeek squeezed more performance per dollar than traditional LLM training setups. This is a huge deal because AI training is one of the most expensive computing tasks in existence.

The GPU’s Role Doesn’t End at Training

Even after an LLM is trained, GPUs remain essential. Why?

Every time you interact with an LLM, it performs a scaled-down version of the same vector computation process used in training. This means inference (using the model) is still compute-heavy—and still needs GPUs to be fast and efficient.

This is why the AI hardware race is far from over—companies still need powerful GPUs not just to train, but to serve AI applications at scale.

What This Means for the Future of LLMs

The DeepSeek approach lowers the barrier for training new, highly specialized LLMs. Here’s why:

  • If DeepSeek’s hardware optimizations become commoditized, training LLMs gets cheaper.
  • Startups could train industry-specific or niche LLMs faster and at lower cost.
  • This could shift AI’s competitive advantage from “who has the biggest model” to “who has the most specialized and efficient model.”

And what happens next? If startups can train their own LLMs with this approach, the application market will explode with highly optimized, task-specific AI models.

The Bottom Line: AI Isn’t Just Faster—It’s Cheaper Without Sacrificing Quality

We’re likely heading into an era where:

  • GPUs remain the backbone of AI, both for training and application deployment.
  • Specialized LLMs will challenge the dominance of general-purpose models like GPT-4 and Gemini.
  • Hardware and efficiency optimizations (like DeepSeek’s) will shape the AI industry’s next major leap.

The race isn’t just about who has the most data or biggest models anymore—it’s about who can make AI cheaper, faster, and more efficient without compromising quality.