AI is evolving fast, making it hard to pinpoint a clear picture. But after following the AI arms race for a while, here’s a breakdown anyone, whether a curious enthusiast or a complete beginner,can understand. Distilled from Jeffery Emanuel’s A Short Case for NVidia Stock. In this article, I explain the DeepSeek’s revolution, in simple terms.
The future of A.I. isn’t in just building it bigger but building it wiser.
The Context
There is a lot of talk about Deep Seek stealing from Chat GPT or distilling the ChatGPT model to make itself work; however, that is irrelevant to the debate about what Deep Seek actually brought to the Table. But before we start, you should know how the pre-DeepSeek AIs work so that you can better understand and appreciate the advancements made during the development of the deep-seek AI.
Tokens and Predictions – The LEGO Analogy
At its core, AI, like ChatGPT, works by breaking text into tokens—small chunks of words. Think of tokens as LEGO bricks that AI puts together to create recognizable LEGO models. It first breaks down the input into individual bricks.
Example: If the question asked was What is the capital of France?
it tokenizes to
["What "," is " , " the "," capital", " of " , " France" ," ?"]
AI doesn’t “know” the answer—it predicts it based on patterns from its training. It picks the next LEGO piece in a half-built set. It seems that the “capital of France” and the most likely brick after that is “Paris,” so it is guessed as “Paris.” Yes, folks, you heard it here: AI Guesses or predicts, to be precise. This prediction-based approach is powerful but has limits.
Scaling against the data wall
For many years, AI was improved by feeding it more data, using bigger models, and increasing computing power. The rule was simple: more LEGO (tokens), more Instructions (parameters), and more time and energy (compute power) = a more fantastic LEGO model (smarter AI). This is known as the pre-training scaling law.
But there’s a catch. There is only so much data you can throw at it. Google Books and the Library of Congress combined produce around 7 trillion tokens. Anything after that has negligible returns, and we’re reaching that data wall—a point where throwing more data at AI won’t make it more intelligent. High-quality data is scarce, and the brute-force approach may not be sustainable.
Inference, Hallucinations, and Chain of Thought
AI inference works like building a LEGO spaceship with a manual. It recognizes the pattern, predicts the next piece, and puts it together step by step based on past model builds. It doesn’t 
memorize every spaceship but constructs one using learned patterns of what part would go next.
However, inference can lead to AI hallucinations. When pieces are missing, AI improvises by adding bricks that look right but don’t belong or makes bricks from putty and stuffs them in there. The results may seem correct but are actually entirely false.
Chain-of-thought (CoT) models take a different approach. Imagine building a complex LEGO model without instructions—instead of just predicting the next piece, you think through each step, explaining why each piece fits before moving forward. CoT reasoning makes AI more logical, increasing accuracy and avoiding prediction and guesswork. But, this uses a lot more energy (compute power and memory)
Expenses !!
Training these AI models requires enormous computing power, expensive GPUs, and data centers. Once a model is trained, do we keep using it? Do we build the next model, the next, and the next? Or scrap it and build a newer, better one with the latest tech? This wasteful cycle makes AI innovation costly and unsustainable. It also puts a high price on entry into the AI domain.
Deep Seek: A Shift in Thinking
Deep Seek challenges this model by prioritizing efficiency over brute-force scaling—a game-changer. So, instead of making AI bigger, the future of AI must be making it smarter with fewer resources. Understanding these key shifts will help you see where the industry is headed, whether you’re just getting into AI or following every new breakthrough. So, what has Deep Seek brought to the Table?
Summary – for the ones in a hurry:
Think of AI as building LEGO models. Old-school AIs like ChatGPT kept adding more and more LEGO bricks (data) and bigger instruction manuals (computing power) to make smarter models. But at some point, adding more just didn’t help—like trying to improve a LEGO car by just dumping more bricks on it.
DeepSeek came in with a smarter toolbox. Instead of piling on, they used sharper tools and clever tricks. Like printing blueprints in HD only where needed (mixed precision), typing multiple words at once instead of one (multi-token prediction), and having a group of expert “teachers” who only speak when their subject is needed (Mixture-of-Experts). The model even taught itself to double-check its work and stay on-topic—like a student learning to think before answering (Long Chain-of-Thought) and sticking to one language in the same sentence (language consistency).
DeepSeek innovations For the more technically inclined:
Here I’ve broken down the 7 innovations that DeepSeek brought to the table and have yet to be bested.
- Mixed precision Framework,
- Multi-token prediction system,
- Multi-head latent Attention,
- Mixture-of-Experts (MOE) transformer architecture,
- GPU Dual-pipeline algorithm,
- Long Chain of thought with Data served cold,
- Language consistency
Buckle up; this is going to get technically bumpy.
DeepSeek surpassed multiple challenges that plagued the AI world. As the story goes, the engineers and mathematicians at DeepSeek had to work with financial and hardware constraints, which led to the development of a highly efficient AI model and begged the invention of new techniques for streamlined resource use.
1. Mixed precision
One of the significant innovations that came out of DeepSeek is the “Mixed precision” framework. DeepSeek’s innovation wasn’t just throwing more power at the problem—it was about training smarter, not harder. A key part of this was their use of mixed precision training, a significant shift from how AI models were traditionally trained.
For years, AI training relied on full precision 32-bit numbers to store and process data. Think of these as 4k high-definition blueprints for constructing an AI model. The more details you pack into these blueprints, the more precise your AI’s decisions. The problem? More precision means more memory, more power, and more cost.
DeepSeek took a different route. They scaled down to 8-bit floating-point numbers (FP8), think 1080p, throughout the training process. At first glance, this sounds like a bad trade-off. Why sacrifice precision? But FP8 is not like regular numbers. Instead of being limited to just the usual 256 possible values, FP8 is designed to store a much wider range of numbers, albeit with slightly less precision than full 32-bit. Deepseek used a math trick to store very small (1080p) and large (4k) numbers.
This change unlocked massive memory savings, allowing DeepSeek to train AI models across thousands of GPUs without the typical overhead. Instead of using the existing training method of full precision and then compressing (which leads to quality loss), they built their models natively in FP8, ensuring they got all the benefits without compromising performance, it’s like printing the blueprints in 4k for the fine details but 1080p for the broad strokes.
And the real kicker? Fewer GPUs needed = lower costs. AI labs spend millions on hardware to keep up with the demands of large-scale training. By making FP8 work from the ground up, DeepSeek slashed those costs, making AI training more efficient, scalable, and sustainable.
This isn’t just an optimization—it’s a fundamental shift in how AI models can be trained in the future.
2. Multi-token Prediction—Doubling Speed Without Sacrificing Quality
Traditional transformer-based AI models generate text one token at a time. It’s like typing a message on your phone where autocorrect suggests one word ahead—slow but precise. This method ensures accuracy, but it also means AI models take their time when generating long responses.
DeepSeek rewrote the playbook. Instead of predicting just one token at a time, they engineered a way to predict multiple tokens simultaneously, without losing the precision of single-token inference.
At first glance, this sounds risky. More predictions at once should, theoretically, mean more errors or hallucinations. But DeepSeek found a way to retain the full causal chain of predictions, ensuring that each token still depends on the ones before. No blind guessing—just structured, context-aware predictions. The results? 85–90% accuracy on these additional token predictions. This means inference speed nearly doubles while maintaining high-quality outputs.
For AI applications where speed is critical, this is a game-changer. It will result in faster responses, reduced compute load, and a system that thinks ahead more efficiently.
3. Multi-Head Latent Attention—Smarter Memory, Faster AI
One of the biggest bottlenecks in AI training is how models handle memory—specifically key-value (KV) indices.
Think of KV indices like an address book. When AI processes information, it doesn’t just memorize words—it keeps track of where and how each word relates to the others. These addresses (KV indices) help the model decide which past words to “pay attention” to when predicting the next token.
The problem? Storing these indices eats up memory—fast. Each GPU maxes out at 96GB of VRAM, and these indices quickly consume that capacity. Most AI labs store them in full precision, which is wasteful but has been the standard approach.
DeepSeek’s Multi-Head Latent Attention (MLA) rewrites the rules. Instead of storing every detail of these KV indices, MLA compresses them—while keeping the essential information intact. The breakthrough? This compression isn’t an afterthought. It’s baked directly into how the AI learns, making the entire training process more efficient from the start.
What makes this revolutionary?
- The compression is differentiable—meaning it’s trained just like the rest of the model, using standard optimization techniques.
- It forces the model to focus on what truly matters, instead of storing unnecessary noise.
- Less memory use = better performance. With fewer resources wasted, AI models can work faster and scale more efficiently.
DeepSeek’s approach doesn’t just shrink memory usage—it makes AI smarter by prioritizing useful information. While others continue storing every detail, DeepSeek’s models pay attention to what actually matters—and the results speak for themselves.
4. Mixture-of-Experts (MOE): Smarter not bigger
Imagine a huge school with hundreds of teachers, but each teacher specializes in a different subject—math, history, science, art, etc. When a student asks a question, they don’t go to every teacher in the school. Instead, the right teachers step forward to answer, while the rest stay in the teachers’ lounge.
This is how Mixture-of-Experts (MoE) AI models work. Instead of using one big AI brain for everything, it has many smaller expert brains, and only the ones that are needed get activated. This creates :
- More knowledge, less clutter – The AI can store a massive amount of information without becoming too slow.
- Saves energy – Instead of making all the teachers work at once, only the best ones for the question are used.
- Cheaper to run – Since only a small group of experts is active, it needs less power to work efficiently.
For example, DeepSeek V3 has 671 teachers (billion parameters), far larger than LLaMA 3, but only 37 teachers (billion parameters) are active at any time which makes it faster, much more affordable, and small enough to fit on two consumer-grade NVIDIA 4090 GPUs (~$2000 total). This makes high-performance AI cheaper and more scalable than traditional monolithic models.
5. GPU Dual-Pipeline – The Two-Lane Highway
Imagine a busy road where cars (data) need to travel quickly. Normally, if the road has only one lane, cars must stop and wait when traffic gets heavy. But if you add a second lane, one can be used for driving (computation) while the other handles deliveries (communication), making everything faster and smoother.
DeepSeek’s Dual-Pipeline algorithm does precisely this for GPUs. Instead of making GPUs stop and switch between tasks, it overlaps computation and communication, like running two lanes at once.
– 20 GPUs handle communication, while the rest focus on heavy
computing, leading to much higher efficiency than typical AI training setups. This allows DeepSeek to squeeze more power out of its hardware, making AI training faster and more effective.
6. Long-chain-of-thought
The R1 model by DeepSeek turned heads. Think of it as a new student who outsmarted top schools before anyone saw it coming—even beating big names like Anthropic in solving problems step-by-step like a human would. But here’s the fun bit: DeepSeek’s tools are not locked in a vault. They’re completely free to use and tinker with—like giving away the blueprint to a spaceship so anyone can build one. That means new AI companies can jump in easily, and experts can double-check DeepSeek’s bold claims.
What makes DeepSeek truly special is how it teaches its AI to think—not just spit out memorized answers. Imagine teaching someone chess not by showing them thousands of games, but by just rewarding good moves and letting them figure things out from scratch. That’s what DeepSeek did. The model learned how to reason step-by-step, double-check its work, and spend more brainpower on tougher problems—all on its own.
Most AI models can be tricked into chasing points—giving wrong answers just to earn rewards. DeepSeek solved this with a smart system, like a game show judge that doesn’t just check if your answer is right, but how you got there and whether you explained it clearly. That makes the model more trustworthy and reliable.
And here’s the mind-blowing part: while training, the model naturally learned to pause, notice when something felt off, and try a different route, just like a human rethinking their answer mid-sentence. No one taught it to do that. It just… figured it out. That’s like a kid teaching themselves how to rethink their homework without being told. This was achieved by building the model on these insights by introducing what the DeepSeek team called “Cold-start” data. Cold-start data is a small set of high-quality examples.
7. Language consistency
DeepSeek also cracked another tough nut, i.e. keeping the AI’s language clean and consistent. In older models that tried to “think step-by-step,” the answers sometimes came out like a sentence that started in English and ended in gibberish or another language. Imagine reading a recipe that begins in English and suddenly switches to Mandrin halfway—confusing, right?
To fix this, DeepSeek added a smart “language check” during training. It’s like training a dog not just to fetch, but to always bring back the same kind of stick. The model was gently nudged to speak clearly and stay in one language, even if that meant sacrificing a tiny bit of performance. The result? Answers that actually make sense from start to finish.
Conclusion
DeepSeek represents a paradigm shift in the world of A.I. They changed the equation by rewiring the engine for performance instead of building a bigger car and hoping it will win the race. Each no innovative technique that was engineered, helped boost the accuracy of whatever bit was dropped for performance. Their focus on working smarter and not harder has shown that The future of A.I. isn’t in just building it bigger but building it wiser.
