Guess what? DeepSeek just dropped a surprise! Their latest update, V3-0324, is now the highest-scoring non-reasoning AI model on the Artificial Intelligence Index. Yes, it even outperformed big names like Google’s Gemini Pro, Claude 3.7 Sonnet, and Meta’s Llama 3.3.
Now to keep it real, it’s not smarter than models like DeepSeek R1 or OpenAI’s top ones, which are great at complex thinking. But here’s the fun part. In situations where speed really matters like chat tools or quick tasks, DeepSeek V3-0324 is showing it can be super helpful.
I’m looking into DeepSeek V3. How did they build it with just 6 million dollars? DeepSeek V3 0324 is starting a new chapter for open source AI, already going up against the biggest names. I didn’t think it would come out this fast, but here it is, and it’s worth checking out.
What Is DeepSeek? (Explained Like You’re 10)
DeepSeek is a smart tech company from China that builds artificial intelligence (AI) tools. It was started in 2023 by a man named Liang Wenfeng. His goal? To help China become a leader in AI, not just follow what other countries like the United States are doing.
But here’s the cool part
In 2021, before many people even knew what was coming, Liang started collecting thousands of Nvidia computer chips. These chips are like the brainpower behind powerful AI. Just after that, the United States made a rule to stop selling those chips to China. Talk about perfect timing.
What Makes DeepSeek So Special?
Unlike big names like OpenAI and Meta, DeepSeek says it can build AI faster, cheaper, and more efficiently. For example, one of its latest AI models only cost 5.6 million dollars to train. That might sound like a lot, but compared to others that cost up to 1 billion dollars, it is a huge saving.
In Simple Words
- DeepSeek is a new Chinese AI company.
- The founder is Liang Wenfeng.
- He started collecting Nvidia chips early which was a smart move.
- Their AI is cheaper to train than OpenAI or Meta.
- The goal is to make China a leader in AI.
Now that you have the basics of what DeepSeek is all about, let’s talk about the buzz surrounding its latest update, DeepSeek V3-0324!
DeepSeek V3-0324: A New Era for Open-Source AI
So, what’s new with DeepSeek? Well, they have just released an update that is catching everyone’s attention. DeepSeek V3 0324 is the latest version that is raising the standard for open-source AI.
It is not just any update. It is making a big impact, especially for things like chatbots, customer service, and live translation. DeepSeek V3 0324 is showing it can go toe to toe with and even beat some of the biggest AI tools out there.
What are the main improvements in DeepSeek V3-0324
Here’s what’s got everyone talking on Twitter about the latest update: DeepSeek V3-0324 brings some seriously impressive specs to the table!
- A 128k context window (though it’s capped at 64k via DeepSeek’s API).
- A mind-blowing 671 billion total parameters, requiring 700GB of GPU memory for FP8 precision.
- 37 billion active parameters, making it a powerhouse for text processing.
- It’s text-only (so no multimodal support for now).
- And, it’s licensed under the MIT License, meaning it’s open-source.
Availability: Open-source weights are available on Hugging Face, and the model can be run locally with instructions from the DeepSeek-V3 repository.
What Experts Are Saying About DeepSeek V3?
DeepSeek V3 has not only caught attention for its budget-friendly build but also earned positive feedback from experts and users across the AI community.
Reuven Cohen, a tech consultant from Toronto, tested the model and shared
“I’ve been using DeepSeek V3 since December. It holds its own against GPT-4 and Claude — and it’s much cheaper to run.”
Chris V. Nicholson, an investor at Page One Ventures, pointed out
“The number of companies who have six million dollars to spend is vastly greater than the number who have one hundred million or one billion.”
Jeffrey Ding, a professor at George Washington University, added
“The chip shortage forced engineers to train the model more efficiently so it could still be competitive.”
These voices highlight something important. DeepSeek V3 is not trying to outsmart every model in the world. It is focused on being fast, affordable, and useful, and that is exactly why it is making waves.
How DeepSeek V3 Was Built Using Fewer Chips?
DeepSeek V3 stands out because it did something almost no one thought possible. It delivered powerful AI performance using only around 2,000 Nvidia chips. Other companies like OpenAI used as many as 16,000.
But how did they manage that?
The key lies in how they trained their model. Instead of relying on massive hardware, DeepSeek focused on efficiency. They used smaller-scale but smarter training methods, optimized their data pipelines, and reused open-source tools that cut down processing load.
Chip Shortages in China and the Nvidia Challenge
China has been facing strict rules from the United States that limit access to high-end AI chips. These Nvidia chips are usually what fuel big AI models. DeepSeek had to work with fewer chips, and this limitation pushed them to innovate.
So instead of scaling up like Silicon Valley giants, they scaled smart.
Why DeepSeek Only Used 2,000 Chips Instead of 16,000?
DeepSeek’s engineers adjusted the training process. They minimized the number of training runs, used pre-trained components from other open-source models, and fine-tuned only the most important parts of the AI. By doing this, they reduced the overall demand for chip power.
This approach made their model cheaper and faster to build without giving up much on performance. That is a big win for smaller teams who want to build strong AI models without needing a supercomputer.
What DeepSeek V3 Can Do Even Without Advanced Reasoning?
DeepSeek V3 may not be a reasoning genius like some of the latest models from OpenAI or Google, but it still gets the job done. In fact, it surprised everyone by performing really well on benchmark tests that are commonly used to check how smart a chatbot is.
What Benchmark Tests Show
When tested on standard tasks like answering questions, solving logic puzzles, and even writing basic computer programs, DeepSeek V3 held its own. It gave results that matched or came very close to what top models like GPT or Gemini delivered.
These tests are not about deep reasoning or solving long word problems. They are more about accuracy, speed, and how well the model understands simple instructions. And DeepSeek V3 proved that it can compete in that space easily.
Tasks It Handles Well Despite Not Being a Reasoning Model
DeepSeek V3 may not dig into complex reasoning, but it shines in everyday AI jobs. Here are some things it does really well
- Answering straightforward questions
- Writing content like blog posts or summaries.
- Translating languages accurately.
- Creating clean, basic code for developers.
- Handling educational queries like math or science explanations.
That makes it perfect for people who do not need an AI that can debate philosophy or solve riddles. It is fast, clean, and practical. For many users, that is exactly what they want.
How DeepSeek V3 Was Trained with Just 6 Million Dollars?
Most powerful AI models need massive amounts of computer chips and expensive hardware to learn. DeepSeek V3 did not.
Instead, it was trained using only 6 million dollars worth of computing power. That means fewer chips, less energy, and much smaller infrastructure. The team used clever training tricks, reused open-source tools, and skipped high-cost features like deep reasoning.
The result? A fast and reliable model that works well without needing the budget of a tech giant.
What This Means for Smaller AI Teams and Startups
This changes everything. DeepSeek V3 shows that you do not need a billion-dollar lab to build useful AI. Smaller companies, university researchers, or new startups can now think big without needing big money.
It proves that with smart planning and efficient tools, building a high-performing AI is possible even on a tighter budget.
Who Helped Build DeepSeek V3 and What Makes Their Approach So Unique?
So far, we have talked about how DeepSeek V3 is fast, budget-friendly, and does not need a mountain of computer chips. But here is something you might not expect.
The team that built it did not start out as AI experts.
From Stock Trading to AI Building
DeepSeek is backed by a company called High Flyer. And before diving into AI, High Flyer was busy doing something totally different. Stock trading. They used smart algorithms to make fast financial decisions and earned enough profits to invest in bigger tech ideas.
Instead of following the usual tech startup path, they took their trading success and used it to build something new. That is how DeepSeek was born. And with smart planning, they were already gathering Nvidia chips way before the chip shortage made headlines.
The Surprising Mix of People Behind the Model
Here is where it gets even more interesting. DeepSeek does not just hire computer science graduates or AI engineers. They bring in poets, language experts, teachers, and other creative minds too.
Why? Because building a great AI is not just about code. It is also about teaching the model how people think, speak, and express ideas.
For example, some team members helped train DeepSeek to understand Chinese poetry and answer tricky college exam questions. That kind of human touch is what makes DeepSeek V3 more relatable and useful.
So while the tools were efficient and the budget was small, it is the team’s unique mix of skills that gave DeepSeek V3 its real power.
How DeepSeek V3 0324 Compares to Other Popular AI Models?
Now that you know what DeepSeek V3 0324 is capable of, you might be wondering how it stacks up next to big names like OpenAI’s GPT-4, Google’s Gemini, and Anthropic’s Claude.
Here’s a quick look at how they compare in terms of cost, performance, and availability:
| Feature | DeepSeek V1 | DeepSeek V2 | DeepSeek V3 | DeepSeek V3 0324 | GPT-4 (OpenAI) | Gemini (Google) | Claude (Anthropic) |
| Reasoning Capability | Basic logic | Improved logic | Basic logic | Better reasoning | Advanced reasoning | Strong reasoning | High-level reasoning |
| Chips Used (Nvidia GPUs) | Unknown | ~1,000 | ~2,000 | ~2,000 (optimized use) | Over 10,000 | Not disclosed | Estimated over 10,000 |
| Open Source | No | Partial (code models) | Yes | Yes | No | No | No |
| Performance in Coding | Basic | Moderate | Good | Improved (Python, logic) | Excellent | Good | Good |
| Cost to Use | Free (limited access) | Free (low compute cost) | Free via Hugging Face | Free and open (HF & GitHub) | $20/month (ChatGPT Plus) | Included with Google One plans | Free & paid options (Claude Pro: $20/month) |
As you can see in the comparison table above, DeepSeek V3 was a big upgrade from earlier versions. It used 671 billion parameters and smart training methods to stay fast and efficient. While staying fully open source, it delivered performance close to top paid models like GPT-4.
Now, DeepSeek V3 0324 builds on that progress and brings even smarter, faster, and more stable performance.
Here are the notable improvements in DeepSeek V3 0324 compared to its predecessor, DeepSeek V3.
Note: DeepSeek V3 0324 recently made headlines by surpassing Claude 3.7 Sonnet on LiveBench, becoming the second highest-ranked non-reasoning model after GPT-4.5 Preview.
Redditors are impressed with its benchmark position, especially given its open-source nature and low cost.
However, some users also raised concerns about hallucinations and slower speeds compared to R1. This mix of praise and criticism shows why real-world testing still matters more than just leaderboard ranks.
Editor’s Note: While benchmarks like LiveBench offer a quick snapshot of performance, I believe DeepSeek’s true value will come from how well it performs across everyday tasks.
If future updates can fix hallucinations and speed issues, this model could genuinely redefine what open-source AI is capable of.
What Are Reddit Users Saying About DeepSeek V3?
Reddit users have been talking a lot about the new DeepSeek V3 update. They’ve tested it, compared it with other AI models, and shared their honest opinions. Here’s a simple breakdown of what people are saying:
How Fast Is DeepSeek V3?
One user tested DeepSeek V3 on a high-end Apple computer (Mac Studio). Here’s what they found:
- It works really fast with small tasks, but slows down when you give it long questions (called “prompts”)
- It can use a lot of memory, even more than 400 GB in some cases. That means it needs a powerful computer to run smoothly
Some people also mentioned that their computers became hot while using it. This means the model uses a lot of power.
Is a Bigger, Better Model Coming Soon?
Many users believe DeepSeek is preparing to launch something called “R2,” which is an improved version of the current model.
Some clues include this:
- In the past, they released a similar model called R1 just a few weeks after an earlier DeepSeek V3 version
- R2 might be even better at solving hard questions and tasks
Users are excited and believe R2 might come out in April before other big AI models are launched by companies like OpenAI.
Why Is Everyone Talking About Open Source?
DeepSeek shares its AI models with the public. This is called “open source.”
People like this because:
- Anyone can use the model for free or at a low cost
- It helps smaller companies and developers build their own tools
- It gives more control to users instead of big tech companies
One Reddit user said that DeepSeek is giving people power that companies like OpenAI want to keep for themselves.
Will DeepSeek Add Voice or Image Features?
Right now, DeepSeek only works with text. But people are hoping they’ll add more features soon, like being able to talk to the AI or show it pictures.
Some say this is necessary to compete with top models like ChatGPT or Google’s Gemini.
Others believe it’s okay if DeepSeek only focuses on getting better at answering questions and solving problems.
Does DeepSeek Still Feel Like a “Friendly” AI?
- Some long-time users feel this new version of DeepSeek V3 sounds more serious and robotic.
- One user shared that it used to feel like a chill friend. Now it feels more like a smart teacher or professor.
Not everyone minds this change, but it shows how different versions can feel different even if they’re technically smarter.
Some Redditors are questioning if DeepSeek really lacks reasoning, especially when it replies with phrases like “wait.” One user said, “Why would they lie about it being non-reasoning? It said ‘wait’ so it has reasoning? This makes no sense.”
Another hot topic is that DeepSeek V3 0324 is now the highest scoring non-reasoning model, making it the first open weights model to lead this category and a major win for open source, as highlighted by Artificial Analysis.
What I Think: After reading the Reddit discussions, I think DeepSeek V3 has clearly impressed the community with its performance and open-source spirit. If R2 delivers as expected, it might just give big players like OpenAI serious competition.
The Future of DeepSeek V3 0324: What’s Coming Next?
The future looks bright for DeepSeek V3 0324. With its major upgrades and open-source design, it’s quickly becoming one of the most exciting models in the AI world.
It packs 685 billion parameters and handles up to 128,000 tokens at once, showing big improvements in reasoning, coding, and math. That puts it right up there with top models from OpenAI, Anthropic, and Google.
Here’s what’s shaping its future:
- Open-Source Accessibility: DeepSeek V3 0324 is available on platforms like Hugging Face, which means anyone can access it for free. This open-source approach gives more developers and businesses the chance to build, test, and customize AI for all kinds of uses. It could help push AI forward in areas like healthcare, education, and finance.
- Performance and Efficiency: Thanks to features like Multi-head Latent Attention (MLA) and Mixture of Experts (MoE), the model runs faster and more efficiently. It can predict multiple tokens at once, which makes it great for real-time tools like chatbots and automated support.
- Competitive Landscape: DeepSeek is rising fast and giving big tech companies some real competition. By offering a powerful and affordable alternative to paid models, it might shake up the AI market and push everyone to innovate more while lowering costs.
- Global Impact: Because it’s free and easy to use, DeepSeek V3 0324 makes advanced AI available to people around the world, even in places with fewer resources. This could open doors to new ideas and tech that help solve real-world problems everywhere.
FAQs
Is DeepSeek V3-0324 free?
Can DeepSeek V3 be run locally?
What is DeepSeek R1 and V3?
When was DeepSeek V3 released?
Conclusion
DeepSeek V3-0324 didn’t just show up; it showed off. Who would have thought an open-source model trained on a budget could outshine the big players? It’s fast, flexible, and clearly not here to play nice.
And guess what? If this is what V3-0324 can do, I can’t wait to see what DeepSeek drops next. So stick with me; I’ll be breaking down more wild AI updates you won’t want to miss!
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