2025-01-31
We know the news around DeepSeek is changing fast and we will update this article periodically with the latest information and insights.
DeepSeek, a company with limited access to GPUs — operating under an embargo —has taken on the biggest players in AI and emerged as a serious contender with the recent releases of their V3 and R1 models. For a fraction of the cost and with far fewer resources, they’ve built a model that’s pushing the boundaries of what’s possible in AI.
But DeepSeek-R1 isn’t just a breakthrough. It’s a wake-up call for the entire AI industry. Forget the old narrative that you need massive infrastructure and billions in compute costs to make real progress. DeepSeek is rewriting the rules, proving that you don’t need massive data centers to create AI that rivals the giants like OpenAI, Meta and Anthropic.
Cheaper. Faster. Smarter. Has DeepSeek just changed the world of tech? It sure feels like it.
Breaking barriers with limited resources
When you think of SOTA (state of the art) AI models, you probably imagine massive clusters of GPUs burning through millions of dollars in electricity. DeepSeek-R1, however, tells a different story. With a budget of just $6 million, DeepSeek managed to build a model that’s within touching distance of the best reasoning models out there.
This isn’t just about saving money; it’s about making cutting-edge AI more accessible to everyone, regardless of their computing capacity. In a world where hardware limitations often dictate who gets to play the AI game, DeepSeek’s success proves that ingenuity can still win out.
The power of compounding research
What makes DeepSeek’s success most impressive is the research that underpins it. This isn’t a one-off breakthrough; it’s the result of years of hard work and cross pollination within the broader AI community.
DeepSeek’s approach integrates key advancements in AI: reinforcement learning, Mixture of Experts (MoE), and Chain of Thought (CoT) prompting, along with test-time computation. It also builds on established training policy research, such as Proximal Policy Optimization (PPO) and Direct Preference Optimization (DPO), to develop Group Relative Policy Optimization (GRPO) — the latest breakthrough in reinforcement learning algorithms for training large language models (LLMs). GRPO is the crucial component which enabled the model to learn self-verification and search in the pre-training phase.
While many of these concepts aren’t new on their own, what DeepSeek has done is consolidate and build on these innovations in a way that unlocks immense efficiency, even going as far as to write their own PTX code, bypassing NVIDIA’s CUDA to optimize every part of process for their model training. And because they’ve open-sourced the entire architecture, this foundational work is now available for others to build on. It’s not just a model; it’s a welcome mat for the next wave of AI development.
Open science, open source
By making their models and methodology available for all, DeepSeek has opened the door for anyone — from researchers to startups — to take these tools and improve on them. This move echoes what platforms like HuggingFace have done in making models more accessible and helps to create a community-driven pipeline that accelerates development and speed of iteration. And while companies like OpenAI and Meta will be paying close attention to DeepSeek’s success, and benefitting from it, this is a rising tide of science that will lift all ships.
Enterprise impact and strategic considerations
DeepSeek has demonstrated that high-performance AI is no longer limited to those with the largest budgets. For companies, it could be time to rethink AI infrastructure costs, vendor relationships and deployment strategies. By adopting more efficient models, organizations can improve their bottom line and accelerate innovation, all while reducing dependency on expensive, massive computer systems and vendor lock in.
As AI becomes more accessible, the competitive landscape will change, and enterprises must focus on how they apply AI, not just on having access to it.
Long-term, the focus will shift from raw computational power to the ability to execute AI effectively, making it a key competitive differentiator. The businesses that adapt will be those who think creatively, reduce operational and process barriers to deployment and foster a culture that embraces these shifts.
It also reintroduces the potential for more personalized AI. Small Language Models have been part of the conversation for a long time due to their potential to be trained efficiently on smaller datasets, hosted at lower costs and tailored to specific use cases. The next step in this AI revolution could combine the sheer power of large SOTA models with the ability to be fine-tuned or retrained for specific applications in a price efficient way. This creates a competitive advantage not through access to technology but through the tailored use of vertically aligned datasets and the creativity of application.
Navigating constant change
At Valtech, we combine deep AI expertise with bespoke, strategic approaches and best in class, multi-model frameworks that help enterprises unlock value, no matter how quickly the world changes. Ready to turn your AI potential into action? Connect with us to get started.
The UNLOCK
Insights that drive action
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Forget deep pockets — win with innovation: The AI race isn’t just about big budgets. Companies that focus on creative problem-solving and resource optimization can punch above their weight.
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Ride the waves of transformation: Change isn’t a slow crawl; it hits in massive, disruptive waves. A flexible, curious attitude to help enterprises stay ahead and turn upheaval into opportunity.
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Break free from AI bottlenecks: Add value, accelerate innovation and escape vendor lock-in by adopting smarter, flexible multi-model frameworks. The future belongs to those who rethink infrastructure and scale AI on their own terms.
