Can an AI model built on a fraction of the budget compete with systems that cost billions? That's the question DeepSeek has forced the AI industry to confront. DeepSeek has raised interest among businesses looking for cost efficiency, self-hosted AI solutions, and better data privacy. At the same time, concerns about security, compliance, and intellectual property (IP) disputes have made decision-makers cautious.
For enterprises seeking Generative AI consulting to evaluate DeepSeek, the question is simple: Does DeepSeek make sense for business use? Can it match the performance of closed-source models? What are the trade-offs in cost, security, and long-term viability? And should companies be concerned about their compliance and regulatory risks? In this blog post, you'll find DeepSeek explained-from its training methodology to how it works and what enterprises need to consider before adopting it.
What is DeepSeek R1?
DeepSeek R1 is an open-source AI model with a mixture-of experts (MoE) architecture. It optimizes compute efficiency by activating only relevant subnetworks during inference. Unlike monolithic transformer models, it reduces computational overhead while maintaining strong performance in reasoning, structured data processing, and multimodal tasks.
DeepSeek's open-source LLM optimizes inference by selectively activating only the necessary model components for each query, minimizing computational overhead. The main advantage of the MoE architecture is that it requires less processing power for inference, particularly for CPU-based operations. While this does not remove the memory requirements entirely, the architecture significantly reduces the need for high-end GPUs.
For real-time applications, hosting the model on GPUs still requires large clusters with sufficient VRAM-DeepSeek, for instance, operates optimally with around 300 H100 GPUs. However, for batch processing, MoE architecture is much more viable on CPUs than dense models, offering a significant cost advantage. Overall, the main selling point of MoE is its ability to accelerate inference on CPUs while still benefiting from GPU clusters for real-time processing: so here's DeepSeek R1 explained in detail.
DeepSeek's efficiency does not stem simply from reducing reliance on advanced GPUs-it results from multiple architectural and training optimizations. To lower training costs, DeepSeek employed FP8 precision, wrote custom PTX code to enhance hardware utilization, and implemented a modular training pipeline for improved resource efficiency. Additionally, its approach incorporates reinforcement learning without human supervision, multi-token prediction, and auxiliary loss-free load balancing, contributing to performance gains while minimizing computational overhead.
Open access allows researchers to evaluate its architecture, test efficiency, and verify capabilities. These optimizations lower entry barriers for AI research, enabling broader experimentation and deployment of large language models at reduced costs. But what enables DeepSeek to achieve this balance between performance and resource optimization?
How DeepSeek architecture optimizes computational performance
DeepSeek's architecture is built on a mixture of experts framework, which activates only the necessary parts of the model during inference, reducing computational overhead. This dynamic allocation of computational resources reduces power consumption while maintaining high levels of reasoning and structured data processing. Unlike traditional AI models, which engage their entire neural network for every query, MoE selectively utilizes specialized subnetworks based on the nature of the task.
The model also integrates Multi-Head Latent Attention (MLA) to enhance information retention while reducing the memory footprint for efficient inference. DeepSeek employs FP8 mixed-precision training to optimize resource utilization further, lowering memory and compute requirements without sacrificing accuracy. Chain-of-thought (CoT) reasoning enables the model to break down complex problems into logical steps, improving accuracy in logic, mathematics, and programming tasks. Additionally, custom load-balancing kernels improve GPU utilization and mitigate network congestion during large-scale training and inference. These architectural choices, including their optimizations in load balancing, are tailored to the specific architecture they use and may not generalize well across different enterprise hardware environments.
The capabilities of DeepSeek explained: What makes it different
While many AI companies invest heavily in cloud-based infrastructure and expensive GPUs, DeepSeek has taken a different approach. It leverages algorithmic efficiency, open-source accessibility, and strategic infrastructure management to develop competitive AI solutions. With these capabilities in mind, let's dive deeper into DeepSeek to uncover how it helps to achieve:
1. Cost-efficient AI development
DeepSeek has significantly reduced the cost of training large language models. R1 was reportedly trained for $5.6M [1], starkly contrasting OpenAI's estimated $100M-$B billion training expenses [2]. However, this figure only reflects the direct training cost and does not account for the full model development expenses, including data curation, infrastructure, and pretraining iterations. Additionally, DeepSeek leveraged v3 models during development, which adds to the overall investment.
Despite these warnings, DeepSeek's ability to build an OpenAI-like model with remarkable efficiency has prompted major AI players to reassess their strategies. Its cost-effectiveness has led enterprises to question whether expensive proprietary models are necessary when open-source alternatives can provide comparable performance at a fraction of the cost.
2. Open-source and self-hosting capabilities
DeepSeek is fully open-source, allowing enterprises to download, modify, and deploy the model on private infrastructure. DeepSeek models can run the model on-premises or within controlled cloud environments. Unlike API-based AI services that require continuous external data exchanges, self-hosting DeepSeek eliminates third-party data exposure risks.
3. Independence from cloud providers
Unlike most AI startups that rely on AWS, Microsoft Azure, or Google Cloud services, DeepSeek operates its dedicated data centers. AI model allows for:
- Full control over AI model training without reliance on third-party cloud providers;
- Faster iteration cycles, as DeepSeek can optimize its AI models without being constrained by external infrastructure;
- Lower long-term operational costs, as in-house data centers reduce dependency on cloud pricing models.
4. Model accessibility
Its modular architecture allows for scalability across different deployment environments, including on-premises and hybrid cloud. However, despite its efficiency improvements, DeepSeek's models are not suited for true edge AI inference due to their computational demands.
Beyond its flagship 600B-parameter model, DeepSeek has developed distilled versions, ranging from 1.5B to 70B parameters [3], to provide lighter, more efficient AI solutions. These distilled models, however, are built using other open-source architectures like Qwen and LLaMA rather than directly inheriting all of DeepSeek-R1's capabilities. While they offer more deployment flexibility, their performance does not match the flagship model's reasoning and comprehension strengths. Businesses considering these alternatives should evaluate their trade-offs in accuracy and efficiency.
What are the DeepSeek training methods?
At the core of DeepSeek's training pipeline is a combination of reinforcement learning, multihead latent attention, and a mixture of expert architecture. This dynamic allocation reduces unnecessary processing and allows DeepSeek's models to function on lower-tier GPUs without significant performance degradation.
Does AI performance depend more on software optimization or hardware?
DeepSeek disclosed that it trained its models on a mix of NVIDIA H800 and H100 GPUs [1], accessible in China before the United States expanded export restrictions in October 2023. While the H800 is a less advanced variant of NVIDIA's high-performance H100, DeepSeek optimized performance by writing custom PTX code to mitigate the hardware limitations. This allowed it to achieve strong results despite the constraints.
However, DeepSeek still relied on massive GPU power, and its training and fine-tuning remained highly expensive. While software optimizations played a role in maximizing efficiency, the fundamental paradigm of large-scale AI training-requiring extensive compute resources-remains unchanged. The claim that DeepSeek succeeded under hardware limitations should be viewed in the context of its extensive GPU usage rather than as a radical departure from standard AI training practices.
What data powers DeepSeek?
The data used for training remains a point of scrutiny. Unlike large AI models that rely on massive internet-scale datasets, DeepSeek employs a structured data curation strategy. The company has disclosed that it combines synthetic data generation with curated real-world datasets to build its training corpus. This approach reduces reliance on indiscriminately scraped web data, which can introduce biases, inconsistencies, and potential legal risks related to proprietary content. However, there is strong speculation that DeepSeek also distilled knowledge from OpenAI's models via API calls, which has raised legal and ethical debates in the AI industry.
While synthetic data allows DeepSeek to retrain its models with controlled, high-quality annotations, the extent to which proprietary model outputs contributed to its training remains unclear.
Does DeepSeek's use of distillation raise legal and ethical concerns in AI model development?
The most controversial aspect of DeepSeek's training has been the question of distillation-a widely used technique in Machine Learning where a smaller model is trained using outputs from a larger, more capable model. OpenAI and Microsoft have claimed that DeepSeek may have employed distillation techniques using OpenAI's outputs, which would violate OpenAI's terms of service if done without authorization.
While distillation is a standard industry practice for improving model efficiency, the broader debate revolves around the selective enforcement of data usage policies. Leading AI companies themselves have faced scrutiny for training on copyrighted materials and potentially unauthorized client data. The concerns raised by OpenAI and Microsoft may also reflect an attempt to shape public opinion and regulatory frameworks in ways that disadvantage open-source competition. The lack of transparency regarding DeepSeek's training data leaves this issue unresolved, but it underscores the wider tensions in AI ethics and competitive strategy.
Given the ongoing debate about AI intellectual property and regulatory oversight, the question remains: How will enterprises balance adopting cost-efficient, open-source AI models like DeepSeek with ensuring compliance with evolving legal and ethical standards in AI development?
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DeepSeek explained: Breaking down its concerns
As enterprises evaluate the potential of DeepSeek for AI integration, several concerns have emerged, ranging from transparency in training costs to privacy, accuracy, and open-source implications. Addressing these concerns requires an objective analysis of the available data and expertise in AI implementation. As an experienced GenAI service provider, N-iX offers deep technical insights and practical solutions for organizations considering DeepSeek and alternative open-source generative AI solutions.
Claim 1: The actual cost of training DeepSeek
DeepSeek has claimed that its R1 model was trained for approximately $5.6M, a figure that, at first glance, appears to undercut the $100M to $1B training budgets of proprietary AI models like OpenAI o1 and o3. However, this figure does not include several crucial costs. Unlike its previous model, V3, for which DeepSeek disclosed training expenses, R1's total cost remains undisclosed. This raises questions about whether the $5.6M figure reflects only the marginal compute cost and excludes research, data curation, infrastructure, and personnel investments.
Recent industry analyses suggest that DeepSeek operates a far more extensive AI infrastructure than publicly acknowledged. Reports indicate that DeepSeek has access to approximately 50,000 NVIDIA GPUs, including H800s, H100s, and H20s, with a total capital expenditure on servers estimated at $1.6B [4]. While its MoE architecture, MLA, and custom load-balancing kernels contribute to efficiency, they do not fully explain how such a large-scale model was trained at a fraction of the cost of its competitors.
While DeepSeek's software innovations undoubtedly reduce GPU load, training an advanced AI system requires substantial data acquisition, fine-tuning, and infrastructure investments. Additionally, the costs of running inference at scale-including cloud infrastructure, power consumption, and ongoing model updates-are not accounted for in DeepSeek's publicized training expenditure. DeepSeek's reported training cost is incomplete and does not include research, infrastructure, or long-term operational expenses. The cost of developing R1 is likely much higher, given DeepSeek's vast GPU resources and the broader financial commitments required for model training and deployment.
The key takeaway for enterprises evaluating AI adoption strategies is clear: AI cost-efficiency depends on more than raw training expenses. It requires an understanding of infrastructure investments, software optimizations, and long-term operational costs. While DeepSeek has made strides in efficiency, the narrative of ultra-low-cost AI training needs to be evaluated with realistic expectations. N-iX understands the complexities of AI. Our expertise in optimizing AI infrastructure, fine-tuning models for enterprise applications, and ensuring cost-effective deployment allows businesses to adopt AI strategically without hidden inefficiencies.
Claim 2: Security considerations
Privacy concerns surrounding DeepSeek stem from its data collection practices and Chinese ownership, raising questions about data security, regulatory compliance, and potential government access. The company's privacy policy states that it collects user information, including chat history, text and audio inputs, device specifications, and IP addresses, with data stored on servers in China. Given China's national intelligence laws, which require businesses to cooperate with government data requests, enterprises must assess the risks of using DeepSeek, especially in regulated industries.
Security vulnerabilities have also been identified. In December 2024, a prompt injection exploit allowed potential account hijackings before being patched. In January 2025, DeepSeek suffered a large-scale cyberattack [5], temporarily limiting new user registrations while mitigating service disruptions. While these issues were resolved, they raised concerns about the model's resilience against security threats. DeepSeek-R1 is also highly prompt-sensitive and lacks major built-in guardrails, making it risky to deploy in real-world, client-facing scenarios without robust safety measures. Its open nature makes it more suited for inter-company usage, where behavior-altering inputs can be restricted. The lack of transparency in its training data makes verifying potential censorship mechanisms or hidden biases difficult, posing risks for enterprise adoption.
One way to address these concerns is running DeepSeek locally rather than relying on cloud-based services, which can help reduce exposure to external servers. However, it's important to note that even when run locally, concerns about potential censorship and hidden biases may still persist and require careful monitoring.
Unlike proprietary models that require API access, DeepSeek's open-source nature allows self-hosting. Platforms like Ollama enable enterprises to deploy DeepSeek within their secure infrastructure, eliminating exposure to external servers. This system will give complete data control and reduce unauthorized access or transmission risks. Our AI engineers help enterprises deploy DeepSeek R-1 safely with additional security layers, including content filtering, access controls, and monitoring systems, to mitigate risks associated with its prompt sensitivity and lack of guardrails.
Claim 3: The accuracy responses of DeepSeek
Some evaluations suggest that DeepSeek's accuracy is lower than proprietary models. However, R1's performance can sometimes be inconsistent, which is not necessarily due to its mixture of experts (MoE) architecture. While MoE selectively activates relevant subnetworks instead of engaging all parameters for every query, this does not inherently impact accuracy.
In a dense transformer model, every query is processed using the complete parameter set, ensuring a consistent output pattern but demanding high computational resources. MoE, in contrast, dynamically routes tasks to specialized subnetworks based on the input type. For example, DeepSeek-R1's MoE implementation activates only 37B parameters simultaneously out of its total 671B-a drastic reduction in active computing while maintaining strong performance in reasoning and structured tasks. The result is a more resource-efficient and scalable model, though its performance can vary depending on optimization and fine-tuning.
Traditional AI benchmarks often favor dense transformer models that process every query using their full parameter set. MoE-based models scale differently, and while some, like WizardLM, have demonstrated performance beyond their parameter class, others, such as Grok-1, have underperformed. Variability in MoE models is influenced more by their training and tuning rather than an inherent lack of determinism. When optimized effectively, MoE models can achieve high performance in structured reasoning, long-context understanding, and domain-specific adaptation.
Claim 4: Replicability: An open-source alternative to DeepSeek-R1
DeepSeek-R1 is a large-scale open-weight language model developed to enhance structured reasoning, mathematical problem-solving, and logical inference. As an advancement over its predecessor, DeepSeek-V3, R1 incorporates improvements in reinforcement learning, reward-based optimization, and test-time computing. DeepSeek-R1 remains a transformer-based model but is trained to follow a step-by-step reasoning approach by default.
This training method encourages the model to evaluate different problem-solving strategies before arriving at a final answer, effectively biasing it towards generating longer outputs that allow for more structured reasoning, such as chain-of-thought or internal monologue-style processing. While conventional models can be prompted to behave similarly, DeepSeek-R1's training optimizes this reasoning process as a default behavior. To fully grasp DeepSeek R1 explained, this model mirrors human-like deliberative thinking, making it particularly strong in handling complex logic-based tasks.
Given the uncertainties surrounding DeepSeek-R1's training data and proprietary refinements, an independent initiative called Open-R1 has been formed to provide a fully open-source reproduction of the model. Hosted by Hugging Face, Open-R1 aims to replicate and validate the methodologies used in DeepSeek-R1. The Open-R1 project aims to:
- Recreate DeepSeek-R1's reasoning model by utilizing open-source datasets and reinforcement learning techniques.
- Improve transparency in AI training by documenting each stage of model development, including dataset selection, fine-tuning methodologies, and parameter adjustments.
- Ensure accessibility by providing a publicly available implementation that can be modified, fine-tuned, and self-hosted without reliance on external cloud infrastructure.
One of the key advantages of Open-R1 is that it removes the geopolitical and regulatory concerns surrounding DeepSeek's Chinese ownership. If enterprises hesitate to adopt AI models developed in regions with stringent data governance laws, Open-R1 provides an alternative that can be securely deployed in on-premise or private cloud environments. As part of its strategic initiatives, N-iX is exploring opportunities to support businesses adopting Open-R1 as a transparent and self-hosted AI solution.
Claim 5: Limited enterprise-grade support
While DeepSeek provides high-performance open-source models, it lacks the enterprise-grade support ecosystem that major AI providers like OpenAI, Google, or Microsoft offer. Without direct vendor support, companies relying on DeepSeek for mission-critical applications may face challenges in model fine-tuning, security updates, performance optimizations, and compliance management.
Unlike proprietary AI services that offer service-level agreements (SLAs), dedicated customer support, and infrastructure integrations, DeepSeek is open-source, which means enterprises must handle deployment, maintenance, and troubleshooting independently. N-iX offers expertise in deploying, fine-tuning, and securing open-source models for enterprise environments. Our team provides end-to-end support, including model optimization, security hardening, compliance alignment, and infrastructure scaling.
Wrapping up
In this blog post, we have DeepSeek explained from multiple angles, addressing its strengths, limitations, and impact on the AI landscape. While DeepSeek's efficiency gains are impressive, enterprises must weigh the trade-offs carefully, especially regarding data privacy, trust, and regulatory compliance. Open-source AI offers flexibility, but without the right expertise, it can introduce risks and inefficiencies. As AI adoption accelerates, the real advantage will not be just about cost-it will be about strategic and expert implementation.
With 22 years of experience and over 200 AI and ML specialists, we specialize in optimizing open-source AI models for enterprise use, ensuring they are fine-tuned, secure, and aligned with business objectives. Recognized as a rising star in data engineering by ISG, we bring a wealth of expertise to help you succeed. Our portfolio of over 60 Data Science and AI projects showcases our ability to deliver impactful, scalable AI systems across industries. Whether fine-tuning DeepSeek, mitigating risks, or implementing open-source alternatives, we make AI work for business without the guesswork. Let's build AI that works for you and only on your terms.
Reference
- DeepSeek-V3 Technical Report
- Artificial Intelligence Index Report 2024 - Stanford University
- DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
- DeepSeek Debates: Chinese Leadership On Cost, True Training Cost, Closed Model Margin Impacts - SemiAnalysis
- DeepSeek hit with large-scale cyberattack - CNBC
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