DeepSeek Demystified: How This Open-Source Chatbot Outpaced Industry Giants
DeepSeek’s website tagline “Into the unknown” hints at its ambition to push the boundaries of AI.
DeepSeek is an open-source AI chatbot and large language model (LLM) that has rapidly emerged as a serious contender to industry-leading systems like OpenAI’s ChatGPT and Google’s Gemini. Launched in January 2025 by a Hangzhou-based startup, DeepSeek quickly rattled the global AI industry by matching the performance of top Western models at a fraction of the cost. In fact, its reasoning capabilities rival OpenAI’s GPT-4 (ChatGPT’s underlying model) while being trained for tens of millions of dollars less. Released under an MIT open-source license, DeepSeek’s model weights are freely available, enabling researchers and developers to use and fine-tune the model without restriction. This open approach—unusual in a field dominated by closed proprietary systems—helped DeepSeek’s chatbot soar to the #1 spot on app stores (briefly overtaking ChatGPT) and amass millions of users within weeks of launch. In this article, we’ll demystify DeepSeek: exploring how it works, what sets it apart from giants like ChatGPT and Google’s Gemini, and how you can implement it for your own projects.
How DeepSeek Works
Model Architecture – Mixture-of-Experts (MoE):
At the core of DeepSeek’s success is an innovative mixture-of-experts architecture. Instead of a single colossal neural network, DeepSeek-R1 is built from many smaller expert models. It packs an astounding 671 billion parameters in total, but only ~37 billion parameters are active for any given input token. In practice, this means multiple expert networks specialize in different tasks, and only the most relevant “experts” are engaged for a query. This MoE design drastically cuts computation without sacrificing accuracy – achieving the power of a huge model at the cost of a much smaller one.
By comparison, OpenAI’s GPT-4 and Google’s Gemini are believed to use dense Transformer architectures that activate all model weights for each prompt, making them more computationally heavy. DeepSeek’s efficient architecture is further enhanced by Multi-Head Latent Attention (MLA) and a new Native Sparse Attention (NSA) mechanism that together enable an unprecedented 128K token context window. In other words, DeepSeek can handle extremely long prompts or documents (up to 128,000 tokens) and still retain relevant information, a context length several times larger than what GPT-4 (8K–32K) or most models can handle. This makes DeepSeek especially effective for long-form analysis, big code files, or multi-document queries.
Training Methodology – Large-Scale RL & Fine-Tuning:
DeepSeek’s training pipeline also breaks new ground. While it was initially pre-trained on a massive 14.8 trillion token corpus (to learn general language patterns), the real magic came from its post-training process focused on reasoning. The developers applied reinforcement learning (RL) at scale to “teach” the model how to think step-by-step and solve complex problems – without relying solely on supervised fine-tuning. In fact, the first version “R1-Zero” was trained via RL directly on the base model with no human-labeled examples, and it organically developed behaviors like self-checking answers and generating longer chain-of-thought explanations. DeepSeek then introduced a multi-stage training for the final R1 model, including a “cold start” phase with curated reasoning data to improve output clarity.
This was followed by iterative cycles of RL (to boost reasoning and align with human preferences) and supervised fine-tuning (to correct errors and solidify skills). Through these stages, DeepSeek-R1 learned to break down complex tasks into steps, verify its answers, and even explain its thought process. This is different from ChatGPT’s training, which uses Reinforcement Learning from Human Feedback (RLHF) mainly to make responses polite and safe – DeepSeek’s RL was aimed specifically at incentivizing deep reasoning ability. The result is a model that not only gives answers, but can articulate how it arrived at them (a level of transparency most others lack).
Key Differentiators (vs. ChatGPT & Gemini):
Several factors make DeepSeek stand out from its high-profile competitors:
• Open-Source Accessibility: Unlike OpenAI’s and Google’s models, DeepSeek’s weights are open and licensed for free use. Developers can inspect the model’s internals, host it on their own hardware, and fine-tune it for custom needs without restrictive licenses. This openness has fostered a growing community driving improvements, whereas ChatGPT/Gemini remain black boxes.
• Cost and Efficiency: DeepSeek was trained with remarkably low compute cost relative to its performance. According to its researchers, the flagship R1 model was trained for only about $5.6 million using 2,048 Nvidia H800 GPUs. By contrast, GPT-4’s training is estimated to have cost well over $100 million and required thousands of top-of-the-line H100 GPUs. The efficiency gains come from design choices like MoE (sparsely activating parameters), 8-bit floating point training for faster computation, and near-perfect parallelization across GPU clusters.
This efficiency not only cut costs by an order of magnitude, it also means cheaper runtime and scaling for end-users. For example, DeepSeek can match or exceed GPT-4 level performance using cheaper hardware. Its developers even managed to train it under U.S. export restrictions by using less powerful chips (the H800 is a constrained version of Nvidia’s H100). The net effect is that AI research and deployment are no longer exclusive to tech giants with unlimited budgets – DeepSeek proved cutting-edge results are achievable with more modest resources through smart engineering.
• Long-Context and Reasoning Prowess: Thanks to innovations like the NSA algorithm, DeepSeek handles very long inputs and complex multi-step problems better than most. It excels at logical reasoning, math, and coding tasks, often requiring it to “think” through many steps. Google’s Gemini is expected to be multimodal (handling images/text) and highly integrated with search, but details are scarce – its early versions (as seen in Google’s Bard updates) focus on broad knowledge and conversational finesse. ChatGPT (GPT-4) is a strong generalist but constrained by smaller context windows and closed training methods.
DeepSeek’s focus on stepwise reasoning gives it an edge in any task that demands careful, structured thought (e.g. solving a math problem, writing complex code, or analyzing lengthy documents). Independent evaluations noted DeepSeek-R1 outperforming GPT-4/Gemini on coding challenges and math competitions, even if its general English prose might be slightly less polished by default. Moreover, R1 is designed to explain its reasoning, which is valuable for users wanting insight into the solution process.
• Alignment and Domain Expertise: DeepSeek’s training incorporated diverse knowledge (it even hired experts outside computer science to enrich the model ), giving it a broad skillset from writing and role-play to factual Q&A. However, as a Chinese-developed model, it was also aligned with that government’s content regulations – meaning it may refuse queries on certain sensitive topics. ChatGPT and Gemini also have moderation filters, but tuned to different norms. For developers, this means DeepSeek might require adjustments depending on the deployment context (for example, the open model can be further fine-tuned to relax or change certain response behaviors as needed). Overall, DeepSeek’s open training methods have unlocked capabilities similar to top models while providing far more flexibility to adapt and improve the model. Next, we’ll see how these design choices translate into real-world performance numbers.
Technical Breakdown & Comparison
Performance Benchmarks:
On standard NLP and reasoning benchmarks, DeepSeek-R1 stands toe-to-toe with the best models in the world. Internal evaluations and third-party tests show R1 matching or exceeding OpenAI’s GPT-4 and other competitors on a wide array of tasks. For instance, on coding challenges like HumanEval (pass@1 code accuracy) DeepSeek’s base model achieved 65.2%, significantly outperforming Meta’s LLaMA 3 (54.9%) and even beating OpenAI’s text-davinci in some cases. It also set new state-of-the-art scores in math competitions – scoring 79.8% on the AIME 2024 exam, which is well above what other models of any size have achieved.
DeepSeek particularly shines in STEM fields (math, coding, logical reasoning) where its chain-of-thought training pays off. Evaluators noted it solved complex problems with steps and justifications, often outperforming ChatGPT and Claude on discrete reasoning tasks. On general knowledge and language understanding (e.g. the MMLU academic benchmark), DeepSeek is in the same elite tier as GPT-4, scoring in the high 80s on accuracy One area where GPT-4/Gemini still have an edge is fluent English prose and open-ended creativity – DeepSeek’s English writing is strong but occasionally less nuanced, partly due to its focus on factual and structured responses.
Nonetheless, it handled Chinese language exams better than any non-Chinese model (surpassing even Alibaba’s Qwen 2.5 in several Chinese tests) , and it demonstrated superior ability to handle very long documents without losing context (thanks to that 128K token window, which far outstrips most rivals). Another unique aspect is explainability: DeepSeek can output a self-explanation of its reasoning process on demand, which other proprietary models typically cannot do. This makes it attractive for high-stakes applications where understanding why the AI gave an answer is important.
Training Data & Strategies:
DeepSeek was trained on a massive and diverse dataset of 14.8 trillion tokens, covering multiple languages and domains. This scale of pretraining data is on par with or larger than what most industry labs use (for comparison, LLaMA 2 was trained on ~2T tokens). The diversity and size of data gave the model a strong foundation. But what truly differentiates DeepSeek is how it was fine-tuned for reasoning using RL. The team’s 22-page research paper details how they iteratively improved the model’s logical reasoning by letting it generate solutions and then rewarding or refining them.
They even employed rejection sampling (having the model produce multiple answers and picking the best) and additional supervised tuning on those high-quality outputs. This strategy is akin to training a student by letting them attempt problems and then learn from the best attempt – it unlocked “meta-cognitive” skills in the model like checking its work and taking more “thinking steps” for hard questions. Notably, all these advanced training techniques were conducted at a relatively low cost by leveraging efficient parallelism and 8-bit precision training. DeepSeek’s entire V3 base model (on which R1 is built) was trained with only 2.78 million GPU-hours on H800 chips. By contrast, GPT-4 likely used tens of millions of A100/H100 GPU-hours (an order of magnitude more expensive per hour).
The DeepSeek team achieved this efficiency through engineering optimizations: they overlapped communication and computation across nodes to keep GPUs busy at all times and used custom load-balancing so that no expert model sat idle. The outcome is a model that is not just powerful, but also stable and reproducible – the training run had no crashes or irreversible loss spikes , which is impressive for such a large-scale run. In summary, DeepSeek’s training strategy combined sheer data scale with clever reinforcement learning and technical optimizations, resulting in a model that punches above its weight in performance.
Real-World Efficiency & Usability:
One of DeepSeek’s biggest selling points is its cost-effective deployment and flexibility in real-world use. Because the model is open-source, companies and developers can deploy it on their own servers or cloud instances without paying usage fees to a provider. And since it’s optimized for efficiency, you don’t necessarily need the absolute latest hardware to run it. In China, DeepSeek’s own chatbot service reportedly reached 22 million daily active users within weeks, surpassing even popular platforms like Douban.
This kind of scale was possible because the model could be served on a network of accessible GPUs (including domestic hardware alternatives). For instance, DeepSeek supports running on consumer-grade GPU setups and even non-Nvidia hardware – the developers have provided support for AMD GPUs and Huawei Ascend NPUs in addition to Nvidia, so you’re not locked into one vendor. By contrast, something like GPT-4 is only accessible via OpenAI’s API (you cannot self-host it), and Google’s Gemini will likely be tied to Google’s cloud services. DeepSeek also comes in various model sizes, which means you can choose a smaller version if you need faster, lighter performance. The team distilled the R1 model’s knowledge into compact models ranging from 1.5B up to 70B parameters.
These distilled versions maintain strong reasoning ability (the 14B model even beat some 30B+ models on reasoning benchmarks ) and can run on a single GPU. This gives developers a lot of flexibility: for production workloads where latency and cost are critical, a 7B or 14B DeepSeek variant fine-tuned to your domain might be ideal. On the other hand, if you need maximum accuracy and have the resources, you can deploy the full 37B (per-token) expert model on a multi-GPU server for top-tier results. Another aspect of usability is integration – because DeepSeek is open, it’s already being integrated into various tools and platforms.
For example, there are community projects using DeepSeek as a backend for chat interfaces, and even data platforms like Snowflake demonstrated running DeepSeek for natural language queries. This vibrant ecosystem means you are likely to find existing libraries, Docker images, and community forums to help with any implementation questions. In short, DeepSeek has transformed cutting-edge NLP from a luxury into an accessible tool: many tasks that previously required a paid API or a large research budget can now be achieved with an open model that you control.
Implementation Guide
Ready to experiment with DeepSeek? In this section, we provide a practical guide to installing the model, fine-tuning it for your needs, and deploying it in production. Whether you want to run the full model or a smaller variant, the process is straightforward and well-documented by the DeepSeek community.
Installation and Setup
1. Environment Preparation:
DeepSeek runs on Linux with Python 3.10+ (Windows/Mac are not officially supported for the full model). Make sure you have access to a machine with a modern GPU (Nvidia GPUs with at least 40GB memory for the 37B model, or smaller GPUs for the distilled models). Install PyTorch (with CUDA support) and other dependencies. The DeepSeek team provides a requirements file for the full model. For example, to prepare the environment:
# Clone the DeepSeek repository
git clone https://github.com/deepseek-ai/DeepSeek-V3.git && cd DeepSeek-V3
# Install required libraries (Torch, Triton, Transformers, etc.)
pip install -r inference/requirements.txt
# (Optional) Create a new virtual env before installing to avoid conflicts2. Download Model Weights:
The next step is to obtain DeepSeek’s model weights. These are available on Hugging Face Hub for both the full MoE model and the distilled versions. For the full DeepSeek-V3 (R1 base) weights (~680B of data), you can download from the official HuggingFace repository. For example, download the DeepSeek-V3 weight files and place them in the DeepSeek-V3 directory on your machine . If you plan to use a smaller distilled model, you can instead download those checkpoints (for instance, deepseek-ai/DeepSeek-R1-Distill-Qwen-14B for the 14B version). Ensure you have enough disk space (the 14B model is on the order of tens of GB, while the full model is hundreds of GB).
3. Weight Conversion (for full model):
DeepSeek’s full model uses a custom format (FP8 weights split across experts). The repository includes conversion scripts to prepare these for inference. For example, after downloading weights, run:
# Navigate to the inference folder
cd inference
# Convert Hugging Face weights to DeepSeek’s inference format
python convert.py --hf-ckpt-path /path/to/DeepSeek-V3 --save-path /path/to/DeepSeek-V3-Demo \
--n-experts 256 --model-parallel 16This script will process the weights and output a ready-to-run model file. (The --n-experts 256 and --model-parallel 16 arguments correspond to DeepSeek-V3’s MoE configuration; you can adjust these based on your parallel GPUs if needed.)
4. Running Inference (local demo):
With everything set up, you can now launch the DeepSeek chatbot locally. The repository provides a generate.py script to run an interactive chat. For example:
# Launch DeepSeek interactive chat (example for 2 nodes with 8 GPUs each)
torchrun --nnodes 2 --nproc-per-node 8 --master-addr <MASTER_IP> --node-rank <RANK> \
generate.py --ckpt-path /path/to/DeepSeek-V3-Demo --config configs/config_671B.json \
--interactive --temperature 0.6 --max-new-tokens 200This command uses PyTorch’s distributed launcher to start the model across nodes/GPUs. Adjust --nnodes and --nproc-per-node according to your setup (for instance, --nnodes 1 --nproc-per-node 4 for a single machine with 4 GPUs). Once it launches, you should get a prompt where you can start entering queries and receive DeepSeek’s answers in real-time. If you don’t have multiple GPUs, you may try loading a distilled model instead, which can often run on a single high-memory GPU or even CPU (with slower inference). For instance, using the Hugging Face Transformers API to load a 7B or 14B model:
from transformers import AutoTokenizer, AutoModelForCausalLM
model_name = "deepseek-ai/DeepSeek-R1-Distill-Qwen-7B" # or 14B, etc.
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", trust_remote_code=True)
prompt = "Explain the significance of the number 42."
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=100)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)This Python example loads a smaller 7B DeepSeek model and generates a response to a sample question. The trust_remote_code=True flag allows loading any custom layers or tokenizers defined by DeepSeek (since the model is based on Qwen, it may use a special tokenizer). After a brief processing, the model will print a completion (in this case, likely an explanation of “42” in a Douglas Adams context). Even the distilled models support up to 32k or 128k context length, so you can test long prompts as well. Keep in mind that as of this writing, direct Hugging Face Transformer support for the 671B MoE model is not available, but the distilled models work as usual.
Fine-Tuning for Specific Use Cases
One huge advantage of DeepSeek’s open-source nature is that you can fine-tune it on your own data to specialize the model for specific tasks or industries. Fine-tuning the full 671B model would require enormous compute, but thankfully the authors have provided smaller “expert distilled” models (1.5B–70B) that are much more tractable to train. For most use cases, you can pick a model size that fits your hardware and then do either full fine-tuning or parameter-efficient tuning (like LoRA adapters).
Selecting a Model Size: If your use case is lightweight (e.g. a specific Q&A or classification task), a 7B or 14B model might be sufficient and can be fine-tuned on a single GPU. For more complex tasks (like writing code or multi-step reasoning in a domain), the 32B or 70B distilled models will give higher accuracy, at the cost of more VRAM and longer training time. For reference, the 14B model achieves state-of-the-art performance among models of its size on many reasoning benchmarks , so it’s a good balance of power and efficiency.
Data Preparation: Fine-tuning requires a dataset of input-output examples for your task. For instance, if you want to adapt DeepSeek as a legal assistant, you might prepare a set of legal questions and answers or document summaries. DeepSeek’s training used a JSON-based prompt format, but you can simply use plain text prompts with the model’s instruction style. A zero-shot prompt format (direct instructions without examples) tends to work best. If possible, include some chain-of-thought examples in the training data (where the answer is explained step-by-step) – this aligns well with DeepSeek’s style of reasoning and can further improve performance.
Fine-Tuning Process: You can leverage Hugging Face’s Trainer API or libraries like PEFT for parameter-efficient fine-tuning. Here’s a simplified example using the Transformers Trainer for a small model:
from transformers import AutoTokenizer, AutoModelForCausalLM, TrainingArguments, Trainer
model_name = "deepseek-ai/DeepSeek-R1-Distill-Qwen-14B"
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True)
# Tokenize your dataset
train_encodings = tokenizer(list_of_prompts, padding=True, truncation=True, return_tensors="pt")
labels = tokenizer(list_of_expected_answers, padding=True, truncation=True, return_tensors="pt")["input_ids"]
# Set up training arguments (small example settings)
training_args = TrainingArguments(
output_dir="finetune_deepseek_legal",
per_device_train_batch_size=1,
gradient_accumulation_steps=4,
num_train_epochs=3,
learning_rate=2e-5,
fp16=True,
logging_steps=10,
save_steps=100,
save_total_limit=2
)
# Define a simple Trainer
trainer = Trainer(model=model, args=training_args,
train_dataset=CustomTextDataset(train_encodings, labels))
trainer.train()In practice, you’d replace CustomTextDataset with a Dataset object that yields tokenized prompt→answer pairs. We use a low batch size and accumulate gradients to fit the model in limited GPU memory; adjust these settings based on your hardware. After fine-tuning, you can test the model on new inputs to see the specialized behavior. The key is that you now have your own customized version of DeepSeek, which can be kept private or deployed as needed – something not possible with closed models like ChatGPT without sending data to an API. (Always remember to follow the open-source license terms and attribute the base model in research or products.)
Tips for Fine-Tuning Success: Since DeepSeek models have a strong reasoning ability, it helps to maintain that format in your fine-tuning. Encourage the model to produce reasoning steps if appropriate (you can include instructions like “Let’s think this through:” in your prompts or few-shot examples). The authors note that the model responds well to prompts that trigger its chain-of-thought mode. Additionally, monitor for overfitting – if your dataset is small, consider using techniques like LoRA to only adjust a few weights, or perform only a couple epochs of fine-tuning so you don’t degrade the general pre-trained knowledge. With open models, you can iteratively refine your fine-tuning and even do things like reward model training for your specific criteria, following the example of DeepSeek’s own RL process. For most developers, however, a straightforward supervised fine-tune on domain data will suffice to significantly improve performance on niche tasks.
Deployment Strategies
Deploying DeepSeek in production requires balancing performance, cost, and scalability. Here are some strategies and considerations for bringing DeepSeek-powered applications to users:
• Hosted API vs Self-Hosted: If you want to skip infrastructure headaches, you can use DeepSeek via its official API. DeepSeek provides a cloud API and a web chat interface, letting you send requests to their hosted service (which runs the full model). This is similar to using OpenAI’s API, except DeepSeek’s is presently free or low-cost due to the open model. However, an API means you rely on a third party and must abide by their rate limits and any content policies. The alternative is self-hosting the model on your own server or cloud instance, which gives you full control.
Self-hosting the 37B expert model might involve multiple GPU machines behind a service endpoint, whereas hosting a 7B–14B model could be done on a single high-memory VM instance. If using cloud providers, consider instances with NVIDIA A100 or H100 GPUs for the full model, or even consumer-grade GPUs for smaller models. Some developers report running the 7B/14B models on as little as 20GB of CPU RAM by using 4-bit quantization, though performance will be slower.
• Inference Optimization: To serve responses with low latency, leverage optimized inference frameworks. DeepSeek’s team recommends tools like vLLM and LMDeploy, which are specialized high-performance inference engines. For example, you can spin up a REST API using vLLM by loading a model with just a one-line command (as shown in their docs): vllm serve deepseek-ai/DeepSeek-R1-Distill-Qwen-32B --tensor-parallel-size 2 --max-model-len 32768.
This will handle efficient batching of requests and fast context processing. If you are deploying on GPU, also consider TensorRT-LLM for Nvidia GPUs, which can further speed up inference via low-level optimizations (the DeepSeek repo includes integration hooks for TensorRT and even INT4/INT8 quantization). By using these optimized runtimes, you can significantly reduce response time and hardware requirements, especially when serving many concurrent users.
• Scaling and Load Management: When deploying a chatbot or AI service, you’ll need to handle multiple user sessions. With DeepSeek, one approach is to run a distributed cluster of the model to serve many requests in parallel. The MoE architecture can be sharded – for instance, run different expert partitions on different nodes. The provided torchrun example in installation already shows multi-node launching. You can containerize that setup with Docker or Kubernetes for easier orchestration.
Another approach for scale is to use the smaller distilled models for high-concurrency tasks, and reserve the big model for when a request explicitly needs extra reasoning power. Because all versions share a similar interface, you could implement a tiered system (fast but slightly less accurate responses from a 7B model for simple queries, and on-demand responses from the 37B model for complex queries). This helps optimize costs: use your GPU fleet efficiently by routing requests based on complexity.
• Memory and Compute Considerations: The full DeepSeek-R1 model (37B active, MoE 671B total) is huge – it requires around 720 GB of memory in 16-bit (or ~90 GB in 8-bit) to load . That’s why multi-GPU is a must at that scale. If you lack such resources, stick to the distilled models. These have much lower memory footprints (the 14B Qwen distilled model is roughly 28 GB in FP16, and can be further reduced to ~15 GB with 8-bit quantization). There are community-contributed 4-bit and 8-bit quantized versions (e.g. in GGUF format on HuggingFace ) which allow running even 32B models on a single GPU with some performance trade-off. Always monitor GPU memory usage and throughput; enabling deep GPU memory optimization (like using DeepSpeed-Inference or Accelerate big model inference) can help fit models that otherwise wouldn’t.
• Monitoring and Maintenance: Like any production AI, you should monitor DeepSeek’s outputs and update it as needed. Since it’s open source, you have the option to incorporate updates – for example, if DeepSeek releases an R2 model or improved checkpoints, you can evaluate and swap those in. The open community is rapidly evolving; already new algorithms (such as the mentioned NSA for long context) are being integrated. Keep an eye on the DeepSeek GitHub for patches and improvements (bugs in the initial release may be fixed over time). If you fine-tuned the model, maintain version control of your fine-tuned weights so you can rollback if an update causes regressions.
In summary, deploying DeepSeek can range from running a single Docker container with a small model to managing a distributed cluster for the full model. Start small – get a prototype working with a distilled model – then scale up as demand grows. The good news is that you’re in control: no external dependencies, and full insight into how the system operates.
Conclusion
DeepSeek’s rise signals a new chapter in the AI industry. In a field that has been dominated by proprietary giants, DeepSeek proved that open-source models can compete at the highest level. By outpacing industry leaders in key areas like reasoning and efficiency, it has forced experts to reconsider the “bigger = better, but also costlier” paradigm.
The future of DeepSeek looks promising: the team is actively improving the model (e.g. refining long-context performance and releasing distilled versions) and the community is contributing integrations and fine-tuned variants. We may soon see DeepSeek integrated into office suites, search engines, and personal assistants – anywhere an advanced conversational AI is needed.
For developers, DeepSeek offers an unprecedented opportunity to build with a state-of-the-art chatbot that you can truly own and customize. As one analyst noted, DeepSeek “challenges the narrative that innovation must come at an unsustainable cost,” potentially democratizing AI access for smaller enterprises and teams.
With this guide and the resources available, you have everything needed to start exploring DeepSeek and harness its capabilities in your projects. The giants have been warned: the open-source upstart is here to stay, and it’s only getting better from here.
Cohorte Team
March 4, 2025
