Llama 3 has been sitting comfortably on the throne of local AI for over a year now. Developers have memorised the Ollama commands, fine-tuned their prompts, and built entire workflows around Meta’s powerhouse model. But something shifted in early 2025 when a Chinese AI lab released DeepSeek R1, claiming it could match OpenAI’s o1 reasoning capabilities while running entirely on your own hardware.
The question everyone is asking is simple but loaded with implications. Should you switch?
I spent the last week running DeepSeek R1’s distilled versions against Llama 3 on identical coding tasks. The results surprised me, not because one model dominated across the board, but because they revealed something more interesting about how we should think about local AI moving forward.
Understanding the Reasoning Revolution
Most language models work like extremely sophisticated autocomplete systems. You give them a prompt, and they start predicting the next word, then the next, then the next. This approach works remarkably well for many tasks. Llama 3 can write decent Python scripts, explain concepts, and even debug simple errors. But it does all of this through pattern matching against its training data.
DeepSeek R1 operates differently. Before generating its final answer, it engages in what researchers call chain of thought reasoning. You can actually watch this happen in real time. The model outputs a thinking block where it breaks down the problem, considers different approaches, identifies potential pitfalls, and plans its solution strategy. Only after this internal deliberation does it produce the actual code or answer you requested.
The difference looks something like this. When you ask Llama 3 to write a function that validates email addresses, it immediately starts outputting code based on common patterns it has seen. When you ask DeepSeek R1 the same question, you first see a thinking block where it considers edge cases like plus signs in addresses, international domain extensions, and the subtle differences between technically valid and practically acceptable email formats. Then it writes the code.
This is not just a parlour trick. The thinking process fundamentally changes the quality of complex outputs.
The Hardware Challenge and the Distillation Solution
Here is where things get practical. The full DeepSeek R1 model weighs in at 671 billion parameters. To run this monster, you would need a data centre, not a home server. Even if you managed to acquire the hardware, the inference speed would make it unusable for interactive coding assistance.
DeepSeek solved this through a process called knowledge distillation. They trained smaller models to mimic the reasoning patterns of the giant model. Think of it like learning to play chess by studying grandmaster games. You do not need grandmaster-level computational power to think like one if you have learned the patterns.
The distilled versions come in two main flavours that matter for home lab users. DeepSeek R1 Distil Llama 8B runs on modest hardware, even a decent laptop with 16GB of RAM. DeepSeek R1 Distill Qwen 32B requires more muscle, sitting comfortably on machines with RTX 3090 or 4090 GPUs, but delivers noticeably better reasoning on complex problems.
Both models retain the chain of thought capability that makes R1 special. The 8B version thinks faster but sometimes misses nuances. The 32B version thinks more thoroughly and catches edge cases more reliably.
The Head-to-Head Comparison
I tested both models across three categories of programming tasks to see where each excels.
Speed tells the first part of the story. Llama 3 generates tokens faster because it does not have the overhead of producing thinking tokens before the actual output. On a simple request to explain how Python decorators work, Llama 3 finished its explanation in about 8 seconds. DeepSeek R1 32B took nearly 15 seconds because it first spent time analyzing different ways to explain the concept before settling on its approach.
For straightforward coding tasks, the models perform similarly. When I asked each to write a web scraper for extracting article titles from a news site, both produced working code on the first try. The structure differed slightly, Llama 3 used requests and BeautifulSoup in a more direct style while DeepSeek added error handling and user agent headers by default, but either script would have gotten the job done.
The real divergence appears with complex logic problems. I gave both models this prompt: write a complete Snake game in Python using Pygame with proper collision detection, score tracking, and increasing difficulty.
Llama 3 produced code that looked good at first glance. It had the basic structure, the snake moved, and food appeared. But when I ran it, the snake could slither right through walls. The game ended only if the snake hit itself. The difficulty never increased because the speed variable was set once and never modified. These are classic beginner mistakes that happen when you generate code pattern by pattern without considering the complete system.
DeepSeek R1’s thinking block was fascinating to watch. It explicitly noted that wall collision needed separate handling from self-collision. It planned out how to increment the speed variable and how to tie it to the score. It even considered whether the snake should wrap around the screen edges or die at the boundary, ultimately choosing the latter with a comment explaining why. The resulting code worked perfectly on the first run.
I repeated variations of this test with different complex scenarios. Building a file system backup tool that handles incremental backups. Creating a REST API with proper authentication middleware. Writing a data processing pipeline with error recovery. The pattern held consistent. For complex tasks requiring multiple interconnected pieces of logic, DeepSeek R1 produced more reliable first drafts.
Running DeepSeek R1 Locally
If you followed the earlier Ollama setup, getting DeepSeek R1 running takes one command. For the 8B model that runs on modest hardware, you type ollama run deepseek-r1:8b and wait for the download to complete. For the 32B version if you have the GPU headroom, ollama run deepseek-r1:32b does the trick.
The first time you interact with the model, you will notice something unusual in the terminal output. Before your actual answer appears, you see blocks of text wrapped in thinking tags. This is the model working through the problem. You can read these thinking blocks to understand its reasoning process, which proves surprisingly useful when debugging why it made certain choices.
Some developers find the thinking tokens distracting and want only the final answer. Others, myself included, have started to appreciate seeing the reasoning. When DeepSeek suggests an approach I disagree with, I can often pinpoint exactly where in its thinking process it made an assumption I want to challenge.
Making the Switch Decision
After a week of testing, I am not switching completely to DeepSeek R1. Instead, I am running both models for different purposes.
For conversational queries, general knowledge questions, and quick explanations, Llama 3 remains my default. It responds faster, and the quality difference for these tasks does not justify the wait time. When I want to know what a Python context manager does or get a quick code snippet for parsing JSON, Llama 3 gives me what I need immediately.
For coding tasks with any meaningful complexity, I now reach for DeepSeek R1 first. The difference becomes especially pronounced when working on something new where I have not yet built up my own mental model. Watching the model think through architecture decisions, consider edge cases, and plan before coding helps me think more clearly about the problem too.
Mathematical reasoning shows similar patterns. Simple arithmetic or explaining basic algebra concepts works fine with either model. But ask for help understanding a proof or working through a complex probability problem, and DeepSeek R1’s step-by-step reasoning becomes invaluable.
One concern worth addressing directly involves DeepSeek’s origins. The company is Chinese, which raises questions about data privacy for some users. But here is the critical detail that makes local AI powerful. You are running the model weights entirely on your own hardware. No prompts leave your network. No responses get sent back to DeepSeek’s servers. The model you downloaded is frozen in time, and it operates completely offline if you want it to. This is true for DeepSeek R1 just as it is for Llama 3, Mistral, or any other model you run through Ollama.
The geopolitical origins of the training process matter for broader discussions about AI development, but they do not affect the privacy guarantees of local inference. Your code never leaves your machine.
What This Means for Local AI
DeepSeek R1 represents more than just another model competing for attention in an increasingly crowded field. It demonstrates that chain-of-thought reasoning, previously available only through expensive API calls to closed models, can now run on hardware you already own.
This changes the calculation for developers building AI-assisted workflows. You no longer have to choose between privacy and capability. You can have both. The model thinking out loud before generating code creates a fundamentally different user experience compared to models that dive straight into output generation.
The practical impact is reflected in fewer iteration cycles. With Llama 3, I often find myself running code, hitting an error, then prompting again with the error message. With DeepSeek R1, the first attempt more often produces working code because the model caught the edge case during its thinking phase.
This does not make DeepSeek R1 perfect. It still hallucinates occasionally. It still requires careful prompting for best results. And for simple tasks, the thinking overhead genuinely wastes time. But for complex problem solving, having a model that plans before executing feels like a genuine step forward rather than just incremental improvement.
Looking Forward
Your home lab now runs models that can reason through problems systematically before writing a single line of code. This capability was science fiction even two years ago. But getting the models running is only half the battle.
Tomorrow, we tackle the unglamorous but critical task that keeps all of this working over the long term. Your models, your configurations, your fine-tuned prompts, and your custom setups all live on drives that can fail without warning.
The next article covers automating backups so a single hardware failure does not erase months of work.
The tools exist. The models work. Now we make it all durable enough to rely on for real work.
Don't forget to follow for upcoming articles.