DeepSeek or Qwen for Local Use: Which One to Pick

"DeepSeek or Qwen?" is too broad a question without model sizes and your hardware next to it. A compact Qwen on a laptop, server-grade DeepSeek V4, and an 8-billion parameter distilled DeepSeek are three completely different experiences. What's worth comparing is what actually fits in the memory of one computer.

The short answer

For running locally at home, Qwen is usually the more convenient pick: the family has many sizes, so it's easier to match one to 8, 16, or 32 GB of memory. DeepSeek is worth choosing when you need a specific distilled model or have server-grade hardware for the larger weights.

If the question is specifically DeepSeek V4 versus Qwen 3.6, Qwen's advantage on an ordinary PC is even clearer. Even DeepSeek V4 Flash falls into the very-large category, while Qwen 3.6 has 27B and 35B-A3B versions. Those still need memory, but at least they're in the range of a strong workstation after quantization.

Why the family name tells you nothing about requirements

File size depends on parameter count and quantization. A rough rule for GGUF Q4: roughly 0.5–0.7 GB of memory per billion parameters, plus headroom for context and overhead. So a 7–8B model usually fits in 8 GB of RAM, a 14B one is more comfortable at 16 GB, and the 30B class wants around 24–32 GB or more.

That's an estimate, not a speed guarantee. VRAM speeds up generation, RAM lets the model start at all, and a long context increases the cost.

DeepSeek: strong models, read the name carefully

The flagship DeepSeek V4 Pro and Flash are published openly, but they're too large for a home computer. Catalogs also have small models with "DeepSeek" in the name — usually these are distillations built on the Qwen or Llama architecture. They carry over some of the approach from the large model, but aren't equal to the cloud V4 in quality.

That kind of model makes sense for reasoning, math, and tasks where you've already tested that specific build. Look at the full model identifier, size, and license — not just the word "DeepSeek."

Qwen: easier to match to your computer

Qwen has a wide lineup of open models, including compact versions and dedicated code variants. That's practical for a local agent: you can start with a small model, verify tool calling works, and move up a size from there.

Qwen 3.6 at 27B isn't a model for a weak laptop anymore. With 8–16 GB of memory, it makes more sense to look at smaller Qwen models from the previous generation rather than force the newest number to run at any cost.

How to compare on your own hardware

Take two models of similar size and the same quantization level. Then run a set of your own tasks through both: a short answer, a long document, code, a tool call. Note the time to first response, tokens per second, peak memory use, and error count. No one else's benchmark shows how a model works with your own files.

In Doka you can load your own .gguf or connect Ollama and switch between models without changing your workflow. There are dedicated guides for DeepSeek locally and Qwen Coder.

What to pick

For a first local setup and limited memory — a Qwen model sized to fit. For experimenting with reasoning — a compact DeepSeek distillation, but compare it against a Qwen model of the same class. Leave the flagship DeepSeek V4 to an API or a multi-accelerator server: having open weights doesn't make a model home-friendly.

Don't compare them in a chat window

The biggest mistake when choosing is turning it into a job interview for the models. Ask a couple of questions, judge the tone, pick whichever "answers more pleasantly." That criterion predicts nothing about real work.

Models differ where they're actually put under load: long context, tool calls, several steps in a row with a check on the result. A model that writes beautifully in an empty chat can lose the plot on the third step of a task, and vice versa. You can only see that on a real task, not in a conversation.

So the practical order is: install an agent, feed both models the same real task with a verifiable outcome — a failing test, a folder of documents, a report from an export — and count how many attempts it takes to get a result.

Doka is convenient for a test like this because models switch in the interface with no reconfiguration: download both from the catalog, run the same task through each, compare. No terminal, no separate engine to stand up — download it for free. You'll also see what size your machine can actually handle; there are memory guidelines in the article on GPUs for local models, or the model calculator if you'd rather not eyeball it.