Run Qwen Coder & DeepSeek Locally: The 2026 Free AI Pair-Programmer Setup

A developer detailed a 2026 setup for running coding models like Qwen Coder and DeepSeek locally on a 16GB laptop, using Ollama and Continue.dev for offline autocomplete, refactors, and chat. The setup eliminates monthly Copilot fees by leveraging models such as qwen2.5-coder:7b as a daily driver and deepseek-coder-v2 for harder reasoning, with tradeoffs in quality and latency. The guide includes model selection, RAM requirements, and configuration steps for VS Code and JetBrains editors.

You're paying $10 to $20 a month for Copilot. You don't have to. A 2024-era laptop can run a coding model good enough for autocomplete, refactors, and "explain this function" entirely offline. No API key, no telemetry, no per-token bill. Here's the exact 2026 setup I run on a 16GB machine. Two years ago, local coding models were a toy. The autocomplete was slow and the suggestions were noise. That changed. qwen2.5-coder and deepseek-coder-v2 are genuinely useful now, and the tooling caught up: Ollama serves them, Continue.dev wires them into your editor, and the whole thing runs on hardware you already own. The pitch is simple: The tradeoff is quality and latency. We'll be honest about both. This is the decision that makes or breaks the experience. Pick a model your machine can actually hold in memory, or it spills to disk and crawls. Fast, fits anywhere 8GB+ ollama pull qwen2.5-coder:1.5b ~1.0GB ollama pull qwen2.5-coder:3b ~1.9GB The sweet spot for most laptops 16GB ollama pull qwen2.5-coder:7b ~4.7GB Quality tier, needs headroom 32GB+ comfortable ollama pull deepseek-coder-v2 ~8.9GB 16b MoE ollama pull qwen2.5-coder:14b ~9.0GB ollama pull qwen2.5-coder:32b ~20GB Rough rule: the model file size is the floor, then add a few GB for context and the OS. A 4.7GB model on a 16GB machine is comfortable. A 20GB model on the same machine is not. | Model | Size | RAM I'd want | Use it for | |---|---|---|---| qwen2.5-coder:1.5b | 1.0GB | 8GB | Autocomplete, fast iteration | qwen2.5-coder:7b | 4.7GB | 16GB | Daily driver: chat, refactors, explain | deepseek-coder-v2 | 8.9GB | 32GB | Harder reasoning, multi-file context | qwen2.5-coder:32b | 20GB | 64GB | Near-cloud quality, if you have the RAM | deepseek-coder-v2 is a 16b mixture-of-experts model, so it punches above what its file size suggests, only a couple billion parameters are active per token. It's the one I reach for when qwen2.5-coder:7b gives a shallow answer. A note on quantization: those file sizes are the default 4-bit quants Ollama ships. They're the right call for a laptop. You can pull a higher-precision tag like qwen2.5-coder:7b-instruct-q8 0 for slightly better output, but it roughly doubles the memory and the speed cost, and on everyday coding tasks I can't tell the difference. Start with the defaults. My actual setup on 16GB: qwen2.5-coder:1.5b for inline autocomplete it has to be fast or it's useless , qwen2.5-coder:7b for the chat sidebar where I can wait two seconds. I keep deepseek-coder-v2 pulled but unloaded for the occasional gnarly problem, and let Ollama swap it in on demand. Linux / WSL curl -fsSL https://ollama.com/install.sh | sh macOS brew install ollama Windows: download from https://ollama.com/download Start the server. It listens on localhost:11434 : ollama serve Confirm it's alive and a model responds: ollama run qwen2.5-coder:7b "Write a TypeScript debounce function" If that prints code, you have a working local LLM. Everything else is wiring. Continue.dev https://continue.dev is the open-source extension that turns Ollama into an editor assistant. It does chat, inline edits highlight code, Cmd/Ctrl+I, describe the change , and tab autocomplete. Install it from the VS Code or JetBrains marketplace, then point it at your local models. Edit ~/.continue/config.yaml : name: Local pair programmer version: 1.0.0 schema: v1 models: - name: Qwen Coder 7B provider: ollama model: qwen2.5-coder:7b roles: - chat - edit - name: Qwen Coder 1.5B autocomplete provider: ollama model: qwen2.5-coder:1.5b roles: - autocomplete Two models, two jobs. The 1.5b handles the tight feedback loop of tab autocomplete where every millisecond shows. The 7b handles chat and multi-line edits where you'll tolerate a short wait for a better answer. Restart VS Code, open the Continue sidebar, and ask it something about the file you have open. It reads your editor context and answers against your actual code, locally. If you'd rather skip Continue and use the official Copilot-style hook, recent VS Code versions let you add Ollama as a custom model provider in the chat panel pointing at http://localhost:11434 . Continue is still the more flexible option for autocomplete tuning. One thing worth knowing: autocomplete and chat use different prompt formats under the hood. Continue handles this for you when you assign the autocomplete role, picking the fill-in-the-middle template the Qwen Coder models were trained on. If your inline suggestions come out garbled, it's almost always because a chat-only model got assigned the autocomplete role. The qwen2.5-coder family supports fill-in-the-middle at every size, which is why I use it for both jobs. Before trusting any editor integration, hit the server directly. Ollama exposes an OpenAI-compatible endpoint, so this works with zero SDK: js const response = await fetch "http://localhost:11434/v1/chat/completions", { method: "POST", headers: { "Content-Type": "application/json" }, body: JSON.stringify { model: "qwen2.5-coder:7b", messages: { role: "system", content: "You are a senior TypeScript reviewer." }, { role: "user", content: "Find the bug:\nfunction sum a, b { return a - b }", }, , temperature: 0, stream: false, } , } ; const data = await response.json ; console.log data.choices 0 .message.content ; No API key. No account. If this returns "it subtracts instead of adds," your local pair programmer is online and you can build anything on top of it. For a streaming UI the token-by-token effect , flip stream: true and read the response body as a stream. Same endpoint shape as OpenAI, so any client library that targets OpenAI works by just changing the base URL. Numbers depend entirely on your hardware, so here's what I see on my machine 16GB RAM, integrated GPU, WSL2 on Windows rather than invented benchmarks: qwen2.5-coder:7b that's a few seconds. After that it stays warm. 1.5b feels instant enough 7b 1.5b and 3b or you'll be waiting.Tip: keep ollama serve running in the background all day. Don't start and stop it per request, you pay the load cost every time. deepseek-coder-v2 do tab completion, the latency kills the flow. temperature: 0 Modelfile . ollama run qwen2.5-coder:7b "" at startup preloads it so your first real prompt isn't the slow one.I use local models for most of the day and reach for Claude only when the problem is genuinely hard. Here's the honest split: | Task | Local 7b / deepseek | Reach for cloud | |---|---|---| | Inline autocomplete | Great | Overkill | | "Explain this function" | Great | No need | | Boilerplate, tests, docstrings | Great | No need | | Refactor within one file | Good | Marginal gain | | Multi-file architecture reasoning | Hit or miss | Better | | Subtle security review | Use as first pass | Better | | Latest framework APIs 2026 | Stale | Better | The cloud still wins on hard reasoning and on knowledge of the newest APIs. But for the volume of small, repetitive coding questions that make up most of a day, local is not "good enough as a fallback," it's just good enough. The quality gap that existed in 2024 has mostly closed for everyday work. When I built spectr-ai https://github.com/pavelEspitia/spectr-ai , my AI smart-contract auditor, I made the engine provider-agnostic for exactly this reason. The same analysis runs against Claude or against Ollama: Local, free, no API key, contract never leaves the machine pnpm --filter @spectr-ai/engine dev -- \ --model ollama:qwen2.5-coder:1.5b examples/vulnerable.sol For smart-contract work, that "never leaves the machine" part isn't a nice-to-have. Audit clients don't want their unreleased code shipped to a third-party API. Local models make a privacy-preserving first pass possible, then I escalate the interesting findings to Claude. Get qwen2.5-coder:7b running, wire up Continue, and use it for a week before you renew any Copilot subscription. The setup costs you twenty minutes and zero dollars a month after that.