{"slug": "run-qwen-coder-deepseek-locally-the-2026-free-ai-pair-programmer-setup", "title": "Run Qwen Coder & DeepSeek Locally: The 2026 Free AI Pair-Programmer Setup", "summary": "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.", "body_md": "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.\n\nTwo years ago, local coding models were a toy. The autocomplete was slow and the suggestions were noise. That changed. `qwen2.5-coder`\n\nand `deepseek-coder-v2`\n\nare 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.\n\nThe pitch is simple:\n\nThe tradeoff is quality and latency. We'll be honest about both.\n\nThis 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.\n\n```\n# Fast, fits anywhere (8GB+)\nollama pull qwen2.5-coder:1.5b    # ~1.0GB\nollama pull qwen2.5-coder:3b      # ~1.9GB\n\n# The sweet spot for most laptops (16GB)\nollama pull qwen2.5-coder:7b      # ~4.7GB\n\n# Quality tier, needs headroom (32GB+ comfortable)\nollama pull deepseek-coder-v2     # ~8.9GB (16b MoE)\nollama pull qwen2.5-coder:14b     # ~9.0GB\nollama pull qwen2.5-coder:32b     # ~20GB\n```\n\nRough 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.\n\n| Model | Size | RAM I'd want | Use it for |\n|---|---|---|---|\n`qwen2.5-coder:1.5b` |\n1.0GB | 8GB | Autocomplete, fast iteration |\n`qwen2.5-coder:7b` |\n4.7GB | 16GB | Daily driver: chat, refactors, explain |\n`deepseek-coder-v2` |\n8.9GB | 32GB | Harder reasoning, multi-file context |\n`qwen2.5-coder:32b` |\n20GB | 64GB | Near-cloud quality, if you have the RAM |\n\n`deepseek-coder-v2`\n\nis 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`\n\ngives a shallow answer.\n\nA 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`\n\nfor 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.\n\nMy actual setup on 16GB: `qwen2.5-coder:1.5b`\n\nfor inline autocomplete (it has to be fast or it's useless), `qwen2.5-coder:7b`\n\nfor the chat sidebar where I can wait two seconds. I keep `deepseek-coder-v2`\n\npulled but unloaded for the occasional gnarly problem, and let Ollama swap it in on demand.\n\n```\n# Linux / WSL\ncurl -fsSL https://ollama.com/install.sh | sh\n\n# macOS\nbrew install ollama\n\n# Windows: download from https://ollama.com/download\n```\n\nStart the server. It listens on `localhost:11434`\n\n:\n\n```\nollama serve\n```\n\nConfirm it's alive and a model responds:\n\n```\nollama run qwen2.5-coder:7b \"Write a TypeScript debounce function\"\n```\n\nIf that prints code, you have a working local LLM. Everything else is wiring.\n\n[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.\n\nEdit `~/.continue/config.yaml`\n\n:\n\n```\nname: Local pair programmer\nversion: 1.0.0\nschema: v1\n\nmodels:\n  - name: Qwen Coder 7B\n    provider: ollama\n    model: qwen2.5-coder:7b\n    roles:\n      - chat\n      - edit\n  - name: Qwen Coder 1.5B (autocomplete)\n    provider: ollama\n    model: qwen2.5-coder:1.5b\n    roles:\n      - autocomplete\n```\n\nTwo 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.\n\nRestart 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.\n\nIf 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`\n\n. Continue is still the more flexible option for autocomplete tuning.\n\nOne thing worth knowing: autocomplete and chat use different prompt formats under the hood. Continue handles this for you when you assign the `autocomplete`\n\nrole, 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`\n\nfamily supports fill-in-the-middle at every size, which is why I use it for both jobs.\n\nBefore trusting any editor integration, hit the server directly. Ollama exposes an OpenAI-compatible endpoint, so this works with zero SDK:\n\n``` js\nconst response = await fetch(\"http://localhost:11434/v1/chat/completions\", {\n  method: \"POST\",\n  headers: { \"Content-Type\": \"application/json\" },\n  body: JSON.stringify({\n    model: \"qwen2.5-coder:7b\",\n    messages: [\n      { role: \"system\", content: \"You are a senior TypeScript reviewer.\" },\n      {\n        role: \"user\",\n        content: \"Find the bug:\\nfunction sum(a, b) { return a - b }\",\n      },\n    ],\n    temperature: 0,\n    stream: false,\n  }),\n});\n\nconst data = await response.json();\nconsole.log(data.choices[0].message.content);\n```\n\nNo 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.\n\nFor a streaming UI (the token-by-token effect), flip `stream: true`\n\nand 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.\n\nNumbers 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:\n\n`qwen2.5-coder:7b`\n\nthat's a few seconds. After that it stays warm.`1.5b`\n\nfeels instant enough`7b`\n\n`1.5b`\n\nand `3b`\n\nor you'll be waiting.Tip: keep `ollama serve`\n\nrunning in the background all day. Don't start and stop it per request, you pay the load cost every time.\n\n`deepseek-coder-v2`\n\ndo tab completion, the latency kills the flow.`temperature: 0`\n\n`Modelfile`\n\n.`ollama run qwen2.5-coder:7b \"\"`\n\nat 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:\n\n| Task | Local (`7b` / `deepseek` ) |\nReach for cloud |\n|---|---|---|\n| Inline autocomplete | Great | Overkill |\n| \"Explain this function\" | Great | No need |\n| Boilerplate, tests, docstrings | Great | No need |\n| Refactor within one file | Good | Marginal gain |\n| Multi-file architecture reasoning | Hit or miss | Better |\n| Subtle security review | Use as first pass | Better |\n| Latest framework APIs (2026) | Stale | Better |\n\nThe 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.\n\nWhen 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:\n\n```\n# Local, free, no API key, contract never leaves the machine\npnpm --filter @spectr-ai/engine dev -- \\\n  --model ollama:qwen2.5-coder:1.5b examples/vulnerable.sol\n```\n\nFor 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.\n\nGet `qwen2.5-coder:7b`\n\nrunning, 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.", "url": "https://wpnews.pro/news/run-qwen-coder-deepseek-locally-the-2026-free-ai-pair-programmer-setup", "canonical_source": "https://dev.to/pavelespitia/run-qwen-coder-deepseek-locally-the-2026-free-ai-pair-programmer-setup-jo4", "published_at": "2026-07-16 15:42:24+00:00", "updated_at": "2026-07-16 16:08:26.434048+00:00", "lang": "en", "topics": ["large-language-models", "developer-tools", "ai-tools", "ai-infrastructure", "ai-products"], "entities": ["Ollama", "Continue.dev", "Qwen Coder", "DeepSeek", "VS Code", "JetBrains", "Copilot"], "alternates": {"html": "https://wpnews.pro/news/run-qwen-coder-deepseek-locally-the-2026-free-ai-pair-programmer-setup", "markdown": "https://wpnews.pro/news/run-qwen-coder-deepseek-locally-the-2026-free-ai-pair-programmer-setup.md", "text": "https://wpnews.pro/news/run-qwen-coder-deepseek-locally-the-2026-free-ai-pair-programmer-setup.txt", "jsonld": "https://wpnews.pro/news/run-qwen-coder-deepseek-locally-the-2026-free-ai-pair-programmer-setup.jsonld"}}