Running a coding model on your own machine went from a hobbyist experiment to a genuinely practical workflow in 2026. The appeal for a privacy-conscious developer is direct: your proprietary code never leaves the device, there is no per-token bill, it works offline, and the whole setup is reproducible. The catch is equally direct — the best local LLM for coding is whichever strong model actually fits your VRAM, not whichever tops a leaderboard you cannot run.
This guide ranks the realistic options by that constraint, with concrete VRAM math, the runner-and-editor stack, and an honest account of where local still trails the cloud.
Why run a coding LLM locally at all
- Privacy and IP control. Nothing is sent to a third-party API — no provider-side logging, no risk of your code being retained or used for training, no cross-jurisdiction exposure. For regulated or proprietary codebases this is the whole point. See our note on data sovereignty.
- Cost. After the hardware you already own, inference is free. Heavy users save the most.
- Offline & reproducible. Works on a plane; the same weights give the same behaviour indefinitely, unlike a hosted model that silently changes.
The trade-off is capability and convenience — which is exactly where the honest comparison below matters.
The VRAM reality (read this first)
The single number that decides your options is VRAM at your chosen quantization. A working rule at 4-bit (Q4):
- ~0.6–0.8 GB of VRAM per billion parameters, plus context overhead.
- 7B → ~6–8 GB (RTX 3060/4060-class laptops and desktops).
- 14B → ~10–12 GB.
- 32B → ~20–24 GB (RTX 4090; Apple Silicon with 32 GB+ unified memory).
Apple M-series shines here because the GPU shares system RAM — a 48–64 GB Mac runs 32B models that would need a top-tier discrete GPU otherwise. Below 8 GB, stay at 3B–7B.
The honest 2026 ranking
Qwen2.5-Coder — best all-round local coder. Available in 0.5B up to 32B, it is the model most worth defaulting to: strong fill-in-the-middle completion, broad language coverage, and good reasoning for its size. The 7B fits modest GPUs; the 14B is the sweet spot for a 12 GB card; the 32B rivals much larger models when you have the memory.
DeepSeek-Coder-V2 — strongest broad language coverage. A mixture-of-experts coder with excellent multi-language support. The larger variants are heavy, but smaller distilled options remain practical, and it is a frequent top pick for polyglot codebases.
Codestral — best for low-latency completion. Mistral's code model is tuned for fast fill-in-the-middle and autocompletion, making it a strong choice as an always-on editor assistant rather than a chat-style reasoner.
StarCoder2 / CodeLlama — solid, permissive fallbacks. Mature, well-documented, and easy to run; useful when licensing clarity or ecosystem tooling matters more than topping benchmarks.
For broader, cloud-inclusive comparisons, see best coding LLMs 2026 and best AI coding assistants 2026.
The runner + editor stack
- Runner — execute the model: Ollama (easiest), llama.cpp (most control), LM Studio (GUI), vLLM (throughput/server). Most consumer setups use GGUF quantized weights.
- Editor integration — Continue (VS Code / JetBrains) points your editor at a local endpoint; Tabby runs a self-hosted completion server; some assistants offer offline modes.
- Bind to localhost. Keep the runner on
127.0.0.1, not0.0.0.0, and disable extension telemetry — see network leak detection for verifying nothing escapes.
The common 2026 stack: Ollama serving the model + Continue wired into the editor.
The honest gap versus cloud
Local models do not match frontier hosted models (Claude, GPT) on the hardest multi-file reasoning and long-context refactoring — claiming otherwise is the field's most common exaggeration. What you trade that frontier capability for is privacy, zero marginal cost, offline use and reproducibility. The pragmatic workflow is hybrid: a local model for completion, boilerplate, small refactors, code review and anything touching sensitive code; a hosted model for the rare, genuinely hard architectural problem. Choose per task, not per ideology.
If you want the developer-tool comparisons that surround this topic, see GitHub Copilot alternatives 2026 and Cursor alternatives 2026. For the privacy rationale behind keeping inference local, data sovereignty covers where your data is processed and why it matters.
Editorial analysis based on the models' documented parameter sizes, published quantization behaviour and the documented capabilities of the runners and editor integrations. VRAM figures are practical rules of thumb at 4-bit quantization, not vendor guarantees. We state plainly where local models trail hosted ones rather than overselling parity.
