Best Local LLM for Coding in 2026: What Runs on 16GB VRAM, Macs and Laptops

On 16GB of VRAM, Qwen2.5-Coder 14B is a dependable choice for completions and edits, and OpenAI designed its open-weight gpt-oss 20B to run within 16GB of memory. Both leave some headroom for context at 4-bit quantisation. Larger models such as Devstral 24B or Qwen3-Coder 30B need around 14 to 18GB before context, so they are tight or too large for a 16GB card.

A practical rule of thumb at 4-bit quantisation is about 0.6GB of memory per billion parameters, plus a few gigabytes for context and the runtime. That puts a 7B model near 5GB, a 14B model near 9GB, a 24B model near 14GB and a 30B model near 18GB. Long context windows increase usage further, so always leave headroom and check real usage once the model is loaded.

Yes, Apple Silicon Macs are strong local LLM machines because the GPU shares unified memory with the system. A Mac with 32GB or more of unified memory can run 24B to 30B coding models such as Devstral or Qwen3-Coder that would not fit on many consumer graphics cards. Speed depends on the chip generation and memory bandwidth, and Ollama and llama.cpp both support Apple Silicon.

Not for everything. Local open models handle autocomplete, explanations and small edits well, and they keep code on your machine. On long, multi-file agentic tasks they still trail the frontier cloud models used by Claude Code and Codex. Many developers combine both: a local model for everyday completions and private code, and a cloud agent for complex refactors and larger features.

Install Ollama, pull a coding model such as qwen2.5-coder, and leave it running so it serves a local API. Then install Continue, an open-source coding agent for VS Code and JetBrains, and configure it to use your Ollama models for chat and autocomplete. Everything runs on your machine, so it works offline and your code is never sent to a cloud provider.