What is the best local LLM for coding?

It depends on your hardware and tasks. Coding-tuned models generally beat general models of the same size. Choose the largest one that runs at acceptable speed in your memory, and consider a smaller second model for fast autocomplete. Re-evaluate as new open models are released frequently.

Can I use a local LLM in VS Code?

Yes. Run a local server with a tool such as Ollama or LM Studio, then install an extension that supports local or OpenAI-compatible endpoints, such as Continue. Configure chat and autocomplete models separately to balance quality and speed. Point both at the same local server URL.

Is a local coding LLM as good as hosted models?

For routine tasks such as completion, explanation, and small edits, good local models can be very useful. For complex multi-file reasoning and long agentic tasks, the largest hosted models are usually stronger. Many developers combine both, keeping sensitive or routine work local.

How to Use a Local LLM for Coding

To use a local LLM for coding, install a local runner such as Ollama, LM Studio, or llama.cpp, download a coding-tuned model that fits your memory, and connect it to your editor through an extension or an OpenAI-compatible endpoint. Give it focused context from your codebase, use it for completion, explanation, and small edits, and route harder tasks elsewhere.

Why run a coding model locally

Local models keep source code on your machine, which matters for proprietary or regulated work. They have no per-token cost, work offline, and respond with low latency for small tasks. The trade-offs are hardware requirements and lower capability than the largest hosted models on complex reasoning.

Step 1: choose a runner

Ollama offers a simple command-line workflow and a local API. LM Studio provides a desktop interface for downloading and serving models. llama.cpp is a lightweight engine that many tools build on, and vLLM targets higher-throughput serving on GPUs. Most runners can expose an OpenAI-compatible endpoint, which many editor tools can use.

Step 2: pick a model that fits

Choose a coding-tuned model sized for your memory. Smaller models are fast enough for inline completion; larger ones give better answers in chat and edits. Quantised versions, such as 4-bit, reduce memory with modest quality loss. Check the model's licence and supported context length.

Step 3: connect your editor

Editor extensions, such as Continue for VS Code and JetBrains, can point at a local endpoint for chat and autocomplete. Configure separate models for completion and chat if your hardware allows: a small fast model for completion, a larger one for questions and edits. Some terminal and agent tools also accept custom OpenAI-compatible endpoints.

Step 4: give it the right context

Local models usually have smaller effective context than hosted ones, and they slow down as context grows. Send the relevant function, its callers, related types, and the error message, rather than whole files. A code index helps: search for relevant symbols and snippets first, then pass only those. This also improves accuracy.

Good tasks for local models

Inline completion, explaining unfamiliar code, writing tests for a single function, generating boilerplate, converting formats, writing regular expressions, drafting docstrings, and small refactors within one file are all well suited. These tasks need limited context and are easy to check.

Where local models struggle

Large multi-file refactors, architectural reasoning, debugging subtle concurrency or performance issues, and long agentic sequences of tool calls tend to need stronger models and bigger contexts. A practical setup routes routine work locally and escalates harder tasks to a hosted model, keeping sensitive code local where possible.

Performance tips

Keep the model loaded between requests to avoid reload delays. Limit context length to what you need. Use quantised KV caches if your engine supports them. Close other GPU-heavy applications. Measure tokens per second for your common tasks and adjust model size accordingly.

Using RDK with a local model

RDK indexes your codebase as encrypted private chunks that agents search before calling a model. With a local model, retrieval does two jobs: it supplies precise context so a smaller model answers well, and it returns known answers directly so the model is not asked at all. The guide to indexing a codebase with RDK covers setup.

Privacy and security

Keeping models local protects code from external services, but the machine itself must be secure. Bind local servers to localhost unless remote access is intended, protect any exposed endpoint with authentication, and be careful with extensions that send telemetry. Review what context your editor extension sends, even to a local model, so secrets in configuration files are not included unnecessarily.

Evaluating your setup

Keep a small set of real tasks from your work, such as explaining a tricky function, writing a test, and fixing a known bug. Run them whenever you change models or settings, and compare quality and speed. This tells you more than public benchmarks about whether a model suits your codebase and languages.

Agentic coding locally

Some agent tools can run against local models, letting the model read files, run commands, and iterate. Local models often struggle with long tool-use sequences, so keep tasks small, give clear instructions, and review every change before accepting it.

Prompting local models

Smaller models benefit from explicit instructions: state the language, framework, and expected output format, and ask for one change at a time. Provide examples of the coding style you want. Short, specific prompts usually beat long, open-ended ones.

Keeping models current

Open coding models improve frequently. Revisit your choice every few months, rerun your task set, and switch when a new model is clearly better on your own work.

Frequently asked questions

What is the best local LLM for coding?: It depends on your hardware and tasks. Coding-tuned models generally beat general models of the same size. Choose the largest one that runs at acceptable speed in your memory, and consider a smaller second model for fast autocomplete. Re-evaluate as new open models are released frequently.
Can I use a local LLM in VS Code?: Yes. Run a local server with a tool such as Ollama or LM Studio, then install an extension that supports local or OpenAI-compatible endpoints, such as Continue. Configure chat and autocomplete models separately to balance quality and speed. Point both at the same local server URL.
Is a local coding LLM as good as hosted models?: For routine tasks such as completion, explanation, and small edits, good local models can be very useful. For complex multi-file reasoning and long agentic tasks, the largest hosted models are usually stronger. Many developers combine both, keeping sensitive or routine work local.