Running a coding assistant locally has become a practical option for a growing number of developers. Open-weight models trained specifically on code now rival cloud offerings for many everyday programming tasks while keeping proprietary source code off external servers entirely. This guide covers the most capable local coding models available today and what each tier of hardware can reasonably run and the editor tools that connect a local model to a real development workflow. For a broader treatment of why local inference is worth considering in the first place see the case for running AI locally.
Why a coding-specific model
General-purpose language models can write and explain code, but models trained primarily on source code and documentation and programming-related text tend to outperform them on the tasks that matter most to developers. That includes accurate autocomplete and fill-in-the-middle completion and refactoring suggestions that respect the surrounding context and test generation. The difference is most pronounced in less common languages and frameworks where a general model's training signal is thin. A dedicated coding model also tends to produce less verbose surrounding prose, which suits the tight iterative loop of an editor more than a general-purpose assistant does.
Privacy is an additional consideration that applies specifically to code. Proprietary business logic and unreleased algorithms and internal API keys should not pass through an external service however reputable if they can be handled locally instead. A model running on the developer's own machine has no network path along which that material can travel.
The leading models
Qwen2.5-Coder and Qwen3-Coder
The Qwen2.5-Coder series from Alibaba covers a wide range of sizes from a compact 1.5-billion-parameter build up through 7B and 14B and 32B variants. The 32B model is widely regarded as the strongest local coding model currently available and is competitive with top closed-source offerings on standard coding benchmarks. Developers who have run both report that it handles multi-file reasoning and complex refactors and algorithmic problems at a quality that was not achievable locally a year prior.
Alibaba's newer Qwen3-Coder line extends this further. The 30B-A3B variant uses a mixture-of-experts (MoE) architecture that activates only a fraction of its parameters per token, which allows it to deliver strong performance while making more efficient use of available memory. A substantially larger 480B MoE model targets agentic coding workflows where the model must plan and edit and verify across many steps. Both are discussed in more depth in the Qwen3 local AI guide.
DeepSeek-Coder V2
DeepSeek's coding models have been a consistent presence in community benchmarks. The earlier DeepSeek-Coder line offered 6.7B and 33B variants that remained competitive for their size class. DeepSeek-Coder V2 improved on both code generation quality and instruction-following, which makes it better suited to conversational coding sessions rather than simple completion. The V2 architecture also uses MoE, which gives it a larger effective parameter count than a dense model of the same running size would suggest.
Codestral and Devstral
Mistral AI produces two coding-focused models worth noting. Codestral 22B is a dense mid-range model that performs well on fill-in-the-middle tasks and has native support in several editor integrations. Devstral is Mistral's explicitly agentic coding model designed for multi-step editing workflows where the model must use tools and read files and execute sequences of actions rather than responding to a single prompt. It is a sensible choice for developers exploring agent-driven development pipelines.
CodeLlama and StarCoder2
These models occupy the established tier of local coding assistants. CodeLlama is Meta's code-specialised derivative of Llama 2. It is available at 7B and 13B and 34B and 70B scales and remains widely supported in tooling. StarCoder2 from the BigCode project comes in 3B and 7B and 15B variants and was trained on a particularly broad corpus of permissively licensed source code. Neither matches the Qwen2.5-Coder 32B at the high end, but both run reliably in constrained environments and are worth considering when hardware limits what is feasible.
Matching model size to hardware
The practical constraint for local inference is VRAM, the memory on the graphics card. Running a model that exceeds available VRAM causes it to spill into ordinary system RAM, which degrades speed dramatically and often renders autocomplete too slow to be useful. The VRAM requirements guide covers the mechanics in detail. What follows is a rough qualitative picture by tier.
A card with a moderate amount of VRAM handles the 7B coding models comfortably. At this tier models like Qwen2.5-Coder 7B or StarCoder2 7B deliver useful autocomplete and can answer questions about a codebase at acceptable speed. They are well suited to repetitive completions and boilerplate generation and simple bug explanations, tasks that benefit from low latency over raw reasoning depth.
The 14B tier requires more VRAM and produces a noticeable step up in quality for anything involving context across multiple files or nuanced architectural decisions. Qwen2.5-Coder 14B sits here and is a reasonable target for developers on mid-range hardware who need more than the smallest models can offer.
Cards with substantial VRAM at the high end of the consumer market unlock the 32B tier where Qwen2.5-Coder 32B operates. This is the current ceiling for dense local coding models without moving to enterprise-class hardware, and the quality difference relative to the smaller tiers is significant on hard problems. The WillMyGPURunIt calculator will report which models a given GPU can run and at what expected speed, which is useful before committing to a download.
MoE models like Qwen3-Coder 30B-A3B complicate the picture in a useful way. Their active parameter count during inference is lower than the headline figure, so they may fit where an equivalently labelled dense model would not. Consulting the model card for the specific quantization and its memory footprint is advisable before assuming compatibility. The local AI model comparison guide addresses quantization trade-offs across model families.
Integrating a local coding model into an editor
A model running in isolation is useful for chat, but the real productivity gain comes from integrating it directly into the development environment. Several tools make this straightforward.
Continue is an open-source VS Code and JetBrains extension that connects to a locally running model via Ollama or a compatible API for inline autocomplete and chat about selected code and edit suggestions. It requires no cloud account and handles the fill-in-the-middle prompt format that coding models expect.
Cline (formerly Claude Dev) is a VS Code extension oriented toward agentic tasks. It can read files and run terminal commands and execute sequences of edits under instruction. Pointed at a capable local model such as Qwen2.5-Coder 32B or Devstral it approximates the behaviour of hosted agentic coding assistants without transmitting any code externally.
Aider is a command-line coding assistant that works across any editor by operating on the filesystem directly. It maintains a map of the repository and applies edits as unified diffs and supports local model backends. Developers who prefer terminal-centric workflows and want fine-grained control over what the model touches tend to favour it.
All three tools support Ollama as a backend, which means the setup process reduces to a few steps. Pull the chosen model with Ollama, point the editor extension at the local endpoint, and begin working. The model never leaves the machine.
Choosing a starting point
For most developers Qwen2.5-Coder is the natural first choice. Use the 7B model for constrained hardware and the 32B where the GPU supports it. Those on mid-range cards who want to explore the next tier should consider Qwen2.5-Coder 14B or, if the MoE memory savings are advantageous, Qwen3-Coder 30B-A3B. Codestral 22B is worth evaluating for fill-in-the-middle specifically, and Devstral for workflows that require tool use and multi-step reasoning.
The appropriate tool for connecting the model to an editor depends on working style. Continue is for inline autocomplete and conversational assistance, Cline for agent-driven editing, and Aider for terminal-first workflows. All three are free and support local backends. The only meaningful prerequisite is knowing which models the available hardware can actually run, a question the WillMyGPURunIt calculator answers before any downloading begins.