A general-purpose model can write code, but a model trained for codeof the same size will beat it at the things that matter day to day: filling in the middle of a file, multi-file edits, following a repo's conventions, and staying coherent across the long contexts that agentic tools generate. In 2026 the open-weight coder models that fit a single consumer GPU are good enough to be your daily driver for most tasks — and private, free per token, and yours. Here's the honest ranking, and how to wire them into the coding agents you already use.
Before picking, check what your card can serve. Can I run it? → shows which coder models run great on your GPU and how fast; the agent setup generator spits out the exact config for Cline, Aider, Continue, or Qwen Code.
The best coding models by GPU size
| Model | Params | ~VRAM (Q4) | Why it's good | Runs on |
|---|---|---|---|---|
| Qwen3-Coder-30B (A3B MoE) | 30B / 3B active | ~18 GB | Top single-GPU coder; fast (MoE), great agentic edits | 24GB card |
| DeepSeek R1 Distill Qwen 32B | 32B | ~20 GB | Reasoning-strong; shines on tricky debugging | 24GB card |
| Devstral Small 24B | 24B | ~15 GB | Mistral's agent-tuned coder; strong tool use | 16–24GB card |
| Qwen3 14B | 14B | ~9 GB | Best coder that fits 12–16GB comfortably | 12–16GB card |
| DeepSeek R1 Distill Qwen 14B | 14B | ~9 GB | Reasoning traces help on logic bugs | 12–16GB card |
| Qwen2.5-Coder-7B | 7B | ~5 GB | Best small coder; great autocomplete on 8GB | 8GB card |
The standout is Qwen3-Coder-30B.Because it's a mixture-of-experts model that only activates about 3B parameters per token, it runs far faster than a dense 30B while keeping a large model's breadth — the best balance of capability and speed you can get on a single 24GB card. On 12–16GB, a 14B coder is the productive floor for agentic work. On 8GB, Qwen2.5-Coder-7B is excellent for autocomplete and single-file edits; just keep your context modest.
Context is the hidden requirement
Coding agents don't just send your prompt — they read files, diffs, and tool output into the context window, and they burn through it fast. A coder model with a cramped context will forget the file it edited three steps ago. Aim for at least 32K, and 64K if your VRAM allows, and set it explicitly (most defaults are too small). That KV cache is real VRAM on top of the weights:
A 14B coder at Q4 might be ~9GB of weights, but push it to 64K context and the KV cache can add several more — which is why a 14B sometimes wants a 16GB card to be comfortable for agents. Size it with the context window calculator and VRAM calculator.
How coding agents talk to a local model
Here's the good news that makes all of this practical: nearly every coding agent already speaks the OpenAI API. Cline, Aider, Continue, Codex CLI, and Qwen Code all have an "OpenAI Compatible" provider mode that needs exactly three things — a base URL, an API key, and a model ID. Point those at a local model and the agent neither knows nor cares that the tokens came from a GPU in the next room.
# Any OpenAI-compatible coding agent — same three settings.
# (Cline, Aider, Continue, Codex CLI, Qwen Code, ...)
Base URL: https://wideareaai.com/api/v1
API key: wai_sk_...
Model ID: Qwen3-Coder-30B-A3B-Instruct # must match what your node serves
# Aider, for example:
aider --openai-api-base https://wideareaai.com/api/v1 \
--openai-api-key wai_sk_... \
--model Qwen3-Coder-30B-A3B-InstructThe one important caveat: Claude Code is the exception.It speaks Anthropic's Messages API, not the OpenAI format, so you can't just swap a base URL — it needs a translation layer. We cover the accurate path for it (and every other agent) in how to use Claude Code & coding agents with a local LLM.
Why route a coding model through a gateway
You can run a coder model on localhost and point a local agent at it — and if that's all you need, do that. But coding work tends to grow past one machine: you want the model from your laptop and your desktop, you want a key for a CI job, you don't want the agent to hard-fail when the GPU box reboots mid-task, and occasionally you want a frontier cloud model for a hard problem without rewiring your tool. That's the gateway's job.
Wide Area Intelligence serves your chosen coder model from a node (llama.cpp under the hood) behind one OpenAI-compatible endpoint with revocable wai_sk_ keys, multi-node load balancing, and automatic capability-aware cloud failover for the requests a local model can't handle. Your agent config never changes — it points at https://wideareaai.com/api/v1and the gateway decides what answers. It's free for up to two nodes. Put your coding GPU on the network →
Already know your agent? We have step-by-step guides for Cline, Aider, Continue.dev, and Qwen Code.