Free Local AI Stack Replaces Claude Code And Codex

A fully local AI coding stack is gaining traction among developers who want the speed and fluidity of agentic “vibe coding” without cloud fees or data leaving their machines. By pairing Goose for orchestration, Ollama for local model runtime, and Alibaba’s Qwen Coder model as the engine, teams are standing up an end-to-end coding assistant that rivals cloud tools like Claude Code and Codex — and they’re doing it for free.

A closer look at the local AI coding stack

Think of this setup as a small software department in a box. Goose is the project lead and planner, parsing your intent and turning it into actionable steps. Ollama is the infrastructure layer that runs and serves models on your CPU or GPU via a local API. Qwen Coder — a coding-specialized large language model from the Qwen family — is the developer that writes, explains, and refactors code.

Unlike monolithic cloud assistants, each layer is swappable. You can switch coding models without changing Goose, update Ollama for better performance, or plug in retrieval and testing tools. The result is control, transparency, and the ability to iterate quickly on your own hardware.

How it compares to cloud-based coding agents

Cost and rate limits are the immediate wins. Light coding fits into $20 subscriptions, but sustained, full-day agentic sessions often push developers into $100–$200 monthly tiers — and still encounter queuing or caps. Running locally eliminates per-token billing and keeps throughput under your control.

Privacy and compliance matter just as much. Cloud providers emphasize strong data handling, but many organizations still face contractual or regulatory constraints on where code and prompts can reside. A local stack keeps repositories, diffs, and prompts on your machine, an advantage for teams in finance, healthcare, defense, or any company with strict IP policies.

Performance and benchmarks on real hardware

Qwen’s coder variants have posted competitive results on open benchmarks like HumanEval and MBPP, with open-source community tests showing strong function generation and repair capabilities across common languages. While top proprietary models still lead on broad reasoning and long-horizon tasks, coder-tuned open models have closed much of the gap on routine engineering work.

On real hardware, the experience is increasingly smooth. Developers report 7B-parameter coder models running at roughly 25–45 tokens per second on recent Apple Silicon laptops using Metal acceleration, and 60–120 tokens per second on midrange NVIDIA GPUs like the RTX 4060, depending on quantization and context length. That’s fast enough to drive iterative coding, repo scans, and refactors without noticeable lag.

What each piece contributes to the workflow

Goose handles the agent loop: analyzing your prompt, planning steps, calling the model to read or write code, proposing diffs, and deciding when to iterate. It’s the memory and method in the workflow, keeping context across turns and coordinating tasks like “scan the repo,” “propose changes,” and “apply diffs.”

Ollama is the runtime and API layer. It downloads and manages models, runs inference on CPU or GPU, handles model switching and versioning, and exposes a consistent local endpoint. It’s not a coder by itself; it’s the engine room that makes models available to agents and tools.

Qwen Coder does the heavy lifting on code. It generates functions and tests, explains unfamiliar code, runs diffs, and performs refactors. The model won’t manage multi-step workflows by itself, which is why Goose sits on top, turning your “vibes” into an execution plan.

Setup and hardware needs for local AI coding

A practical baseline is a modern CPU with 16GB RAM and fast SSD storage. For GPU acceleration, expect roughly 6–8GB VRAM for a 7B model and 12–16GB for a 14B model using 4-bit quantization. Storage footprints are modest: a few gigabytes per model, plus cache.

Installation is straightforward: install Ollama, pull a coder model, point Goose at the local endpoint, and initialize a project workspace. macOS, Windows, and Linux are all well supported by the community, with active issue trackers and model cards maintained by contributors and Alibaba Cloud’s Qwen team.

A real-world workflow for agentic local coding

Ask Goose to “audit the repo and make the onboarding flow more modular.” It will scan files, summarize structure, and propose a plan. Qwen Coder drafts new modules, updates imports, and writes tests. Goose applies diffs, rechecks dependencies, and loops until the test suite is green. You stay at the intent level — clarifying constraints, approving changes, or steering style — while the stack does the mechanical work.

With a 16K–32K context window typical of many modern coder models, the system can juggle several files at once. For larger codebases, developers often add lightweight retrieval to feed relevant snippets into the context, improving accuracy without bloating the model.

Trade-offs to expect with local coding assistants

Open models still hallucinate and can struggle with deep, cross-cutting architectural changes. Long-running tasks require careful prompting and guardrails. Proprietary assistants may outperform on broader reasoning benchmarks like SWE-bench and offer richer integrations out of the box. You’ll also spend time tuning quantization, context sizes, and memory use to hit your machine’s sweet spot.

Yet the upside is compelling: predictable performance, no rate limits, local-first security, and the freedom to instrument every step. For many teams, that trade-off is worth it.

Who should try this local AI coding stack

Indie developers, students, and startups looking to avoid monthly fees will benefit immediately. Enterprises with regulated IP can pilot the stack in a controlled environment, measure code quality against internal standards, and scale to beefier workstations if needed. A good first test is a well-scoped refactor or feature module with an existing test suite to quantify gains.

The bottom line: if you want the “local vibe” of agentic coding without the cloud, Goose plus Ollama plus a Qwen Coder model is a credible, flexible replacement for Claude Code and Codex — and it puts you, not a remote service, in the driver’s seat.