4.2 KiB
Local AI Coding Environment Implementation Plan
Objective
Build a local-first AI coding environment on a Windows 11 workstation with the following requirements:
Hardware
- NVIDIA RTX 5070 Ti 12GB VRAM
- 64GB+ system RAM (if available)
- Windows 11
- WSL2 Ubuntu
- VS Code
Existing Software
- Ollama installed and operational
- Qwen3-Coder 30B installed in Ollama
- Claude Code CLI installed
- Git installed
Constraints
- Source code is confidential.
- All code must remain local by default.
- Cloud models may be used only as an optional fallback.
- Minimize API costs.
- Support large codebases.
Target Architecture
VS Code
|
+---- Continue Extension
|
+---- Claude Code CLI
|
+---- Aider
|
v
Ollama
|
v
Qwen3-Coder 30B
Project Goals
Implement and validate the following workflows.
Workflow 1: Local Chat
Developer can:
- Ask coding questions
- Explain code
- Generate code
- Review code
using:
VS Code
+
Continue
+
Ollama
+
Qwen3-Coder 30B
Workflow 2: Local Agent
Developer can:
- Refactor code
- Create files
- Modify files
- Run git-aware edits
using:
Aider
+
Ollama
+
Qwen3-Coder 30B
Workflow 3: Claude Code Optional
Developer can:
- Use Claude Code against local Ollama models
- Compare behavior against Aider
- Determine whether Claude Code provides additional value
using:
Claude Code
+
Ollama
+
Qwen3-Coder 30B
Deliverables
Produce the following:
Deliverable 1
Environment verification script.
Verify:
- Ollama installed
- NVIDIA GPU visible
- CUDA available
- WSL functioning
- Qwen3-Coder model available
Expected output:
PASS: Ollama
PASS: GPU
PASS: CUDA
PASS: Qwen3-Coder
Deliverable 2
Aider installation guide.
Include:
Linux / WSL installation
Commands:
pipx install aider-chat
or preferred installation method.
Ollama integration
Configuration examples.
Verification steps
Simple repository test.
Deliverable 3
Continue configuration.
Create:
VS Code setup instructions
Install Continue extension.
Model configuration
Configure Continue to use:
Ollama
Qwen3-Coder 30B
Example config files
Include complete examples.
Deliverable 4
Claude Code local model integration.
Research and implement the best available approach for:
Claude Code
->
OpenAI-compatible endpoint
->
Ollama
Requirements:
- Use officially supported methods where possible.
- Avoid unsupported hacks.
- Document limitations.
- Provide rollback procedure.
Deliverable 5
Performance optimization.
Analyze:
VRAM usage
Qwen3-Coder 30B on RTX 5070 Ti 12GB.
Recommended quantization
Evaluate:
- Q4
- Q5
- IQ3
Recommend the best balance between:
- Quality
- Speed
- Memory usage
Deliverable 6
Benchmark suite.
Create repeatable tests:
Test 1
Generate a REST API.
Test 2
Refactor a medium-sized module.
Test 3
Write unit tests.
Test 4
Debug a failing application.
Measure:
- Completion time
- Accuracy
- Token throughput
- User effort
Compare:
Aider + Qwen3-Coder 30B
Claude Code + Qwen3-Coder 30B
Continue + Qwen3-Coder 30B
Preferred Outcome
Primary development workflow:
VS Code
+
Continue
+
Qwen3-Coder 30B
Agent workflow:
Aider
+
Qwen3-Coder 30B
Optional advanced workflow:
Claude Code
+
Qwen3-Coder 30B
Success Criteria
The project is successful if:
- All code remains local.
- No cloud services are required.
- Developer can perform daily coding tasks locally.
- Aider successfully edits repositories.
- Continue provides a productive IDE experience.
- Claude Code local integration is evaluated and documented.
- Setup can be reproduced on a fresh machine.
Final Report
Produce a final report containing:
- Architecture diagram
- Installation steps
- Configuration files
- Benchmark results
- Known limitations
- Recommended workflow
- Future upgrade path
The report should be suitable for long-term maintenance and onboarding of additional developers.