Local AI Coding

2025 Research Report

The Developer's Stack for
Local AI-Assisted Coding

The 2025 meta is Specialized, Local, and Quantized. The primary goal is achieving absolute privacy, security, and compliance by running SOTA models entirely on your machine.

Model Architecture: Dense vs. MoE

The divergence in performance between the S-Tier models—Qwen2.5-Coder (Dense) and DeepSeek-V3 (Mixture-of-Experts or MoE)—reveals a crucial distinction in their capabilities.

Qwen2.5-Coder (Dense, 32B)

The "Software Engineer". Highly specialized, strong Aider score, excelling at daily tasks like debugging and generating boilerplate.

DeepSeek-V3 (MoE, 671B Total)

The "Research Scientist". Vast knowledge applied sparsely (37B active params). Superior for complex, abstract reasoning and new algorithms.

Aider Benchmark: Autonomous Coding (Pass %)

The "Benchmark Trap": Code-specific models (Qwen, DeepSeek) outperform the massive Llama generalist because Aider measures *editing* ability, not just function generation.

VRAM Requirement Calculator

Quantization (GGUF) is the key. **4-bit (Q4\_K\_M) is the new frontier**, offering an 8x memory reduction with minimal practical performance loss.

Model Size (Parameters)

Quantization Level

Estimated VRAM (Weights + 8K Context)

-- GB

Select Options

The GGUF format enables flexible weight management, allowing layers to be offloaded to slower system RAM if VRAM is insufficient, though performance suffers greatly. The goal is always 100% VRAM residency.

The Software Ecosystem

The goal is seamless integration. Ollama is the invisible backend service that connects your model to your IDE.

The Runtime: Ollama

Ollama is the clear winner for professionals. It runs as a stable background service and provides a clean, **OpenAI-compatible REST API**. Model management is simple: ollama pull qwen2.5-coder:32b.

Competitors:

LM Studio (GUI focused, less integration power) / llama.cpp (command-line, for tinkerers).

IDE Integration (Offline Mode)

VS Code: Use Continue extension.
Acts as a bridge, routing requests from the editor to your local Ollama API.
JetBrains: Use AI Assistant plugin.
Crucially, enable "Offline mode" to guarantee data never leaves your network.

The Hybrid Stack (FIM)

Separate FIM autocomplete from heavy reasoning for max speed.

Autocomplete (FIM)

Tabby / FauxPilot + Mistral Codestral

Chat & Debug

Ollama + Qwen 32B

Strategic Build Lists

Pragmatic NVIDIA

Best Practicality

HW: RTX 3090/4090 (24GB)
Model: Qwen2.5-Coder 32B
Format: GGUF Q5_K_M
Runtime: Ollama + Continue

Runs S-Tier coding model entirely in VRAM for maximum speed and fluidity.

Apple Ecosystem

Capacity & Portability

HW: M3/M4 Max (64GB+)
Model: Llama 3.1 70B
Format: GGUF Q4_K_M
Runtime: Ollama (Metal)

Leverages Unified Memory to run 70B models impossible on single discrete GPUs.

Ultimate Hybrid

Zero Compromise

FIM: Tabby + Codestral 22B
Debug: Ollama + Qwen 32B
HW: RTX 3090/4090

Separates fast autocomplete from heavy-duty reasoning and chat functions.

Bleeding Edge

Researcher / Complex Math

HW: 2x RTX 4090 (48GB)
Model: DeepSeek-V3 (MoE)
Runtime: Ollama Multi-GPU

Required hardware to run the most powerful open-source reasoning engines.