Best Local AI for Coding — run code LLMs on your machine

DeepSeek-Coder-V4 — best overall

DeepSeek-Coder-V4 is the best local coding model available today. Built by DeepSeek, it achieves GPT-4 level performance on coding benchmarks like HumanEval and MBPP while running on consumer hardware.

Model	Size	Context	HumanEval	VRAM needed
DeepSeek-Coder-V4-Lite	7B	128K	85.2%	8 GB
DeepSeek-Coder-V4	33B	128K	91.6%	24 GB
Qwen2.5-Coder-7B	7B	32K	83.5%	8 GB
Qwen2.5-Coder-32B	32B	32K	88.4%	24 GB
Code Llama 7B	7B	16K	67.8%	8 GB
Code Llama 34B	34B	16K	77.4%	24 GB

DeepSeek-Coder-V4 supports fill-in-the-middle (FIM), making it ideal for inline code completion. It excels at Python, TypeScript, and Rust, and handles refactoring across large files with its 128K context window.

Qwen2.5-Coder — Python specialist

Qwen2.5-Coder from Alibaba is the second-strongest local coding model. It matches DeepSeek-Coder on Python tasks and sometimes exceeds it on data-science and mathematical coding.

Key strengths:

• Python: Top performer for data science, ML, and backend code
• Multi-language: Strong on JavaScript, Java, Go, C++
• FIM support: Fill-in-the-middle for partial code completion
• Efficiency: Excellent token-per-second ratio on consumer GPUs

Qwen2.5-Coder-7B runs comfortably on 8 GB VRAM. The 32B variant needs 24 GB and rivals GPT-3.5 on many benchmarks.

Code Llama — general purpose

Code Llama (Meta) pioneered the local coding model space. While newer models have surpassed it, Code Llama remains a solid choice for general-purpose code generation, especially if you need a well-tested model with broad community support.

Code Llama comes in three variants:

• Code Llama — base code generation
• Code Llama - Python — Python-specialized (largest Python training data)
• Code Llama - Instruct — instruction-following for chat-style coding

The 7B and 13B variants run on most GPUs. The 34B requires 24 GB VRAM or CPU offloading.

Setup with Ollama + VS Code

Getting a local coding AI working takes about 10 minutes.

1. Install Ollama from ollama.com — one command for macOS, Linux, or Windows
2. Pull a coding model: ollama pull deepseek-coder-v4 or ollama pull qwen2.5-coder:7b
3. Install Continue — the VS Code extension at continue.dev
4. Configure Continue to point at your local Ollama endpoint: "apiBase": "http://localhost:11434"
5. Start coding — select the model in VS Code and begin using AI-assisted coding

Continue supports tab-autocomplete, inline chat, and full conversation with local models. Your code never leaves your machine.

Performance benchmarks

On a modern GPU (RTX 3090 / 4090), here is what you can expect:

Model	GPU	Tokens/second
DeepSeek-Coder-V4 7B (Q4)	RTX 3090	42 tok/s
DeepSeek-Coder-V4 7B (Q4)	Apple M2 Max	35 tok/s
Qwen2.5-Coder 7B (Q4)	RTX 3090	44 tok/s
Code Llama 7B (Q4)	RTX 3090	48 tok/s
DeepSeek-Coder-V4 33B (Q4)	RTX 4090	18 tok/s
Qwen2.5-Coder 32B (Q4)	RTX 4090	19 tok/s

At 40+ tok/s, 7B models feel nearly instantaneous for autocomplete. Larger models sacrifice speed for quality but remain usable.

Quality comparison vs cloud

How do local coding models compare to cloud alternatives?

Task	DeepSeek-Coder-V4 (local)	GitHub Copilot (cloud)	Claude 3.5 (cloud)
Tab autocomplete	Good	Excellent	n/a
Function generation	Excellent	Good	Excellent
Code review	Good	Fair	Excellent
Refactoring	Very good	Fair	Excellent
Documentation	Very good	Fair	Excellent
Multi-file understanding	Good (128K)	Limited	Excellent (200K)
Privacy (code stays local)	Yes	No	No

Local models have closed the gap significantly. For most coding tasks, DeepSeek-Coder-V4 is competitive with cloud alternatives — and your code never leaves your machine.

When local coding AI beats cloud

• Proprietary codebases. Your source code is your intellectual property. Local AI keeps it private.
• Offline development. Air-gapped environments, remote sites, or unreliable internet.
• Frequent iterations. Local inference costs nothing per call. Run hundreds of code reviews without API bills.
• Compliance. Regulated industries (finance, defence, healthcare) often prohibit sending code to third-party APIs.
• Learning. Experiment with different models, quantisations, and prompts without rate limits or cost.

When you outgrow local — need team access, larger models, or production uptime — Plugsky gives you the same OpenAI-compatible API with 30+ frontier models. Change one line: base_url.

Frequently asked questions

Which local model is best for coding?

DeepSeek-Coder-V4 is the best overall local coding model. It matches GPT-4 level code generation on many benchmarks, supports 128K context, and runs efficiently on consumer GPUs. Qwen2.5-Coder is a close second, especially strong on Python.

Can a local coding model replace GitHub Copilot?

For inline autocomplete, cloud models like Copilot are still faster and more accurate. For full function generation, code review, refactoring, and documentation, local models like DeepSeek-Coder V4 running through Continue.dev are competitive — and your code stays on your machine.

How much GPU do I need for local coding AI?

A 7B model runs at ~40 tok/s on an RTX 3090/4090 (24 GB). For 13B models you need 16 GB VRAM. For 34B models you need 24 GB or use CPU offloading. Apple Silicon Macs with 16 GB+ unified memory run 7B models well via Metal acceleration.

How do I set up a local coding AI?

Install Ollama, pull a coding model (ollama pull deepseek-coder-v4), install the Continue VS Code extension, and configure it to point at your local Ollama endpoint. The entire setup takes about 10 minutes.

When should I use cloud AI instead of local for coding?

Use cloud AI when you need the absolute best code quality (GPT-4o, Claude 3.5), are working with very large codebases that exceed local context windows, or need team-wide access to the same AI assistant without managing hardware.