Running a local AI coding assistant is one of the most practical GPU use cases you can have. Your completions stay private, there is no subscription fee after the hardware purchase, and with the right setup you can match GitHub Copilot for routine code tasks. The question is what GPU actually makes that practical.
Quick answer: The RTX 4090 (24GB) is the top pick for a local coding assistant — it runs 34B code models at usable speeds. For most developers, the RTX 4060 Ti 16GB ($425) running 13B models is the better value. On a tight budget, the RTX 3060 12GB handles 7B code models well.
NVIDIA GeForce RTX 4090
24GB GDDR6X24GB VRAM runs DeepSeek Coder 33B and Qwen Coder 32B at full precision — the fastest local code completion experience
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Which code models should you run?
The model determines your GPU requirements more than anything else. Code LLMs come in a range of sizes:
| Model | Size | Min VRAM | Quality |
|---|---|---|---|
| Qwen2.5-Coder 1.5B | 1.5B | 4GB | Basic completions |
| DeepSeek-Coder-V2-Lite | 2.4B | 6GB | Good for simple tasks |
| Qwen2.5-Coder 7B | 7B | 8GB | Strong everyday use |
| DeepSeek-Coder 7B | 7B | 8GB | Solid completions |
| Qwen2.5-Coder 14B | 14B | 12GB | Near-Copilot quality |
| DeepSeek-Coder-V2 | 16B | 12GB (Q4) | Very capable |
| Qwen2.5-Coder 32B | 32B | 20GB (Q4) | Excellent, approaches GPT-4 quality |
| DeepSeek-Coder 33B | 33B | 24GB (Q4) | Top local code model |
For context: GitHub Copilot and Cursor run GPT-4-class models. To approach that quality locally, you need 32B+ models, which means 24GB VRAM.
GPU recommendations by use case
RTX 4090 — runs 34B code models
The RTX 4090’s 24GB VRAM is the key. You can load DeepSeek-Coder 33B at 4-bit quantization and still have VRAM headroom. Token generation is fast enough for interactive coding — expect 20-30 tokens/second on Q4 models at 33B, which feels responsive in Continue.dev or a similar IDE integration.
This card also handles the broader AI workloads you might layer in — running your own embedding model, a coding model, and local vector search simultaneously.
Check NVIDIA GeForce RTX 4090 on Amazon→Buy on Shopee SG→RTX 4060 Ti 16GB — the value sweet spot
At around $425, the RTX 4060 Ti 16GB is purpose-built for this use case. It runs 13B models at full speed and 32B models via Q4 quantization if you can tolerate slower inference. For most developers working with Python, TypeScript, or Rust in everyday projects, a well-configured 14B model (Qwen2.5-Coder 14B) handles 90% of Copilot-style completions.
The 16GB version specifically matters here — avoid the 8GB RTX 4060 Ti if you plan to run 14B+ models.
NVIDIA GeForce RTX 4060 Ti 16GB
16GB GDDR616GB VRAM at ~$425 runs Qwen2.5-Coder 14B comfortably — the best dollar-per-token GPU for local coding
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RTX 3060 12GB — budget starting point
The RTX 3060 12GB (~$250 used) runs 7B code models without issue. DeepSeek-Coder 7B and Qwen2.5-Coder 7B both load cleanly, and token speeds are fast enough for autocomplete. For general AI use on a budget this card punches above its weight class.
The limitation: 12GB is not enough for 13B+ models in practice. You are capped at 7B unless you use aggressive quantization (Q2/Q3), which noticeably degrades code quality.
Check NVIDIA GeForce RTX 3060 12GB on Amazon→Buy on Shopee SG→Setting up Continue.dev with Ollama
Continue.dev is the best open-source Copilot replacement. The setup is straightforward:
- Install Ollama and pull your model:
ollama pull qwen2.5-coder:14b - Install the Continue.dev extension in VS Code or JetBrains
- Configure
config.jsonto point at your local Ollama endpoint - Set the model for completions vs. chat separately — a smaller 7B model for fast completions, a 14B/32B for longer chat-style reasoning
Continue.dev also supports Codeium’s free tier as an alternative backend, so you can run hybrid local/cloud depending on task.
Codeium, DeepSeek Coder, and Qwen Coder: which to run?
Codeium: Free cloud service with a local inference option. Does not require your GPU if you use their servers. If your goal is truly private local inference, the self-hosted path is available but complex.
DeepSeek Coder (7B, 33B): Strong for Python and system languages. The 33B model is competitive with Copilot on algorithmic tasks. DeepSeek-Coder-V2 at 16B is a strong middle ground.
Qwen2.5-Coder: Currently the top open-source family for code. The 32B model is routinely rated above GPT-4o on coding benchmarks. Runs well with Ollama.
For a full comparison of code-generation performance by model and GPU, see the codegen AI GPU guide.
Which GPU should YOU buy?
- You want full Copilot replacement (32B+ models): RTX 4090. No other consumer card runs these models at practical speeds with 24GB.
- You want solid 13B-14B model inference for daily coding: RTX 4060 Ti 16GB. Best value for the use case.
- You want basic 7B completions on a tight budget: RTX 3060 12GB. Works well for everyday autocomplete.
- You already have an 8GB card: You are limited to 7B models with Q4 quantization. Consider upgrading the VRAM before the model.
- You need to understand how much VRAM your model actually needs: Read the VRAM guide before buying.
Common mistakes to avoid
- Buying the 8GB RTX 4060 Ti instead of the 16GB version. The 8GB version is fine for gaming but will limit you to small code models. The 16GB version is a completely different proposition for local AI coding.
- Using a 7B model and concluding local AI coding does not work. 7B models are noticeably weaker than Copilot. Try a 14B or 32B model before writing off local inference.
- Running completions and chat with the same heavyweight model. Use a fast 7B for tab completions and reserve the 32B for chat-style problem solving. This dramatically improves the completion latency.
- Not trying quantized models. Q4 quantization of a 33B model typically outperforms a full-precision 7B model on code tasks. Quantize down in size, not quality tier.
Final verdict
| Scenario | GPU |
|---|---|
| Best local coding assistant | RTX 4090 (24GB) |
| Best value for 14B models | RTX 4060 Ti 16GB |
| Budget 7B inference | RTX 3060 12GB |
| No-compromise 32B+ models | RTX 4090 or RTX 5090 |
Running local code LLMs is now fast enough to be genuinely useful. The hardware bar is lower than you might expect — an RTX 4060 Ti 16GB and Ollama gets you within striking distance of paid Copilot, with no subscription and no data leaving your machine.