GitHub Copilot costs $10-19/month and sends your code to the cloud. A local coding LLM costs nothing per query, runs offline, and never leaves your machine. The RTX 4090 is the best GPU for a full-featured local coding setup — it fits DeepSeek Coder 33B, the most capable open code model, at Q4. For most developers, the RTX 4060 Ti 16GB at $425 handles 7B-14B code models well enough to replace Copilot.
NVIDIA GeForce RTX 4090
24GB GDDR6X24GB VRAM fits DeepSeek Coder 33B at Q4_K_M. The only consumer GPU that handles the best open-source code model without compromise.
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Code LLMs worth running locally
| Model | Params | VRAM (Q4) | Strengths |
|---|---|---|---|
| DeepSeek Coder V2 Lite | 16B | ~9.5GB | Best 16B code model, strong on multi-file |
| DeepSeek Coder 33B | 33B | ~20GB | Near GPT-4 Turbo on HumanEval |
| Qwen2.5 Coder 7B | 7B | ~4.5GB | Fast, good for autocomplete |
| Qwen2.5 Coder 14B | 14B | ~9GB | Balanced quality and speed |
| CodeLlama 34B | 34B | ~21GB | Strong on code completion |
| Phi-4 14B | 14B | ~9GB | Excellent at reasoning over code |
DeepSeek Coder 33B scores 79.3 on HumanEval — competitive with GPT-3.5 and ahead of most open models. It is the target for users who want a genuine Copilot replacement.
GPU recommendations by use case
Full Copilot replacement (33B models)
For DeepSeek Coder 33B or CodeLlama 34B, you need 24GB VRAM. These models use ~20-21GB at Q4_K_M, leaving minimal room on anything smaller.
Best: RTX 4090 (24GB) at ~$2,200. Delivers ~18 tok/s on DeepSeek Coder 33B — fast enough for interactive code generation.
Budget 24GB alternative: Used RTX 3090 (~$900). Slightly faster bandwidth than the 4090 on memory-bound tasks. Excellent for code LLM work.
Check NVIDIA GeForce RTX 3090 on Amazon→Buy on Shopee SG→7B-14B code models (best value tier)
Qwen2.5 Coder 14B and DeepSeek Coder V2 Lite both run well on 16GB. They cover:
- Autocomplete as you type
- Function and class generation
- Code explanation and review
- Refactoring suggestions
Best: RTX 4060 Ti 16GB (~$425). Runs DeepSeek Coder V2 Lite at Q4_K_M (~9.5GB) with 35 tok/s — fast enough for real-time autocomplete in Continue.dev. For a deeper look at pairing hardware with that specific extension, see our best GPU for Continue.dev guide.
NVIDIA GeForce RTX 4060 Ti 16GB
16GB GDDR6Runs all 7B-14B code models. Fast enough for real-time autocomplete via Continue.dev. The developer daily driver at $425.
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Entry level / 7B only
If budget is tight, an RTX 3060 12GB (~$250 used) runs Qwen2.5 Coder 7B at full precision and DeepSeek Coder V2 Lite at Q4. Inference is around 25 tok/s on 7B models — usable, not blazing.
Setting up a local Copilot with Continue.dev
Continue.dev is the open-source VS Code / JetBrains extension that connects to local Ollama models. Setup in three steps:
- Install Ollama and pull your model:
ollama pull deepseek-coder-v2:16b - Install the Continue extension in VS Code
- Configure the model endpoint in Continue’s
config.json
Once running, you get tab autocomplete, inline edits, and chat — identical to Copilot’s feature set, all local. Codeium’s self-hosted option and LM Studio are alternatives if you prefer a GUI.
Which GPU should YOU buy?
Want a true Copilot replacement with 33B model quality? RTX 4090 ($2,200) or used RTX 3090 (~$820). Both fit DeepSeek Coder 33B at Q4_K_M with comfortable headroom.
Daily driver for autocomplete and code chat (14B)? RTX 4060 Ti 16GB ($425). Runs DeepSeek Coder V2 Lite and Qwen2.5 Coder 14B smoothly — genuinely usable as a Copilot replacement.
Just want to try it without spending much? Used RTX 3060 12GB ($250). Handles 7B code models well. Outgrow it fast if you use it seriously.
Need 33B but can’t afford the hardware? Cloud inference via RunPod gives you on-demand access to larger models without the upfront cost.
Common mistakes to avoid
- Running a 7B code model and expecting GPT-4 quality. Qwen2.5 Coder 7B and CodeLlama 7B are capable but not GPT-4. For complex multi-file tasks, you need 14B-33B.
- Buying an 8GB card for code work. DeepSeek Coder V2 Lite (16B) won’t fit. Even quantized to Q3, you will have quality issues. 16GB is the minimum for serious code LLM use.
- Ignoring latency requirements. Autocomplete needs 20+ tok/s to feel natural. If your GPU delivers 8 tok/s, you will keep turning the feature off. Benchmark before committing to a model.
- Skipping context length configuration. Code tasks need large context — 8K minimum, 32K ideal. Configure Ollama’s
num_ctxparameter or you will get truncated completions on large files.
Final verdict
| Goal | GPU | Price |
|---|---|---|
| DeepSeek Coder 33B (best quality) | RTX 4090 | ~$2,200 |
| DeepSeek Coder 33B (best value) | RTX 3090 (used) | ~$900 |
| 14B code models (daily driver) | RTX 4060 Ti 16GB | ~$425 |
| 7B code models (budget entry) | RTX 3060 12GB (used) | ~$250 |
A local coding LLM on an RTX 4060 Ti 16GB running DeepSeek Coder V2 Lite costs $425 once and nothing per month. At $10-19/month for Copilot, that pays off in under four years — and you get privacy and offline access from day one.
For more on running DeepSeek models locally, see our DeepSeek GPU guide. If you want the broader code LLM landscape, the code LLM GPU guide covers more models. Running through Ollama? The Ollama GPU guide has setup tips.