Best GPU for Continue.dev (Local AI Coding) in 2026

From the Best GPU for LLM archive. The canonical version has interactive calculators, an up-to-date GPU comparison table, and live pricing.

Getting tired of pasting code into a web browser and hoping the AI provider doesn't train on it? Continue.dev solves that — it's a VS Code and JetBrains plugin that routes AI completions through a local LLM backend. No API key, no cloud, no data leaving your machine. The GPU you pair it with determines whether you get a genuinely useful coding assistant or a frustrating one.

Quick answer: The RTX 4060 Ti 16GB ($400) is the best value for Continue.dev — it handles 14B code models well, and 14B is the sweet spot for quality autocomplete. Power users who want 33B model quality should get the RTX 4090.

How Continue.dev uses your GPU

Continue.dev doesn't do inference directly — it talks to a backend like Ollama, llama.cpp, or LM Studio running on your machine. The backend does the actual inference; Continue sends the code context and receives completions.

This matters because:

Autocomplete (fill-in-the-middle) needs low latency — first token within 1-2 seconds
Chat (asking questions about code) can tolerate 2-4 second delays
Context length matters — you may send entire files or multi-file context windows

For inline autocomplete to feel like Copilot, you need at least 25-30 tok/s from your backend. For chat, 15 tok/s is acceptable.

Best models for Continue.dev by use case

Model	Size	VRAM (Q4_K_M)	Speed (4090)	Best For
Qwen 2.5 Coder 7B	7B	~5GB	~65 tok/s	Fast autocomplete
Qwen 2.5 Coder 14B	14B	~9GB	~38 tok/s	Balanced quality + speed
Qwen 2.5 Coder 32B	32B	~19GB	~20 tok/s	Best local code quality
DeepSeek Coder V2 Lite (16B)	16B	~10GB	~32 tok/s	Strong reasoning
CodeLlama 34B	34B	~21GB	~18 tok/s	Good context understanding

The 14B sweet spot: Qwen 2.5 Coder 14B at ~38 tok/s on a 4090 gives you fast enough autocomplete AND good code quality. On an RTX 4060 Ti 16GB, it runs at ~22 tok/s — still workable for autocomplete.

GPU recommendations by budget

Budget: RTX 3060 12GB (~$250 used)

Runs 7B code models at Q4_K_M at around 18-20 tok/s. Autocomplete works but there is a noticeable lag. The 7B model quality means more suggestions need manual correction. Works for occasional use, frustrating as a daily driver.

Value: RTX 4060 Ti 16GB (~$400)

The real minimum for a good Continue.dev experience. The 16GB VRAM runs Qwen 2.5 Coder 14B at Q4_K_M at ~22 tok/s — fast enough for autocomplete to feel responsive. 14B quality gives useful completions with fewer edits. This is the recommendation for most developers.

Sweet spot: RTX 4070 Ti Super (~$700)

16GB VRAM, faster memory bandwidth than the 4060 Ti 16GB. Runs 14B at ~28 tok/s and handles 32B models with some CPU offload. A noticeable step up in responsiveness for autocomplete, especially for developers who keep Continue.dev running all day.

Best: RTX 4090 (~$1,600)

24GB VRAM runs Qwen 2.5 Coder 32B at Q4_K_M with 5GB to spare. At ~20 tok/s, the 32B model produces output that frequently requires zero editing — suggestions are syntactically and semantically correct on first try. For developers where code quality directly affects productivity, this pays for itself.

GPU tier list available at the original article

Which GPU should YOU buy?

Occasional coding assistant or hobby projects: The RTX 3060 12GB at ~$250 used runs 7B models adequately. Expect some latency and manual correction of suggestions.

Daily driver for professional development: The RTX 4060 Ti 16GB at $400 is the right call. 14B at 22 tok/s is fast enough that autocomplete stops feeling like waiting, and 14B quality is genuinely useful.

Power user or polyglot developer (multiple languages, complex codebases): Jump to the RTX 4090. The 32B model quality is a step change — fewer wrong completions, better multi-file reasoning, and it handles the long context windows that large codebases require.

Team deployment (running a shared backend): Consider two RTX 4090s or look at cloud GPU options for serving multiple developers.

Setting up Continue.dev with Ollama

Continue.dev works out of the box with Ollama:

Install Ollama: curl -fsSL https://ollama.com/install.sh | sh
Pull a code model: ollama pull qwen2.5-coder:14b
Install the Continue.dev VS Code extension
In Continue config, set provider to ollama and model to qwen2.5-coder:14b

Ollama automatically detects your GPU and runs inference on it. For autocomplete specifically, set a smaller, faster model (7B) in the Continue autocomplete config and use the larger model (14B/32B) for chat — this gives you fast suggestions without sacrificing chat quality.

Common mistakes to avoid

Using a 12GB card and expecting 14B models to feel fast. 12GB technically fits 14B at Q4_K_M (~9GB) but leaves minimal headroom for context. You'll see slowdowns when your code context grows. Budget for 16GB minimum.
Picking the model before the GPU. Decide what quality you need, then buy the GPU that runs that model at acceptable speed — not the other way around.
Running autocomplete and chat with the same large model. Set autocomplete to a fast 7B model in Continue settings and reserve the larger model for explicit chat. The latency difference is massive for everyday use.
Ignoring context length. When you enable "full codebase context" in Continue, it can send 8K-32K tokens per request. A model that fits in VRAM but leaves no room for the KV cache will silently truncate your context and give worse answers.
Assuming AMD works the same. Continue.dev with Ollama works on AMD GPUs, but ROCm support is patchy on older cards. If you're on AMD, check Ollama's ROCm compatibility list before buying.

Final verdict

GPU	Best Model	Autocomplete Speed	Daily Driver?	Price
RTX 3060 12GB	Qwen Coder 7B	~18 tok/s	Barely	~$250
RTX 4060 Ti 16GB	Qwen Coder 14B	~22 tok/s	Yes	~$400
RTX 4070 Ti Super	Qwen Coder 14B	~28 tok/s	Great	~$700
RTX 4090	Qwen Coder 32B	~20 tok/s	Best	~$1,600

For most developers, the RTX 4060 Ti 16GB hits the right balance. It runs a genuinely capable 14B code model fast enough to feel like Copilot, costs $400, and uses reasonable power. Step up to the RTX 4090 if you work in complex, multi-file codebases where suggestion quality matters more than raw speed.

For more on running local code models, see the best GPU for code LLMs guide and the best GPU for Ollama. If you're exploring other local AI coding tools, best GPU for local coding LLM covers the broader landscape.