Quick answer: For code completion and generation, an RTX 4060 Ti 16GB ($425) handles 7B code models well. For the best coding experience with 33-34B models, the RTX 4090 ($2,200) is the go-to pick.
NVIDIA GeForce RTX 4090
24GB GDDR6XRuns Qwen 2.5 Coder 32B at 20 tok/s — the best local code model at near-commercial quality.
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Why code LLMs have different GPU needs
Code LLMs work differently from general chat models. Code completion demands low latency for inline suggestions, fill-in-the-middle tasks use bidirectional context, and code generation with long outputs benefits from sustained throughput. Speed matters more here because you are waiting for suggestions while you type.
Popular code LLMs and their VRAM requirements
| Model | Parameters | Q4_K_M Size | Minimum VRAM | Strength |
|---|---|---|---|---|
| CodeLlama 7B | 7B | ~4.5GB | 8GB | Fast completions |
| CodeLlama 13B | 13B | ~7.5GB | 12GB | Better reasoning |
| CodeLlama 34B | 34B | ~20GB | 24GB | Complex code generation |
| DeepSeek Coder V2 Lite (16B) | 16B | ~9.5GB | 12GB | Strong multi-language |
| DeepSeek Coder V2 (236B MoE) | 236B | ~135GB | Multi-GPU | Near-GPT-4 coding |
| Qwen 2.5 Coder 7B | 7B | ~4.5GB | 8GB | Excellent for its size |
| Qwen 2.5 Coder 14B | 14B | ~8.5GB | 12GB | Great quality/size ratio |
| Qwen 2.5 Coder 32B | 32B | ~19GB | 24GB | Best local code model |
Qwen 2.5 Coder 32B and CodeLlama 34B are the standout models for serious local coding (the whole Qwen 2.5 lineup and what each size needs). Both need ~20GB at Q4_K_M, making the RTX 4090 the natural home. (As of June 2026, the new Qwen3-Coder-Next — 80B MoE with 3B active params, 256K context — has taken the SWE-rebench #1 spot; its hardware story is different from the dense models above and worth its own guide.)
GPU benchmarks for code LLMs
Speed benchmarks using Ollama with Q4_K_M quantization:
| GPU | Qwen Coder 7B | CodeLlama 13B | Qwen Coder 32B | Price |
|---|---|---|---|---|
| RTX 5090 | ~95 tok/s | ~55 tok/s | ~28 tok/s | ~$4,900 |
| RTX 4090 | ~65 tok/s | ~40 tok/s | ~20 tok/s | ~$2,200 |
| RTX 5080 | ~55 tok/s | ~32 tok/s | Needs offload | ~$1,400 |
| RTX 4070 Ti Super | ~40 tok/s | ~25 tok/s | Needs offload | ~$800 |
| RTX 4060 Ti 16GB | ~28 tok/s | ~18 tok/s | Needs offload | ~$425 |
| RTX 3060 12GB (used) | ~18 tok/s | ~12 tok/s | No | ~$250 |
For inline code completion, you want at least 30 tok/s to feel responsive. For longer code generation, 15-20 tok/s is acceptable.
Matching GPU to your coding workflow
Inline completion (Copilot-style): Latency is king. You need the first token fast. A 7B model on a fast GPU beats a 34B model on a slow GPU for this use case. The RTX 4070 Ti Super running Qwen Coder 7B at ~40 tok/s gives a snappy experience.
Code generation and refactoring: Quality matters more here. Larger models produce better code with fewer errors. Qwen 2.5 Coder 32B on an RTX 4090 at ~20 tok/s gives you near-commercial quality at reasonable speed.
Code review and explanation: Context length matters because you need to fit large code blocks into the prompt. 16GB cards handle 7-14B models with 8K+ context. For 32K context with 14B+ models, get a 24GB card.
Which GPU should you buy?
If you mainly do inline code completion (Copilot-style autocomplete), get the RTX 4060 Ti 16GB — a 7B model at 28 tok/s is fast enough for real-time suggestions and costs only $425. If you do code generation and refactoring where output quality matters more than latency, jump to the RTX 4090 — it runs Qwen Coder 32B at 20 tok/s, which is the best local code model available. If budget is not a concern and you want the fastest possible coding experience, the RTX 5090 is the only card that runs 32B code models above 25 tok/s.
Common mistakes to avoid
- Buying a 12GB card for code LLMs. Code models with long context windows (8K-16K tokens for full file context) eat more VRAM than chat models. 12GB gets tight fast — 16GB is the real minimum.
- Choosing a bigger model over a faster GPU. For inline completion, a 7B model at 40 tok/s produces better workflow than a 34B model at 12 tok/s. Speed matters more than quality for autocomplete.
- Ignoring context length requirements. Code tasks often need the full file (or multiple files) in context. A model that fits in VRAM but leaves no room for KV cache will truncate your code context and give worse suggestions.
- Running FP16 when Q4_K_M is fine. For code completion, Q4_K_M quantization produces nearly identical suggestions to FP16. Save the VRAM for longer context instead.
Our recommendation
| Workflow | Best Model | Best GPU | Price |
|---|---|---|---|
| Fast completions on a budget | Qwen Coder 7B | RTX 4060 Ti 16GB | ~$425 |
| Balanced coding assistant | Qwen Coder 14B | RTX 4070 Ti Super | ~$800 |
| Best local coding experience | Qwen Coder 32B | RTX 4090 | ~$2,200 |
| Maximum quality | Qwen Coder 32B | RTX 5090 | ~$4,900 |
The RTX 4090 running Qwen 2.5 Coder 32B is the best local coding setup in 2026. It fits the model at Q4_K_M with room for long context windows and delivers usable generation speed. If you are on a budget, the RTX 4060 Ti 16GB with a 7B code model still beats cloud-dependent tools for privacy and latency.
Check NVIDIA GeForce RTX 4060 Ti 16GB on Amazon→Buy on Shopee SG→ Check NVIDIA GeForce RTX 4090 on Amazon→Buy on Shopee SG→NVIDIA GeForce RTX 5090
32GB GDDR7Only card that runs 32B code models above 25 tok/s — fastest inline completion and generation experience.
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For more on how much VRAM these models actually consume in practice, see our VRAM requirements guide. If you prefer running code models through Ollama, all these GPUs work great with it out of the box. Connecting those models to your editor? See our best GPU for Continue.dev guide for VS Code and JetBrains extension-specific advice — and for a workflow-level walkthrough of pairing a coding model to a developer setup, see our best GPU for a local coding LLM guide.