The RTX 4070 Ti Super is the best GPU for local AI code generation. Its 16GB VRAM loads Codestral 22B quantized, DeepSeek Coder 6.7B at full precision, and StarCoder2 15B at Q4 — all fast enough for real-time autocomplete.
NVIDIA GeForce RTX 4070 Ti Super
16GB GDDR6X16GB VRAM loads Codestral 22B quantized and DeepSeek Coder 6.7B at full precision for snappy real-time autocomplete.
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Who this is for
You want Copilot-quality code suggestions running entirely on your hardware. No API calls, no subscription fees, no sending proprietary code to external servers. Local code generation models need fast inference because every keystroke waits for predictions. If you also want a general-purpose local AI assistant beyond code, see our best GPU for AI assistant guide.
GPU comparison for code generation
| GPU | VRAM | DeepSeek 6.7B | Codestral 22B Q4 | StarCoder2 15B Q4 | Price |
|---|---|---|---|---|---|
| RTX 5090 | 32GB | ~60 tok/s | ~30 tok/s | ~35 tok/s | ~$4,900 |
| RTX 4090 | 24GB | ~52 tok/s | ~26 tok/s | ~30 tok/s | ~$2,200 |
| RTX 5080 | 16GB | ~45 tok/s | ~20 tok/s | ~24 tok/s | ~$1,400 |
| RTX 4070 Ti Super | 16GB | ~38 tok/s | ~17 tok/s | ~20 tok/s | ~$800 |
| RTX 5070 Ti | 16GB | ~40 tok/s | ~18 tok/s | ~22 tok/s | ~$1,050 |
| RTX 4060 Ti 16GB | 16GB | ~26 tok/s | ~12 tok/s | ~14 tok/s | ~$425 |
Speeds are approximate for Q4_K_M quantization. Real autocomplete latency depends on prompt length and model architecture.
Check NVIDIA GeForce RTX 5080 on Amazon→Buy on Shopee SG→Which GPU should you buy?
- Budget ($425)? The RTX 4060 Ti 16GB runs smaller code models well enough for autocomplete. Response times are noticeable but workable.
- Sweet spot ($700-750)? The RTX 4070 Ti Super or RTX 5070 Ti. Both have 16GB VRAM and fast enough inference that autocomplete feels snappy. I personally run this tier for daily coding.
- Maximum speed ($2,200)? The RTX 4090 loads larger models and generates faster. Worth it if you use 33B code models or want instant fill-in-the-middle completions.
Common mistakes to avoid
- Using models too large for your VRAM — a 33B model that offloads to CPU will autocomplete slower than a 7B model that fits entirely in VRAM. Smaller and faster beats bigger and slower for code generation.
- Skipping quantization — Q4 and Q5 quantized code models perform nearly identically to FP16 on coding benchmarks. There is no reason to waste VRAM on full precision.
- Forgetting context length — code generation eats context fast. A 16K context window on a 22B model needs more VRAM than a 4K context on the same model. Plan your VRAM around realistic context sizes.
- Buying for training when you only need inference — code generation is an inference workload. You do not need a $2,000 GPU unless you are fine-tuning.
Final verdict
| Use Case | Best GPU | Why |
|---|---|---|
| Budget autocomplete | RTX 4060 Ti 16GB | 16GB loads 7B-15B models |
| Daily coding assistant | RTX 4070 Ti Super | Fast inference, good VRAM |
| Pro developer (large models) | RTX 4090 | 24GB for 33B models |
| Fine-tuning code models | RTX 5090 | 32GB for training |
NVIDIA GeForce RTX 4070 Ti Super
16GB GDDR6XThe daily driver for local code generation — fast enough for fill-in-the-middle completions without the flagship price.
Affiliate links — we may earn a commission at no extra cost to you. Amazon ships globally; Shopee SG covers Singapore & ASEAN.
If you also train models with PyTorch, you may want more VRAM. For broader AI workloads beyond code, see the best GPU for deep learning guide. For a complete setup guide covering IDE integration, Ollama, and model selection for a local coding assistant, see our best GPU for local AI coding guide.
Autocomplete speed matters more than benchmark scores for code generation. A GPU that generates 30+ tokens per second feels instant. Anything below 15 tokens per second starts to break your flow.