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DeepSeek Coder V2 33B needs 19.5 GB VRAM at Q4. The RTX 4070 Ti Super 16GB has 16 GB.
🔍 DeepSeek Coder V2 33B (DeepSeek) is a 33B-parameter model for code generation and analysis. Top-tier code model, competitive with GPT-4 for programming. The question is whether the RTX 4070 Ti Super 16GB’s 16 GB of VRAM can hold it — here’s the exact math.
VRAM requirements
| Quantization | VRAM needed | RTX 4070 Ti Super 16GB |
|---|---|---|
| Q4_K_M (recommended) | 19.5 GB | ❌ |
| Q8_0 (near-lossless) | 34 GB | ❌ |
Why it won’t fit (and what to do)
DeepSeek Coder V2 33B needs 19.5 GB at Q4 but the RTX 4070 Ti Super 16GB has only 16 GB — you’re 3.5 GB short. You can offload layers to system RAM, but expect single-digit tokens/sec (painfully slow for chat).
About the RTX 4070 Ti Super 16GB
Architecture: Ada · VRAM: 16 GB · Price: ~$800.
Good: 16GB with a wide bus — fast for 14B and squeezes 27B at Q4
Watch out: Pricey for 16GB; 24GB cards are better value for big models
Check current RTX 4070 Ti Super 16GB price on Amazon →
Related checks
Other models on the RTX 4070 Ti Super 16GB
🎯 LLM Hardware Checker
Enter your exact GPU + RAM, see every model you can run.
💾 VRAM Requirements
Exact VRAM per model at Q4/Q5/Q8/FP16 with context scaling.
Frequently asked questions
Can the RTX 4070 Ti Super 16GB run DeepSeek Coder V2 33B?
No — not enough VRAM without CPU offloading. DeepSeek Coder V2 33B needs 19.5 GB VRAM at Q4_K_M; the RTX 4070 Ti Super 16GB has 16 GB.
How much VRAM does DeepSeek Coder V2 33B need?
DeepSeek Coder V2 33B needs about 19.5 GB at Q4_K_M (recommended) and 34 GB at Q8_0.
How many tokens per second will the RTX 4070 Ti Super 16GB get on DeepSeek Coder V2 33B?
It won't fit in VRAM; with CPU offloading expect very slow single-digit tokens/sec.