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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.
About the speed figure. The tokens/sec number above is an estimate, not a measured benchmark. Real throughput depends on your runtime and backend (Ollama, llama.cpp, vLLM), the exact quantization you download, context length, memory bandwidth, and whether any layers are offloaded to CPU. Treat it as a rough guide to the tier of performance, not a promised result.