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DeepSeek Coder V2 33B needs 19.5 GB VRAM at Q4. The RTX 5090 32GB has 32 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 5090 32GB’s 32 GB of VRAM can hold it — here’s the exact math.
VRAM requirements
| Quantization | VRAM needed | RTX 5090 32GB |
|---|---|---|
| Q4_K_M (recommended) | 19.5 GB | ✅ |
| Q8_0 (near-lossless) | 34 GB | ❌ |
Expected performance
Running DeepSeek Coder V2 33B at Q4 on the RTX 5090 32GB, expect roughly ~31 tokens/sec in Ollama or llama.cpp — comfortably faster than reading speed, so chat feels responsive.
Headroom: 19.5 GB used of 32 GB leaves about 12.5 GB free for KV cache (context). That’s fine for 4-8K context; for 32K+ you may need a smaller batch or a lower quant.
About the RTX 5090 32GB
Architecture: Blackwell · VRAM: 32 GB · Price: ~$2,000+.
Good: 32GB + Blackwell speed — runs 70B at Q4 on a single card
Watch out: Very expensive, high power draw, hard to buy at MSRP
Check current RTX 5090 32GB price on Amazon →
Related checks
Other models on the RTX 5090 32GB
🎯 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 5090 32GB run DeepSeek Coder V2 33B?
Yes — runs great at Q4 (recommended). DeepSeek Coder V2 33B needs 19.5 GB VRAM at Q4_K_M; the RTX 5090 32GB has 32 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 5090 32GB get on DeepSeek Coder V2 33B?
Roughly ~31 tokens/sec at Q4 in Ollama or llama.cpp.
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.