Can I Run DeepSeek-OCR on RTX 3090 Desktop (24 GB VRAM, 64 GB RAM)?
Written by Jakub Rusinowski · Last updated September 11, 2026
Yes — comfortably
Yes, comfortably — DeepSeek-OCR 3B at Q8_0 needs about 4.8 GB of the 24 GB usable on RTX 3090 Desktop (24 GB VRAM, 64 GB RAM), leaving ~19.2 GB spare and running at ~269.8 tok/s (estimated), with room for about 8,192 tokens of context.
Confidence: medium · Recommended quantization: Q8_0 · Estimated speed: ~269.8 tok/s
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RTX 3090 Desktop (24 GB VRAM, 64 GB RAM) — what it gives a model
| Usable memory for models | 24 GB |
| Memory bandwidth | 936 GB/s |
| Form factor | Desktop |
| Operating system | Windows or Linux |
| Memory upgradeable | Yes |
DeepSeek-OCR on RTX 3090 Desktop (24 GB VRAM, 64 GB RAM): memory by quantization
| Quant | Memory needed | Fits 24 GB? | Max context | Est. speed | Download |
|---|---|---|---|---|---|
| F16 | 7.6 GB | ✓ Yes | 8K | ~223.8 tok/s | 6 GB |
| Q8_0 | 4.8 GB | ✓ Yes | 8K | ~269.8 tok/s | 3.2 GB |
| Q6_K | 4.1 GB | ✓ Yes | 8K | ~285 tok/s | 2.5 GB |
| Q5_K_M | 3.8 GB | ✓ Yes | 8K | ~292.5 tok/s | 2.1 GB |
| Q4_K_M | 3.5 GB | ✓ Yes | 8K | ~300 tok/s | 1.8 GB |
| Q3_K_M | 2.9 GB | ✓ Yes | 8K | ~313.5 tok/s | 1.3 GB |
| Q2_K | 2.6 GB | ✓ Yes | 8K | ~321.5 tok/s | 1 GB |
What to watch out for
- This model does not publish its full attention configuration, so the KV-cache share of these figures is inferred from its parameter count rather than computed exactly.
RTX 3090 desktop limitations
- The cheapest route to 24 GB of VRAM, and the standard used-market recommendation for local LLMs.
- Older architecture: no FP8 acceleration, and higher idle power than a current card.
Recommended setup
Ollama or llama.cpp (CUDA/ROCm) — vLLM if you need concurrent serving
How these numbers are calculated
- Memory need = quantized weights + KV cache at 8,192 tokens (f16) + 0.8 GB runtime overhead.
- 24 GB of VRAM on the NVIDIA GeForce RTX 3090 at 936 GB/s.
- 64 GB of system RAM available for CPU offload when a model exceeds VRAM.
- Throughput is a memory-bandwidth roofline estimate, not a measurement. It is labelled "estimated" everywhere it appears.
- KV cache is inferred from the parameter count because this model does not publish its full attention configuration.
FAQ
Can I run DeepSeek-OCR on RTX 3090 Desktop (24 GB VRAM, 64 GB RAM)?
Yes, comfortably — DeepSeek-OCR 3B at Q8_0 needs about 4.8 GB of the 24 GB usable on RTX 3090 Desktop (24 GB VRAM, 64 GB RAM), leaving ~19.2 GB spare and running at ~269.8 tok/s (estimated), with room for about 8,192 tokens of context.
Which quantization of DeepSeek-OCR should I use on RTX 3090 Desktop (24 GB VRAM, 64 GB RAM)?
Q8_0 — it needs about 4.8 GB of the 24 GB available, downloads as roughly 3.2 GB, and runs at an estimated 269.8 tokens/sec with up to 8K of context.
What limits DeepSeek-OCR on RTX 3090 Desktop (24 GB VRAM, 64 GB RAM)?
Nothing binding — the model fits with headroom and generates at a usable speed on this hardware.
Which runtime should I use?
Ollama or llama.cpp (CUDA/ROCm) — vLLM if you need concurrent serving
Other Computers
Other Models on RTX 3090 Desktop (24 GB VRAM, 64 GB RAM)
- DeepSeek R1 on RTX 3090 Desktop (24 GB VRAM, 64 GB RAM)
- Devstral on RTX 3090 Desktop (24 GB VRAM, 64 GB RAM)
- EXAONE 3.5 on RTX 3090 Desktop (24 GB VRAM, 64 GB RAM)
- Falcon 3 on RTX 3090 Desktop (24 GB VRAM, 64 GB RAM)
- Gemma 2 Family on RTX 3090 Desktop (24 GB VRAM, 64 GB RAM)
DeepSeek-OCR on GPUs
- DeepSeek-OCR on NVIDIA GeForce RTX 5060 Ti 8GB
- DeepSeek-OCR on NVIDIA GeForce RTX 5060
- DeepSeek-OCR on NVIDIA GeForce RTX 4060
What This Model Is Good At
Model & Tools
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