Can I Run Llama 3.3 on RTX 5090 Desktop (32 GB VRAM, 64 GB RAM)?
Autor: Jakub Rusinowski · Ostatnia aktualizacja: 8 grudnia 2024
Yes
Yes — Llama 3.3 70B Instruct at Q2_K needs about 26.5 GB of the 32 GB usable on RTX 5090 Desktop (32 GB VRAM, 64 GB RAM) (~5.5 GB spare), at ~49.1 tok/s (estimated), with room for about 16,384 tokens of context.
Confidence: high · Recommended quantization: Q2_K · Estimated speed: ~49.1 tok/s
See what else this hardware can run →
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RTX 5090 Desktop (32 GB VRAM, 64 GB RAM) — what it gives a model
| Usable memory for models | 32 GB |
| Memory bandwidth | 1792 GB/s |
| Form factor | Desktop |
| Operating system | Windows or Linux |
| Memory upgradeable | Yes |
Llama 3.3 on RTX 5090 Desktop (32 GB VRAM, 64 GB RAM): memory by quantization
| Quant | Memory needed | Fits 32 GB? | Max context | Est. speed | Download |
|---|---|---|---|---|---|
| F16 | 143.5 GB | ✗ No | — | — | 140 GB |
| Q8_0 | 77.9 GB | ✗ No | — | — | 74.4 GB |
| Q6_K | 60.9 GB | ✗ No | — | — | 57.4 GB |
| Q5_K_M | 53.1 GB | ✗ No | — | — | 49.6 GB |
| Q4_K_M | 45.7 GB | ✗ No | — | — | 42.3 GB |
| Q3_K_M | 33.3 GB | ✗ No | — | — | 29.8 GB |
| Q2_K | 26.5 GB | ✓ Yes | 16K | ~49.1 tok/s | 23 GB |
What to watch out for
- Q2_K is the only quantization that fits, and it is a heavily degraded one — expect noticeably worse output than the same model at Q4_K_M. A smaller model at Q4 is usually the better trade.
- 1 larger variant of Llama 3.3 does not fit and would need CPU offload or different hardware.
RTX 5090 desktop limitations
- 575 W board power — budget for a 1000 W+ PSU and the heat it puts into the room.
- Models larger than 32 GB must offload to system RAM, which costs roughly an order of magnitude in speed.
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.
- 32 GB of VRAM on the NVIDIA GeForce RTX 5090 at 1792 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 computed from this model's published attention configuration.
FAQ
Can I run Llama 3.3 on RTX 5090 Desktop (32 GB VRAM, 64 GB RAM)?
Yes — Llama 3.3 70B Instruct at Q2_K needs about 26.5 GB of the 32 GB usable on RTX 5090 Desktop (32 GB VRAM, 64 GB RAM) (~5.5 GB spare), at ~49.1 tok/s (estimated), with room for about 16,384 tokens of context.
Which quantization of Llama 3.3 should I use on RTX 5090 Desktop (32 GB VRAM, 64 GB RAM)?
Q2_K — it needs about 26.5 GB of the 32 GB available, downloads as roughly 23 GB, and runs at an estimated 49.1 tokens/sec with up to 16K of context.
What limits Llama 3.3 on RTX 5090 Desktop (32 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 Models on RTX 5090 Desktop (32 GB VRAM, 64 GB RAM)
- Magistral Small on RTX 5090 Desktop (32 GB VRAM, 64 GB RAM)
- MiniCPM-V on RTX 5090 Desktop (32 GB VRAM, 64 GB RAM)
- Ministral on RTX 5090 Desktop (32 GB VRAM, 64 GB RAM)
- Ministral 3 on RTX 5090 Desktop (32 GB VRAM, 64 GB RAM)
- Mistral Family on RTX 5090 Desktop (32 GB VRAM, 64 GB RAM)
Llama 3.3 on GPUs
- Llama 3.3 on NVIDIA GeForce RTX 5090
- Llama 3.3 on NVIDIA GeForce RTX 5080
- Llama 3.3 on NVIDIA GeForce RTX 5070 Ti
- Llama 3.3 on NVIDIA GeForce RTX 5070
What This Model Is Good At
Model & Tools
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