Can I Run Gemma 3n on RTX 5090 Desktop (32 GB VRAM, 64 GB RAM)?
Written by Jakub Rusinowski · Last updated April 1, 2025
Yes — comfortably
Yes, comfortably — Gemma 3n E4B at Q8_0 needs about 10.3 GB of the 32 GB usable on RTX 5090 Desktop (32 GB VRAM, 64 GB RAM), leaving ~21.7 GB spare and running at ~174.3 tok/s (estimated), with room for about 32,768 tokens of context.
Confidence: medium · Recommended quantization: Q8_0 · Estimated speed: ~174.3 tok/s
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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 |
Gemma 3n 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 | 17.6 GB | ✓ Yes | 32K | ~118.4 tok/s | 15.7 GB |
| Q8_0 | 10.3 GB | ✓ Yes | 32K | ~174.3 tok/s | 8.3 GB |
| Q6_K | 8.4 GB | ✓ Yes | 32K | ~198.5 tok/s | 6.4 GB |
| Q5_K_M | 7.5 GB | ✓ Yes | 32K | ~212 tok/s | 5.6 GB |
| Q4_K_M | 6.7 GB | ✓ Yes | 32K | ~226.6 tok/s | 4.7 GB |
| Q3_K_M | 5.3 GB | ✓ Yes | 32K | ~256.3 tok/s | 3.3 GB |
| Q2_K | 4.5 GB | ✓ Yes | 32K | ~276.3 tok/s | 2.6 GB |
Which Gemma 3n sizes fit
| Variant | Needs at Q4_K_M | Fits? | Est. speed |
|---|---|---|---|
| Gemma 3n E4B | 6.7 GB | ✓ Fits | ~226.6 tok/s |
| Gemma 3n E2B | 5.1 GB | ✓ Fits | ~287.5 tok/s |
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 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 inferred from the parameter count because this model does not publish its full attention configuration.
FAQ
Can I run Gemma 3n on RTX 5090 Desktop (32 GB VRAM, 64 GB RAM)?
Yes, comfortably — Gemma 3n E4B at Q8_0 needs about 10.3 GB of the 32 GB usable on RTX 5090 Desktop (32 GB VRAM, 64 GB RAM), leaving ~21.7 GB spare and running at ~174.3 tok/s (estimated), with room for about 32,768 tokens of context.
Which quantization of Gemma 3n should I use on RTX 5090 Desktop (32 GB VRAM, 64 GB RAM)?
Q8_0 — it needs about 10.3 GB of the 32 GB available, downloads as roughly 8.3 GB, and runs at an estimated 174.3 tokens/sec with up to 32K of context.
What limits Gemma 3n 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 Computers
Other Models on RTX 5090 Desktop (32 GB VRAM, 64 GB RAM)
- Gemma 4 on RTX 5090 Desktop (32 GB VRAM, 64 GB RAM)
- GLM-4.7 / GLM-Z1 on RTX 5090 Desktop (32 GB VRAM, 64 GB RAM)
- GLM-5 / GLM-5.1 on RTX 5090 Desktop (32 GB VRAM, 64 GB RAM)
- GLM-6 on RTX 5090 Desktop (32 GB VRAM, 64 GB RAM)
- GPT-OSS on RTX 5090 Desktop (32 GB VRAM, 64 GB RAM)
Gemma 3n on GPUs
- Gemma 3n on NVIDIA GeForce RTX 5060 Ti 8GB
- Gemma 3n on NVIDIA GeForce RTX 5060
- Gemma 3n on NVIDIA GeForce RTX 4060
- Gemma 3n on NVIDIA GeForce RTX 3080 (10GB)