Can I Run Gemma 3 on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM)?
作者： Jakub Rusinowski · 最后更新： 2025年3月12日
Yes, but it is tight
Yes, but it is tight — Gemma 3 12B Instruct at Q8_0 needs about 14.6 GB of the 16 GB usable on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM), leaving only ~1.4 GB before the runtime starts swapping. Expect ~47.6 tok/s (estimated), with room for about 16,384 tokens of context.
Confidence: medium · Recommended quantization: Q8_0 · Estimated speed: ~47.6 tok/s
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RTX 5080 Desktop (16 GB VRAM, 32 GB RAM) — what it gives a model
| Usable memory for models | 16 GB |
| Memory bandwidth | 960 GB/s |
| Form factor | Desktop |
| Operating system | Windows or Linux |
| Memory upgradeable | Yes |
Gemma 3 on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM): memory by quantization
| Quant | Memory needed | Fits 16 GB? | Max context | Est. speed | Download |
|---|---|---|---|---|---|
| F16 | 25.8 GB | ✗ No | — | — | 24 GB |
| Q8_0 | 14.6 GB | ✓ Yes | 16K | ~47.6 tok/s | 12.8 GB |
| Q6_K | 11.7 GB | ✓ Yes | 64K | ~58.9 tok/s | 9.8 GB |
| Q5_K_M | 10.3 GB | ✓ Yes | 64K | ~66.1 tok/s | 8.5 GB |
| Q4_K_M | 9.1 GB | ✓ Yes | 64K | ~74.7 tok/s | 7.2 GB |
| Q3_K_M | 7 GB | ✓ Yes | 128K | ~96 tok/s | 5.1 GB |
| Q2_K | 5.8 GB | ✓ Yes | 128K | ~113.7 tok/s | 3.9 GB |
Which Gemma 3 sizes fit
| Variant | Needs at Q4_K_M | Fits? | Est. speed |
|---|---|---|---|
| Gemma 3 27B Instruct | 18.4 GB | ✗ Too large | — |
| Gemma 3 12B Instruct | 9.1 GB | ✓ Fits | ~74.7 tok/s |
| Gemma 3 4B Instruct | 3.6 GB | ✓ Fits | ~167.5 tok/s |
| Gemma 3 1B Instruct | 1.5 GB | ✓ Fits | ~318.5 tok/s |
What to watch out for
- Only ~1.4 GB of headroom at Q8_0: a longer context or a second application can push this into swapping.
- 1 larger variant of Gemma 3 does not fit and would need CPU offload or different hardware.
RTX 5080 desktop limitations
- 16 GB VRAM is the binding constraint, not compute — a slower 24 GB card runs strictly more models.
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.
- 16 GB of VRAM on the NVIDIA GeForce RTX 5080 at 960 GB/s.
- 32 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 Gemma 3 on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM)?
Yes, but it is tight — Gemma 3 12B Instruct at Q8_0 needs about 14.6 GB of the 16 GB usable on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM), leaving only ~1.4 GB before the runtime starts swapping. Expect ~47.6 tok/s (estimated), with room for about 16,384 tokens of context.
Which quantization of Gemma 3 should I use on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM)?
Q8_0 — it needs about 14.6 GB of the 16 GB available, downloads as roughly 12.8 GB, and runs at an estimated 47.6 tokens/sec with up to 16K of context.
What limits Gemma 3 on RTX 5080 Desktop (16 GB VRAM, 32 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 5080 Desktop (16 GB VRAM, 32 GB RAM)
- Gemma 3n on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM)
- Gemma 4 on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM)
- GLM-4.7 / GLM-Z1 on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM)
- GLM-5 / GLM-5.1 on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM)
- GPT-OSS on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM)
Gemma 3 on GPUs
- Gemma 3 on NVIDIA GeForce RTX 5090
- Gemma 3 on NVIDIA GeForce RTX 5080
- Gemma 3 on NVIDIA GeForce RTX 5070 Ti
- Gemma 3 on NVIDIA GeForce RTX 5070