Can I Run Gemma 4 on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM)?
Autor: Jakub Rusinowski · Ostatnia aktualizacja: 3 czerwca 2026
Yes
Yes — Gemma 4 12B (Unified) at Q8_0 needs about 14.2 GB of the 16 GB usable on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM) (~1.8 GB spare), at ~48.2 tok/s (estimated), with room for about 65,536 tokens of context.
Confidence: high · Recommended quantization: Q8_0 · Estimated speed: ~48.2 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 4 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 | 25.4 GB | ✗ No | — | — | 24 GB |
| Q8_0 | 14.2 GB | ✓ Yes | 64K | ~48.2 tok/s | 12.8 GB |
| Q6_K | 11.3 GB | ✓ Yes | 64K | ~59.9 tok/s | 9.8 GB |
| Q5_K_M | 9.9 GB | ✓ Yes | 64K | ~67.3 tok/s | 8.5 GB |
| Q4_K_M | 8.7 GB | ✓ Yes | 64K | ~76.3 tok/s | 7.2 GB |
| Q3_K_M | 6.6 GB | ✓ Yes | 64K | ~98.6 tok/s | 5.1 GB |
| Q2_K | 5.4 GB | ✓ Yes | 64K | ~117.4 tok/s | 3.9 GB |
Which Gemma 4 sizes fit
| Variant | Needs at Q4_K_M | Fits? | Est. speed |
|---|---|---|---|
| Gemma 4 31B | 21.4 GB | ✗ Too large | — |
| Gemma 4 26B-A4B | 17 GB | ✗ Too large | — |
| Gemma 4 12B (Unified) | 8.7 GB | ✓ Fits | ~76.3 tok/s |
| Gemma 4 E4B | 6.8 GB | ✓ Fits | ~102.6 tok/s |
| Gemma 4 E2B | 4.9 GB | ✓ Fits | ~138.5 tok/s |
What to watch out for
- 2 larger variants of Gemma 4 do 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 4 on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM)?
Yes — Gemma 4 12B (Unified) at Q8_0 needs about 14.2 GB of the 16 GB usable on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM) (~1.8 GB spare), at ~48.2 tok/s (estimated), with room for about 65,536 tokens of context.
Which quantization of Gemma 4 should I use on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM)?
Q8_0 — it needs about 14.2 GB of the 16 GB available, downloads as roughly 12.8 GB, and runs at an estimated 48.2 tokens/sec with up to 64K of context.
What limits Gemma 4 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)
- 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)
- Granite 3.0 on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM)
- IBM Granite 4.0 on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM)
Gemma 4 on GPUs
- Gemma 4 on NVIDIA GeForce RTX 5090
- Gemma 4 on NVIDIA GeForce RTX 5080
- Gemma 4 on NVIDIA GeForce RTX 5070 Ti
- Gemma 4 on NVIDIA GeForce RTX 5070