Can I Run Gemma 3 on RTX 4090 Desktop (24 GB VRAM, 64 GB RAM)?
Written by Jakub Rusinowski · Last updated March 12, 2025
Yes — comfortably
Yes, comfortably — Gemma 3 12B Instruct at Q8_0 needs about 16.6 GB of the 24 GB usable on RTX 4090 Desktop (24 GB VRAM, 64 GB RAM), leaving ~7.4 GB spare and running at ~47.3 tok/s (estimated), with room for about 16,384 tokens of context.
Confidence: high · Recommended quantization: Q8_0 · Estimated speed: ~47.3 tok/s
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RTX 4090 Desktop (24 GB VRAM, 64 GB RAM) — what it gives a model
| Usable memory for models | 24 GB |
| Memory bandwidth | 1008 GB/s |
| Form factor | Desktop |
| Operating system | Windows or Linux |
| Memory upgradeable | Yes |
Gemma 3 on RTX 4090 Desktop (24 GB VRAM, 64 GB RAM): memory by quantization
| Quant | Memory needed | Fits 24 GB? | Max context | Est. speed | Download |
|---|---|---|---|---|---|
| F16 | 27.9 GB | ✗ No | — | — | 24 GB |
| Q8_0 | 16.6 GB | ✓ Yes | 16K | ~47.3 tok/s | 12.8 GB |
| Q6_K | 13.7 GB | ✓ Yes | 32K | ~57.8 tok/s | 9.8 GB |
| Q5_K_M | 12.4 GB | ✓ Yes | 32K | ~64.4 tok/s | 8.5 GB |
| Q4_K_M | 11.1 GB | ✓ Yes | 32K | ~72.1 tok/s | 7.2 GB |
| Q3_K_M | 9 GB | ✓ Yes | 32K | ~90.5 tok/s | 5.1 GB |
| Q2_K | 7.8 GB | ✓ Yes | 32K | ~105.2 tok/s | 3.9 GB |
Which Gemma 3 sizes fit
| Variant | Needs at Q4_K_M | Fits? | Est. speed |
|---|---|---|---|
| Gemma 3 27B Instruct | 25.4 GB | ✗ Too large | — |
| Gemma 3 12B Instruct | 11.1 GB | ✓ Fits | ~72.1 tok/s |
| Gemma 3 4B Instruct | 4.4 GB | ✓ Fits | ~161.4 tok/s |
| Gemma 3 1B Instruct | 2 GB | ✓ Fits | ~290.4 tok/s |
What to watch out for
- 1 larger variant of Gemma 3 does not fit and would need CPU offload or different hardware.
RTX 4090 desktop limitations
- 24 GB is the sweet spot for 27–32B models at Q4; 70B needs offload or a second 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 4090 at 1008 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 Gemma 3 on RTX 4090 Desktop (24 GB VRAM, 64 GB RAM)?
Yes, comfortably — Gemma 3 12B Instruct at Q8_0 needs about 16.6 GB of the 24 GB usable on RTX 4090 Desktop (24 GB VRAM, 64 GB RAM), leaving ~7.4 GB spare and running at ~47.3 tok/s (estimated), with room for about 16,384 tokens of context.
Which quantization of Gemma 3 should I use on RTX 4090 Desktop (24 GB VRAM, 64 GB RAM)?
Q8_0 — it needs about 16.6 GB of the 24 GB available, downloads as roughly 12.8 GB, and runs at an estimated 47.3 tokens/sec with up to 16K of context.
What limits Gemma 3 on RTX 4090 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 4090 Desktop (24 GB VRAM, 64 GB RAM)
- Gemma 3n on RTX 4090 Desktop (24 GB VRAM, 64 GB RAM)
- Gemma 4 on RTX 4090 Desktop (24 GB VRAM, 64 GB RAM)
- GLM-4.7 / GLM-Z1 on RTX 4090 Desktop (24 GB VRAM, 64 GB RAM)
- GLM-5 / GLM-5.1 on RTX 4090 Desktop (24 GB VRAM, 64 GB RAM)
- GLM-6 on RTX 4090 Desktop (24 GB VRAM, 64 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