Can I Run Gemma 2 Family on RTX 4090 Desktop (24 GB VRAM, 64 GB RAM)?
作者： Jakub Rusinowski · 最后更新： 2024年6月27日
Yes — comfortably
Yes, comfortably — Gemma 2 9B IT at Q8_0 needs about 12.6 GB of the 24 GB usable on RTX 4090 Desktop (24 GB VRAM, 64 GB RAM), leaving ~11.4 GB spare and running at ~59.7 tok/s (estimated), with room for about 8,192 tokens of context.
Confidence: high · Recommended quantization: Q8_0 · Estimated speed: ~59.7 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 2 Family 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 | 21 GB | ✓ Yes | 8K | ~35.8 tok/s | 18 GB |
| Q8_0 | 12.6 GB | ✓ Yes | 8K | ~59.7 tok/s | 9.6 GB |
| Q6_K | 10.4 GB | ✓ Yes | 8K | ~72.1 tok/s | 7.4 GB |
| Q5_K_M | 9.4 GB | ✓ Yes | 8K | ~79.7 tok/s | 6.4 GB |
| Q4_K_M | 8.4 GB | ✓ Yes | 8K | ~88.5 tok/s | 5.4 GB |
| Q3_K_M | 6.8 GB | ✓ Yes | 8K | ~108.9 tok/s | 3.8 GB |
| Q2_K | 6 GB | ✓ Yes | 8K | ~124.6 tok/s | 3 GB |
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 2 Family on RTX 4090 Desktop (24 GB VRAM, 64 GB RAM)?
Yes, comfortably — Gemma 2 9B IT at Q8_0 needs about 12.6 GB of the 24 GB usable on RTX 4090 Desktop (24 GB VRAM, 64 GB RAM), leaving ~11.4 GB spare and running at ~59.7 tok/s (estimated), with room for about 8,192 tokens of context.
Which quantization of Gemma 2 Family should I use on RTX 4090 Desktop (24 GB VRAM, 64 GB RAM)?
Q8_0 — it needs about 12.6 GB of the 24 GB available, downloads as roughly 9.6 GB, and runs at an estimated 59.7 tokens/sec with up to 8K of context.
What limits Gemma 2 Family 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 3 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)
Gemma 2 Family on GPUs
- Gemma 2 Family on NVIDIA GeForce RTX 5070
- Gemma 2 Family on NVIDIA GeForce RTX 5060 Ti 8GB
- Gemma 2 Family on NVIDIA GeForce RTX 5060
- Gemma 2 Family on NVIDIA GeForce RTX 4070 Ti
What This Model Is Good At
Model & Tools
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