Can I Run Gemma 2 Family on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM)?
Superseded model. Gemma 2 Family has been superseded by Gemma 4. This page is kept for reference; the newer family is a better starting point.
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Written by Jakub Rusinowski · Last updated June 27, 2024
Yes
Yes — Gemma 2 9B IT at Q8_0 needs about 13.2 GB of the 16 GB usable on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM) (~2.8 GB spare), at ~44.2 tok/s (estimated), with room for about 8,192 tokens of context.
Confidence: high · Recommended quantization: Q8_0 · Estimated speed: ~44.2 tok/s
RTX 4090 Laptop (16 GB VRAM, 32 GB RAM) — what it gives a model
| Usable memory for models | 16 GB |
| Memory bandwidth | 717 GB/s |
| Form factor | Laptop |
| Operating system | Windows or Linux |
| Memory upgradeable | Yes |
Gemma 2 Family on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM): memory by quantization
| Quant | Memory needed | Fits 16 GB? | Max context | Est. speed | Download |
|---|
| F16 | 21.6 GB | ✗ No | — | — | 18 GB |
| Q8_0 | 13.2 GB | ✓ Yes | 8K | ~44.2 tok/s | 9.6 GB |
| Q6_K | 11 GB | ✓ Yes | 8K | ~53.8 tok/s | 7.4 GB |
| Q5_K_M | 10 GB | ✓ Yes | 8K | ~59.8 tok/s | 6.4 GB |
| Q4_K_M | 9.1 GB | ✓ Yes | 8K | ~66.8 tok/s | 5.4 GB |
| Q3_K_M | 7.5 GB | ✓ Yes | 8K | ~83.3 tok/s | 3.8 GB |
| Q2_K | 6.6 GB | ✓ Yes | 8K | ~96.4 tok/s | 3 GB |
RTX 4090 laptop limitations
- A mobile RTX 4090 carries 16 GB, not the desktop card's 24 GB, and is closer to a desktop 4080 in throughput — which is why it is modelled against that chip here.
- Sustained throughput depends on the chassis power limit; thin laptops throttle well below the quoted figures.
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 4080 at 717 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 2 Family on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM)?
Yes — Gemma 2 9B IT at Q8_0 needs about 13.2 GB of the 16 GB usable on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM) (~2.8 GB spare), at ~44.2 tok/s (estimated), with room for about 8,192 tokens of context.
Which quantization of Gemma 2 Family should I use on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM)?
Q8_0 — it needs about 13.2 GB of the 16 GB available, downloads as roughly 9.6 GB, and runs at an estimated 44.2 tokens/sec with up to 8K of context.
What limits Gemma 2 Family on RTX 4090 Laptop (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 4090 Laptop (16 GB VRAM, 32 GB RAM)
Gemma 2 Family on GPUs
What This Model Is Good At
Model & Tools
← Can I Run It? | Gemma 2 Family model page | Check your hardware