Can I Run Gemma 4 on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM)?
Written by Jakub Rusinowski · Last updated June 3, 2026
Yes, but it is tight
Yes, but it is tight — Gemma 4 12B (Unified) at Q8_0 needs about 14.9 GB of the 16 GB usable on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM), leaving only ~1.1 GB before the runtime starts swapping. Expect ~30.1 tok/s (estimated), with room for about 8,192 tokens of context.
Confidence: low · Recommended quantization: Q8_0 · Estimated speed: ~30.1 tok/s
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RTX 4090 Laptop (16 GB VRAM, 32 GB RAM) — what it gives a model
| Usable memory for models | 16 GB |
| Memory bandwidth | 576 GB/s |
| Form factor | Laptop |
| Operating system | Windows or Linux |
| Memory upgradeable | Yes |
Gemma 4 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 | 26.1 GB | ✗ No | — | — | 24 GB |
| Q8_0 | 14.9 GB | ✓ Yes | 8K | ~30.1 tok/s | 12.8 GB |
| Q6_K | 11.9 GB | ✓ Yes | 32K | ~37.7 tok/s | 9.8 GB |
| Q5_K_M | 10.6 GB | ✓ Yes | 32K | ~42.6 tok/s | 8.5 GB |
| Q4_K_M | 9.4 GB | ✓ Yes | 32K | ~48.7 tok/s | 7.2 GB |
| Q3_K_M | 7.2 GB | ✓ Yes | 32K | ~64.1 tok/s | 5.1 GB |
| Q2_K | 6.1 GB | ✓ Yes | 64K | ~77.6 tok/s | 3.9 GB |
Which Gemma 4 sizes fit
| Variant | Needs at Q4_K_M | Fits? | Est. speed |
|---|---|---|---|
| Gemma 4 31B | 21.3 GB | ✗ Too large | — |
| Gemma 4 26B-A4B | 18.2 GB | ✗ Too large | — |
| Gemma 4 12B (Unified) | 9.4 GB | ✓ Fits | ~48.7 tok/s |
| Gemma 4 E4B | 6.8 GB | ✓ Fits | ~67.8 tok/s |
| Gemma 4 E2B | 4.9 GB | ✓ Fits | ~94.9 tok/s |
What to watch out for
- Only ~1.1 GB of headroom at Q8_0: a longer context or a second application can push this into swapping.
- 2 larger variants of Gemma 4 do not fit and would need CPU offload or different hardware.
- This model does not publish its full attention configuration, so the KV-cache share of these figures is inferred from its parameter count rather than computed exactly.
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 4090 Laptop GPU at 576 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 inferred from the parameter count because this model does not publish its full attention configuration.
FAQ
Can I run Gemma 4 on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM)?
Yes, but it is tight — Gemma 4 12B (Unified) at Q8_0 needs about 14.9 GB of the 16 GB usable on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM), leaving only ~1.1 GB before the runtime starts swapping. Expect ~30.1 tok/s (estimated), with room for about 8,192 tokens of context.
Which quantization of Gemma 4 should I use on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM)?
Q8_0 — it needs about 14.9 GB of the 16 GB available, downloads as roughly 12.8 GB, and runs at an estimated 30.1 tokens/sec with up to 8K of context.
What limits Gemma 4 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
- Gemma 4 on MacBook Pro M4 Max 128 GB
- Gemma 4 on MacBook Pro M4 Max 48 GB
- Gemma 4 on MacBook Pro M4 Pro 24 GB
- Gemma 4 on MacBook Air M4 16 GB
Other Models on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM)
- GLM-4.7 / GLM-Z1 on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM)
- GLM-5 / GLM-5.1 on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM)
- GLM-6 on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM)
- GPT-OSS on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM)
- Granite 3.0 on RTX 4090 Laptop (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