Can I Run Gemma 2 Family on RTX 4060 Laptop (8 GB VRAM, 16 GB RAM)?
Autor: Jakub Rusinowski · Ostatnia aktualizacja: 27 czerwca 2024
Yes
Yes — Gemma 2 9B IT at Q3_K_M needs about 6.8 GB of the 8 GB usable on RTX 4060 Laptop (8 GB VRAM, 16 GB RAM) (~1.2 GB spare), at ~33.8 tok/s (estimated), with room for about 8,192 tokens of context.
Confidence: high · Recommended quantization: Q3_K_M · Estimated speed: ~33.8 tok/s
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RTX 4060 Laptop (8 GB VRAM, 16 GB RAM) — what it gives a model
| Usable memory for models | 8 GB |
| Memory bandwidth | 256 GB/s |
| Form factor | Laptop |
| Operating system | Windows or Linux |
| Memory upgradeable | Yes |
| Price | $1,099 (lib/data/laptops.ts (street price), checked 2026-07-06) |
Gemma 2 Family on RTX 4060 Laptop (8 GB VRAM, 16 GB RAM): memory by quantization
| Quant | Memory needed | Fits 8 GB? | Max context | Est. speed | Download |
|---|---|---|---|---|---|
| F16 | 21 GB | ✗ No | — | — | 18 GB |
| Q8_0 | 12.6 GB | ✗ No | — | — | 9.6 GB |
| Q6_K | 10.4 GB | ✗ No | — | — | 7.4 GB |
| Q5_K_M | 9.4 GB | ✗ No | — | — | 6.4 GB |
| Q4_K_M | 8.4 GB | ✗ No | — | — | 5.4 GB |
| Q3_K_M | 6.8 GB | ✓ Yes | 8K | ~33.8 tok/s | 3.8 GB |
| Q2_K | 6 GB | ✓ Yes | 8K | ~40 tok/s | 3 GB |
What to watch out for
- Only ~1.2 GB of headroom at Q3_K_M: a longer context or a second application can push this into swapping.
- Q3_K_M is the only quantization that fits, and it is a heavily degraded one — expect noticeably worse output than the same model at Q4_K_M. A smaller model at Q4 is usually the better trade.
- 1 larger variant of Gemma 2 Family does not fit and would need CPU offload or different hardware.
RTX 4060 laptop limitations
- 8 GB VRAM limits you to 7–8B models at Q4 with a short context.
- System RAM is usually upgradeable on this class of laptop even though VRAM is not.
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.
- 8 GB of VRAM on the NVIDIA GeForce RTX 4060 Laptop GPU at 256 GB/s.
- 16 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 4060 Laptop (8 GB VRAM, 16 GB RAM)?
Yes — Gemma 2 9B IT at Q3_K_M needs about 6.8 GB of the 8 GB usable on RTX 4060 Laptop (8 GB VRAM, 16 GB RAM) (~1.2 GB spare), at ~33.8 tok/s (estimated), with room for about 8,192 tokens of context.
Which quantization of Gemma 2 Family should I use on RTX 4060 Laptop (8 GB VRAM, 16 GB RAM)?
Q3_K_M — it needs about 6.8 GB of the 8 GB available, downloads as roughly 3.8 GB, and runs at an estimated 33.8 tokens/sec with up to 8K of context.
What limits Gemma 2 Family on RTX 4060 Laptop (8 GB VRAM, 16 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 2 Family on MacBook Pro M4 Max 128 GB
- Gemma 2 Family on MacBook Pro M4 Max 48 GB
- Gemma 2 Family on MacBook Pro M4 Pro 24 GB
- Gemma 2 Family on MacBook Air M4 16 GB
Other Models on RTX 4060 Laptop (8 GB VRAM, 16 GB RAM)
- Gemma 3 on RTX 4060 Laptop (8 GB VRAM, 16 GB RAM)
- Gemma 3n on RTX 4060 Laptop (8 GB VRAM, 16 GB RAM)
- Gemma 4 on RTX 4060 Laptop (8 GB VRAM, 16 GB RAM)
- GLM-4.7 / GLM-Z1 on RTX 4060 Laptop (8 GB VRAM, 16 GB RAM)
- GLM-5 / GLM-5.1 on RTX 4060 Laptop (8 GB VRAM, 16 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
← Can I Run It? | Gemma 2 Family model page | Check your hardware