Running Gemma 4 on RTX 4060
The NVIDIA RTX 4060 with 8GB VRAM is one of the most popular consumer GPUs. While it can't run Gemma 4's larger models at full precision, it handles the E2B and E4B variants excellently and can even run quantized versions of larger models with some offloading.
This guide covers which Gemma 4 models work on the RTX 4060, expected performance numbers, and optimization tips to get the best experience.
Which Models Fit on 8GB VRAM?
Gemma 4 E2B
ExcellentVRAM: ~1.5 GB (INT4) / ~4 GB (FP16)
Runs perfectly with plenty of VRAM headroom. Fast inference at all quantization levels.
Gemma 4 E4B
GreatVRAM: ~3 GB (INT4) / ~8 GB (FP16)
The ideal model for RTX 4060. INT4 leaves room for large context windows. FP16 fits tight but works.
Gemma 4 26B MoE
Partial (offloading)VRAM: ~16 GB (INT4) — exceeds 8GB
Requires CPU offloading. Offload ~50% of layers to CPU. Usable but significantly slower than full GPU.
Gemma 4 31B Dense
Not recommendedVRAM: ~18 GB (INT4) — exceeds 8GB
Too large even at INT4. CPU offloading makes it very slow. Consider the E4B or 26B MoE instead.
Expected Performance on RTX 4060
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| gpuRtx4060Page.performance.headers.model | gpuRtx4060Page.performance.headers.prompt | gpuRtx4060Page.performance.headers.gen |
|---|---|---|
| Gemma 4 E2B (Q4) | ~85 t/s | ~45 t/s |
| Gemma 4 E4B (Q4) | ~55 t/s | ~30 t/s |
| Gemma 4 E4B (Q8) | ~35 t/s | ~20 t/s |
| Gemma 4 27B MoE (Q4) | ~12 t/s | ~8 t/s |
Performance varies by software (Ollama, vLLM, llama.cpp), driver version, and system configuration. Numbers are approximate for interactive use.
Optimal Setup for RTX 4060
Use Ollama or llama.cpp
Both automatically detect and utilize your RTX 4060. Ollama is the simplest option — just 'ollama run gemma4:e4b'.
Stick with INT4 Quantization
INT4 (Q4_K_M) is the sweet spot for 8GB VRAM. It preserves ~93-95% quality while leaving room for context and the KV cache.
Limit Context Length
Use 4096–8192 context length to stay within VRAM. Larger contexts consume memory for the KV cache. Only increase if you have the headroom.
Update NVIDIA Drivers
Ensure you have the latest NVIDIA drivers and CUDA toolkit. Newer drivers often improve inference performance.
RTX 4060 vs Other GPUs for Gemma 4
gpuRtx4060Page.comparison.desc
| gpuRtx4060Page.comparison.headers.gpu | gpuRtx4060Page.comparison.headers.models | gpuRtx4060Page.comparison.headers.notes |
|---|---|---|
| RTX 4060 (8 GB) | E2B, E4B (Q4) | Best value for small models |
| RTX 4060 Ti (16 GB) | E4B (FP16), 27B MoE (Q4) | Sweet spot for most users |
| RTX 4070 (12 GB) | E4B (Q8), 27B MoE (Q4 partial) | Good mid-range option |
| RTX 4080 (16 GB) | 27B MoE (Q4), 31B (Q4 partial) | Handles larger models |
| RTX 4090 (24 GB) | All models up to 31B Q4 | Best consumer GPU |
RTX 4060 + Gemma 4 FAQ
Is RTX 4060 good enough for Gemma 4?
Yes, for the E2B and E4B models. The E4B at INT4 quantization runs excellently on RTX 4060, delivering ~25 tokens/second — more than fast enough for interactive chat.
Can I run the 31B model on RTX 4060?
Not practically. Even at INT4, the 31B model needs ~18GB VRAM. You could use CPU offloading, but inference would be very slow (~2-3 tok/s). The E4B model is a much better choice for this GPU.
RTX 4060 or RTX 4060 Ti for Gemma 4?
The RTX 4060 Ti (16GB) is significantly better — it can run the 26B MoE model at INT4. If you're buying specifically for AI inference, the extra 8GB VRAM is worth the price difference.
What about the RTX 4060 laptop version?
The laptop RTX 4060 also has 8GB VRAM and works the same way. Performance will be slightly lower due to power limits. E4B at INT4 runs well on laptop variants too.
Should I use CPU offloading for larger models?
You can, but expect a significant speed drop (5-10x slower for offloaded layers). It's better to use a model that fits entirely in VRAM. The E4B model is specifically designed for this hardware tier.
How much system RAM do I need alongside the RTX 4060?
16GB system RAM is sufficient for the E4B model. If you want to try CPU offloading with larger models, 32GB+ is recommended.
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Start Running Gemma 4 on Your RTX 4060
Get the E4B model and start chatting. One command is all it takes.