§01·compatibility · /check
Gemma 4 E4B-IT on RTX 3060
Yes — Gemma 4 E4B-IT runs on the RTX 3060 (12 GB). Fastest community-measured result: 45 tokens/s.
✓ runsmultimodalactive30 series12GB VRAM
model
- name
- Gemma 4 E4B-IT
- slug
- gemma-4-e4b
- vertical
- multimodal
- status
- active
- repo
- huggingface.co ↗
§02·benchmarks
| Task | Quant | Speed | VRAM | Works | Confidence | Source | Verified |
|---|---|---|---|---|---|---|---|
| llm | Q8_0 | 45tokens/s | ✓ | danilchenko.dev· web | 2026-06-14 |
§03·common questions
Can you run Gemma 4 E4B-IT on RTX 3060?
Yes — Gemma 4 E4B-IT runs on the RTX 3060 (12 GB). Fastest community-measured result: 45 tokens/s.
Which quantizations have been tested for Gemma 4 E4B-IT on RTX 3060?
Q8_0 — measured in community benchmarks.
How fast is Gemma 4 E4B-IT on RTX 3060?
Up to 45 tokens/s (llm), the fastest community-measured result.
Are there step-by-step instructions for Gemma 4 E4B-IT on RTX 3060?
Yes — 5 published recipes document this setup; see the recipes listed below.
§04·related recipes
- multimodalbeginner6GB+
Gemma 4 E4B on RTX 3060: Multimodal Inference via Q4_K_M GGUF (llama.cpp or Ollama — BF16 will not fit)
- multimodalbeginner6GB+
Gemma 4 E4B on RTX 4070 Ti: Multimodal Inference via Q4_K_M GGUF (llama.cpp or Ollama — BF16 will not fit)
- multimodalbeginner6GB+
Gemma 4 E4B on RTX 4070 SUPER: Multimodal Inference via Q4_K_M GGUF (llama.cpp or Ollama — BF16 will not fit)
- multimodalbeginner6GB+
Gemma 4 E4B on RTX 4070 Ti SUPER: Multimodal Inference via Q4_K_M GGUF (with optional Q8_0 / BF16)
- multimodalbeginner6GB+
Gemma 4 E4B on RTX 5070: Multimodal Inference via Q4_K_M GGUF (llama.cpp or Ollama — BF16 will not fit)