§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
step-by-step recipe for this pair
Gemma 4 E4B on RTX 3060: Multimodal Inference via Q4_K_M GGUF (llama.cpp or Ollama — BF16 will not fit)
multimodalbeginner6GB+
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·how it was measured
- llmQ8_045 tokens/s
Token generation, E4B Q8_0, llama.cpp full GPU offload (danilchenko.dev Hardware Cheat Sheet, 2026-04-07; first-party RTX 3060 12GB row). ~45 t/s.
recorded from danilchenko.dev ↗ · verified 2026-06-14
§04·more Gemma 4 E4B-IT recipes
- multimodalbeginner5GB+
Gemma 4 E4B on Apple M2 Pro: local vision-language inference in 16 GB unified memory with MLX-VLM
- multimodalbeginner5GB+
Gemma 4 E4B on Apple M3 Max: local vision-language inference in unified memory with MLX-VLM
- multimodalbeginner5GB+
Gemma 4 E4B on Apple M4 Max: local vision-language inference in unified memory with MLX-VLM
- multimodalbeginner5GB+
Gemma 4 E4B on Apple M2 Max: local vision-language inference in unified memory with MLX-VLM
§05·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 — a step-by-step recipe documents Gemma 4 E4B-IT on the RTX 3060, linked at the top of this page.