Gemma 3n E2B Q4 VRAM Requirement and Ollama Model Size
Gemma 3n E2B Q4 needs 2GB minimum VRAM, 12GB optimal VRAM, and 16GB+ system RAM for a safer Ollama run. Popular Ollama model family: Gemma 3n. Caveat: Estimated values are placeholders unless marked measured..
Hardware Snapshot
| Family | Gemma 3n |
|---|---|
| Scenario | multimodal |
| License scope | open-source |
| Quantization | Q4 |
| VRAM minimum | 2GB |
| VRAM optimal | 12GB |
| Best local GPU | RTX 3090 24GB |
| Cloud fallback | A6000 48GB |
| Updated | 2026-02-24 |
| Data status | Estimated baseline (pending measurement) |
| Ollama source | Library reference (verified: 2026-02-24) |
| Ollama tag | gemma3n:e2b |
| Category | multimodal |
Benchmark Anchors
| Hardware | Expected tok/s |
|---|---|
| RTX 3090 24GB | 42 |
| RTX 4090 24GB | 56.7 |
| A100 80GB | 100.8 |
Quick Answers
- How much VRAM does Gemma 3n E2B Q4 need?
- Gemma 3n E2B Q4 needs about 2GB minimum VRAM and 12GB optimal VRAM for a safer local run target.
- Can Gemma 3n E2B Q4 run on an RTX 3090 24GB?
- Yes. Gemma 3n E2B Q4 fits comfortably on an RTX 3090 24GB target with 12GB optimal VRAM.
- What is the Ollama command for Gemma 3n E2B Q4?
- Use ollama run gemma3n:e2b. Check the Ollama tag gemma3n:e2b before running.
- What is the approximate Ollama model size for Gemma 3n E2B Q4?
- Gemma 3n E2B Q4 uses the gemma3n:e2b Ollama tag. Treat the Q4 2 profile as smaller than the runtime VRAM budget; use 2GB minimum VRAM and 12GB optimal VRAM as the safer sizing numbers.
- How much system RAM does Gemma 3n E2B Q4 need in Ollama?
- Plan for at least 16GB system RAM alongside 2GB minimum VRAM and 12GB optimal VRAM. More RAM helps if layers spill to CPU or if you run multiple models.
- What is the RAM vs VRAM requirement for Gemma 3n E2B Q4?
- Gemma 3n E2B Q4 should be sized by GPU VRAM first: 2GB minimum and 12GB optimal. System RAM should be at least 16GB so Ollama has room for CPU-side layers, context, and the surrounding app process.
- What is Gemma 3n E2B Q4 VRAM usage on an RTX 3090?
- Gemma 3n E2B Q4 is comfortable for an RTX 3090 24GB target. The page estimates 2GB minimum and 12GB optimal VRAM, with spill risk marked as low.
- Which LocalVRAM profiles share the gemma3n:e2b Ollama tag?
- LocalVRAM tracks these profiles for the same Ollama tag or model family: Gemma 3n E2B Q4 (Q4, 2GB min/12GB optimal); Gemma 3n E2B Q5 (Q5, 4GB min/14GB optimal); Gemma 3n E2B Q8 (Q8, 8GB min/18GB optimal); Gemma 3n E2B FP16 (FP16, 14GB min/26GB optimal). Use the table on this page to compare quantization and VRAM targets before choosing a run command.
- Is Gemma 3n E2B Q4 benchmark data measured or estimated?
- Gemma 3n E2B Q4 currently uses estimated baseline anchors on this page. Verified benchmark data will replace the estimate after a local hardware run is available.
Ollama Model Size and RAM Notes
| Ollama tag | gemma3n:e2b |
|---|---|
| Run command | ollama run gemma3n:e2b |
| Model size guidance | Q4 quantization, 2 profile; size runtime by VRAM, not download size alone |
| Runtime VRAM target | 2GB minimum, 12GB optimal |
| System RAM planning | 16GB or more when running this tag locally with Ollama |
Download size alone is not a safe runtime budget. Runtime memory also includes KV cache, context length, and GPU layer placement.
Ollama RAM vs VRAM Requirements
| GPU VRAM usage estimate | 2GB minimum / 12GB optimal |
|---|---|
| System RAM requirement | 16GB or more for Ollama runtime headroom |
| RTX 3090 24GB fit | Comfortable |
| CPU spill risk | Low |
| Context warning | Longer context increases KV cache memory, so real VRAM usage can exceed short-prompt estimates. |
For Ollama, VRAM is the first fit check. System RAM matters when layers spill to CPU, when context grows, or when the local app stack runs beside the model.
Available Tag Variants and VRAM Targets
| Profile | Quantization | VRAM target | Ollama tag |
|---|---|---|---|
| Gemma 3n E2B Q4 | Q4 | 2GB min / 12GB optimal | gemma3n:e2b |
| Gemma 3n E2B Q5 | Q5 | 4GB min / 14GB optimal | gemma3n:e2b |
| Gemma 3n E2B Q8 | Q8 | 8GB min / 18GB optimal | gemma3n:e2b |
| Gemma 3n E2B FP16 | FP16 | 14GB min / 26GB optimal | gemma3n:e2b |
Ollama library tags often group multiple quantization choices under one model family. LocalVRAM separates them into VRAM profiles so you can pick the right local target.
Real Hardware Benchmark (RTX 3090)
Real benchmark data not available yet for this tag. Estimated anchors are shown above.
Performance Curve
Reference anchors are baseline estimates. Measured RTX 3090 data is overlaid when available.
Best Hardware for Gemma 3n E2B Q4
- Local run: RTX 3090 (24GB) (Check latest deal) for around 42 tok/s on this profile.
- Cloud run: RunPod A6000 48GB , about 2.4x the local 3090 speed anchor.
- Alternative cloud: Vast.ai options for flexible spot pricing.
Local vs Cloud Cost Hint
| Mode | 40h / month | 120h / month |
|---|---|---|
| Local power only (3090 baseline) | $2.24 | $6.72 |
| A6000 48GB | $30.4 | $91.2 |
Related Model Profiles
- Gemma 3n E2B Q5 4GB min, 14GB optimal
- Gemma 3n E2B Q8 8GB min, 18GB optimal
- Gemma 3n E2B FP16 14GB min, 26GB optimal
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This page currently uses estimated benchmark baselines. Measured data will replace it after validation.