Gemma 2B Q8 VRAM Requirement and Ollama Model Size
Gemma 2B Q8 needs 8GB minimum VRAM, 18GB optimal VRAM, and 32GB+ system RAM for a safer Ollama run. Popular Ollama model family: Gemma. Caveat: Estimated values are placeholders unless marked measured..
Hardware Snapshot
| Family | Gemma |
|---|---|
| Scenario | chat |
| License scope | open-source |
| Quantization | Q8 |
| VRAM minimum | 8GB |
| VRAM optimal | 18GB |
| 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 | gemma:2b |
| Category | chat |
Benchmark Anchors
| Hardware | Expected tok/s |
|---|---|
| RTX 3090 24GB | 30.2 |
| RTX 4090 24GB | 40.8 |
| A100 80GB | 72.5 |
Quick Answers
- How much VRAM does Gemma 2B Q8 need?
- Gemma 2B Q8 needs about 8GB minimum VRAM and 18GB optimal VRAM for a safer local run target.
- Can Gemma 2B Q8 run on an RTX 3090 24GB?
- Yes. Gemma 2B Q8 fits comfortably on an RTX 3090 24GB target with 18GB optimal VRAM.
- What is the Ollama command for Gemma 2B Q8?
- Use ollama run gemma:2b. Check the Ollama tag gemma:2b before running.
- What is the approximate Ollama model size for Gemma 2B Q8?
- Gemma 2B Q8 uses the gemma:2b Ollama tag. Treat the Q8 2 profile as smaller than the runtime VRAM budget; use 8GB minimum VRAM and 18GB optimal VRAM as the safer sizing numbers.
- How much system RAM does Gemma 2B Q8 need in Ollama?
- Plan for at least 32GB system RAM alongside 8GB minimum VRAM and 18GB 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 2B Q8?
- Gemma 2B Q8 should be sized by GPU VRAM first: 8GB minimum and 18GB optimal. System RAM should be at least 32GB so Ollama has room for CPU-side layers, context, and the surrounding app process.
- What is Gemma 2B Q8 VRAM usage on an RTX 3090?
- Gemma 2B Q8 is comfortable for an RTX 3090 24GB target. The page estimates 8GB minimum and 18GB optimal VRAM, with spill risk marked as moderate at long context.
- Which LocalVRAM profiles share the gemma:2b Ollama tag?
- LocalVRAM tracks these profiles for the same Ollama tag or model family: Gemma 2B Q8 (Q8, 8GB min/18GB optimal); Gemma 2B Q4 (Q4, 2GB min/12GB optimal); Gemma 2B Q5 (Q5, 4GB min/14GB optimal); Gemma 2B 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 2B Q8 benchmark data measured or estimated?
- Gemma 2B Q8 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 | gemma:2b |
|---|---|
| Run command | ollama run gemma:2b |
| Model size guidance | Q8 quantization, 2 profile; size runtime by VRAM, not download size alone |
| Runtime VRAM target | 8GB minimum, 18GB optimal |
| System RAM planning | 32GB 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 | 8GB minimum / 18GB optimal |
|---|---|
| System RAM requirement | 32GB or more for Ollama runtime headroom |
| RTX 3090 24GB fit | Comfortable |
| CPU spill risk | Moderate at long context |
| 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 2B Q8 | Q8 | 8GB min / 18GB optimal | gemma:2b |
| Gemma 2B Q4 | Q4 | 2GB min / 12GB optimal | gemma:2b |
| Gemma 2B Q5 | Q5 | 4GB min / 14GB optimal | gemma:2b |
| Gemma 2B FP16 | FP16 | 14GB min / 26GB optimal | gemma:2b |
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 2B Q8
- Local run: RTX 3090 (24GB) (Check latest deal) for around 30.2 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 2B Q4 2GB min, 12GB optimal
- Gemma 2B Q5 4GB min, 14GB optimal
- Gemma 2B FP16 14GB min, 26GB optimal
ollama run gemma:2b More chat models More tiny-class models Benchmark changelog Submit your test result Check local GPU upgrade We may earn a commission if you click links on this page.
This page currently uses estimated benchmark baselines. Measured data will replace it after validation.