Gemma 3 4B: Hardware & Business Fit
- Vision
- Multilingual
- Long context
Newer Gemma generation with a much longer context than Gemma 2 and multimodal image input. Treat sizes as approximate; verify the exact release before relying on them.
- Parameters
- ~4B
- Context
- ~128K tokens
- Deployment
- local
- VRAM @ 4-bit
- ~3GB
What Gemma 3 4B is good for
- ▸On-device assistant
- ▸Multilingual support
- ▸Light document/image tasks
Best quantization choices
Approximate memory per quantization (weights + KV cache at modest context). Treat as ±.
| Quant | ~Memory | When to use |
|---|---|---|
| Q4_K_M | ~3GB | Best size/quality trade-off — the usual default for local serving. |
| Q8_0 | ~4.5GB | Higher fidelity; ~1.7× the memory of 4-bit. |
| FP16 | ~8GB | Full precision; largest footprint, best quality. |
Run Gemma 3 4B locally
Pull and run with Ollama, or grab the weights from Hugging Face.
$ ollama run gemma3:4bgoogle/gemma-3-4b-itCompatible hardware
Devices from our catalog graded for Gemma 3 4B, best fit first.
- NVIDIA B200 (placeholder)NVIDIA · Datacenter GPUs
Fits at FP16 (~8GB) with ~161GB headroom — about 21 concurrent instances.
FP16 · ~8GBRuns well - Supermicro 8x H100 SuperServerSupermicro · AI Servers
Fits at FP16 (~8GB) with ~555.2GB headroom — about 70 concurrent instances.
FP16 · ~8GBRuns well - Dell PowerEdge XE9680Dell · AI Servers
Fits at FP16 (~8GB) with ~555.2GB headroom — about 70 concurrent instances.
FP16 · ~8GBRuns well - AMD Instinct MI300XAMD · Datacenter GPUs
Fits at FP16 (~8GB) with ~161GB headroom — about 21 concurrent instances.
FP16 · ~8GBRuns well - Cloud B200 (Blackwell profile, to verify)Cloud · Cloud GPU Profiles
Fits at FP16 (~8GB) with ~150.4GB headroom — about 19 concurrent instances.
FP16 · ~8GBRuns well - NVIDIA H200 (141GB)NVIDIA · Datacenter GPUs
Fits at FP16 (~8GB) with ~116.1GB headroom — about 15 concurrent instances.
FP16 · ~8GBRuns well - Cloud H200 141GB (profile)Cloud · Cloud GPU Profiles
Fits at FP16 (~8GB) with ~116.1GB headroom — about 15 concurrent instances.
FP16 · ~8GBRuns well - NVIDIA H100 (80GB)NVIDIA · Datacenter GPUs
Fits at FP16 (~8GB) with ~62.4GB headroom — about 8 concurrent instances.
FP16 · ~8GBRuns well - Cloud H100 80GB (profile)Cloud · Cloud GPU Profiles
Fits at FP16 (~8GB) with ~62.4GB headroom — about 8 concurrent instances.
FP16 · ~8GBRuns well - NVIDIA RTX PRO 6000 BlackwellNVIDIA · Professional GPUs
Fits at FP16 (~8GB) with ~76.5GB headroom — about 10 concurrent instances.
FP16 · ~8GBRuns well
Use inside the AI Business OS
Gemma 3 4B suits these AI Business OS agent archetypes:
A model is only the engine. Inside the AI Business OS it is wrapped with permissions, tools, connectors, RAG and audit so it can actually do business work safely — see how the AI Business OS works →
Frequently asked questions
What hardware do I need to run Gemma 3 4B?+
At 4-bit you need roughly ~3GB of usable memory. The minimum self-hostable option in our catalog is the NVIDIA GeForce RTX 3060 12GB. For a comfortable run we recommend the NVIDIA B200 (placeholder).
Which quantization should I use for Gemma 3 4B?+
Q4_K_M is the usual default — the best size/quality trade-off. Step up to Q8_0 or FP16 if you have spare memory and want higher fidelity.
Should I run Gemma 3 4B locally or in the cloud?+
Local-first is recommended for Gemma 3 4B. It fits comfortably on hardware you can own, keeping data private and costs predictable.
Other sizes in the Gemma 3 family
All Gemma 3 models →Same family, different size. Pick the variant that fits your hardware.
Related models
Similar picks — family siblings and nearest-size models of the same kind.
Use Gemma 3 4B inside your AI Business OS
BrainOutput helps you run Gemma 3 4B as a private business agent — wrapped with the tools, connectors, RAG and guardrails it needs to do real work on hardware you control.
Use this model in your AI Business OS