Text Generation
Choose Your Gemma-3 Hosting Plans
Basic GPU Dedicated Server - GTX 1650
- 64GB RAM
- Eight-Core Xeon E5-2667v3
- 120GB + 960GB SSD
- 100Mbps-1Gbps
- OS: Windows / Linux
- GPU: Nvidia GeForce GTX 1650
- Microarchitecture: Turing
- CUDA Cores: 896
- GPU Memory: 4GB GDDR5
- FP32 Performance: 3.0 TFLOPS
Advanced GPU Dedicated Server - RTX 3060 Ti
- 128GB RAM
- Dual 12-Core E5-2697v2
- 240GB SSD + 2TB SSD
- 100Mbps-1Gbps
- OS: Windows / Linux
- GPU: GeForce RTX 3060 Ti
- Microarchitecture: Ampere
- CUDA Cores: 4864
- Tensor Cores: 152
- GPU Memory: 8GB GDDR6
- FP32 Performance: 16.2 TFLOPS
Professional GPU VPS - A4000
- 32GB RAM
- 24 CPU Cores
- 320GB SSD
- 300Mbps Unmetered Bandwidth
- Once per 2 Weeks Backup
- OS: Linux / Windows 10/ Windows 11
- Dedicated GPU: Quadro RTX A4000
- CUDA Cores: 6,144
- Tensor Cores: 192
- GPU Memory: 16GB GDDR6
- FP32 Performance: 19.2 TFLOPS
- Available for Rendering, AI/Deep Learning, Data Science, CAD/CGI/DCC.
Advanced GPU Dedicated Server - A5000
- 128GB RAM
- Dual 12-Core E5-2697v2
- 240GB SSD + 2TB SSD
- 100Mbps-1Gbps
- OS: Windows / Linux
- GPU: Nvidia Quadro RTX A5000
- Microarchitecture: Ampere
- CUDA Cores: 8192
- Tensor Cores: 256
- GPU Memory: 24GB GDDR6
- FP32 Performance: 27.8 TFLOPS
Enterprise GPU Dedicated Server - RTX A6000
- 256GB RAM
- Dual 18-Core E5-2697v4
- 240GB SSD + 2TB NVMe + 8TB SATA
- 100Mbps-1Gbps
- OS: Windows / Linux
- GPU: Nvidia Quadro RTX A6000
- Microarchitecture: Ampere
- CUDA Cores: 10,752
- Tensor Cores: 336
- GPU Memory: 48GB GDDR6
- FP32 Performance: 38.71 TFLOPS
- Optimally running AI, deep learning, data visualization, HPC, etc.
Enterprise GPU Dedicated Server - RTX 4090
- 256GB RAM
- Dual 18-Core E5-2697v4
- 240GB SSD + 2TB NVMe + 8TB SATA
- 100Mbps-1Gbps
- OS: Windows / Linux
- GPU: GeForce RTX 4090
- Microarchitecture: Ada Lovelace
- CUDA Cores: 16,384
- Tensor Cores: 512
- GPU Memory: 24 GB GDDR6X
- FP32 Performance: 82.6 TFLOPS
- Perfect for 3D rendering/modeling , CAD/ professional design, video editing, gaming, HPC, AI/deep learning.
Enterprise GPU Dedicated Server - A100
- 256GB RAM
- Dual 18-Core E5-2697v4
- 240GB SSD + 2TB NVMe + 8TB SATA
- 100Mbps-1Gbps
- OS: Windows / Linux
- GPU: Nvidia A100
- Microarchitecture: Ampere
- CUDA Cores: 6912
- Tensor Cores: 432
- GPU Memory: 40GB HBM2
- FP32 Performance: 19.5 TFLOPS
- Good alternativeto A800, H100, H800, L40. Support FP64 precision computation, large-scale inference/AI training/ML.etc
Enterprise GPU Dedicated Server - A100(80GB)
- 256GB RAM
- Dual 18-Core E5-2697v4
- 240GB SSD + 2TB NVMe + 8TB SATA
- 100Mbps-1Gbps
- OS: Windows / Linux
- GPU: Nvidia A100
- Microarchitecture: Ampere
- CUDA Cores: 6912
- Tensor Cores: 432
- GPU Memory: 80GB HBM2e
- FP32 Performance: 19.5 TFLOPS
Gemma-3-27B Benchmark Performance
What is Google Gemma 3 Good For?
Chatbots and Conversational AI
Text Summarization
Language Learning Tools
Natural Language Processing (NLP) Research
Knowledge Exploration
How to Run Gemma 3 LLMs with Ollama
Sample - Run Gemma-3 with Ollama Command line
This model requires Ollama 0.6 or later.
# install Ollama on Linux
curl -fsSL https://ollama.com/install.sh | sh
Text only - 1B parameter model (32k context window)
ollama run gemma3:1b
Multimodal (Vision) - 4B parameter model (128k context window)
ollama run gemma3:4b
12B parameter model (128k context window)
ollama run gemma3:12b
27B parameter model (128k context window)
ollama run gemma3:27b
Note: Here is a table summarizing the key parameters of Google's Gemma 3 models, along with their approximate VRAM requirements when running 4-bit quantized versions (Q4_0) using Ollama:
Model Size | Parameters | Download Size | VRAM Required (4-bit QAT) | Notes |
---|---|---|---|---|
1B | 1 Billion | ~1 GB | 1~ GB | Suitable for low-end GPUs like GTX 1650 4GB. |
4B | 4 Billion | ~4 GB | 4~ GB | Runs efficiently on GPUs with 4–8GB VRAM. |
12B | 12 Billion | ~8.9 GB | 12~ GB | Optimal performance on GPUs with ≥16GB VRAM. |
27B | 27 Billion | ~18 GB | 24~ GB | Requires GPUs with ≥24GB VRAM |