Gemma Hosting with Ollama — GPU Recommendation
Model Name | Size (4-bit Quantization) | Recommended GPUs | Tokens/s |
---|---|---|---|
gemma3:1b | 815MB | P1000 < GTX1650 < GTX1660 < RTX2060 | 28.90-43.12 |
gemma2:2b | 1.6GB | P1000 < GTX1650 < GTX1660 < RTX2060 | 19.46-38.42 |
gemma3:4b | 3.3GB | GTX1650 < GTX1660 < RTX2060 < T1000 < RTX3060 Ti < RTX4060 < RTX5060 | 28.36-80.96 |
gemma2:9b | 5.4GB | T1000 < RTX3060 Ti < RTX4060 < RTX5060 | 12.83-21.35 |
gemma3n:e2b | 5.6GB | T1000 < RTX3060 Ti < RTX4060 < RTX5060 | 30.26-56.36 |
gemma3n:e4b | 7.5GB | A4000 < A5000 < V100 < RTX4090 | 38.46-70.90 |
gemma3:12b | 8.1GB | A4000 < A5000 < V100 < RTX4090 | 30.01-67.92 |
gemma2:27b | 16GB | A5000 < A6000 < RTX4090 < A100-40gb < H100 = RTX5090 | 28.79-47.33 |
gemma3:27b | 17GB | A5000 < RTX4090 < A100-40gb < H100 = RTX5090 | 28.79-47.33 |
Gemma Hosting with vLLM + Hugging Face — GPU Recommendation
Model Name | Size (16-bit Quantization) | Recommended GPU(s) | Concurrent Requests | Tokens/s |
---|---|---|---|---|
google/gemma-3n-E4B-it google/gemma-3-4b-it | 8.1GB | A4000 < A5000 < V100 < RTX4090 | 50 | 2014.88-7214.10 |
google/gemma-2-9b-it | 18GB | A5000 < A6000 < RTX4090 | 50 | 951.23-1663.13 |
google/gemma-3-12b-it google/gemma-3-12b-it-qat-q4_0-gguf | 23GB | A100-40gb < 2*A100-40gb< H100 | 50 | 477.49-4193.44 |
google/gemma-2-27b-it google/gemma-3-27b-it google/gemma-3-27b-it-qat-q4_0-gguf | 51GB | 2*A100-40gb < A100-80gb < H100 | 50 | 1231.99-1990.61 |
- Recommended GPUs: From left to right, performance from low to high
- Tokens/s: from benchmark data.
Choose The Best GPU Plans for Gemma 3/2 Hosting
- GPU Card Classify :
- GPU Server Price:
- GPU Use Scenario:
- GPU Memory:
- GPU Card Model:
Express GPU Dedicated Server - P1000
- 32GB RAM
- GPU: Nvidia Quadro P1000
- Eight-Core Xeon E5-2690
- 120GB + 960GB SSD
- 100Mbps-1Gbps
- OS: Windows / Linux
- Single GPU Specifications:
- Microarchitecture: Pascal
- CUDA Cores: 640
- GPU Memory: 4GB GDDR5
- FP32 Performance: 1.894 TFLOPS
Basic GPU Dedicated Server - T1000
- 64GB RAM
- GPU: Nvidia Quadro T1000
- Eight-Core Xeon E5-2690
- 120GB + 960GB SSD
- 100Mbps-1Gbps
- OS: Windows / Linux
- Single GPU Specifications:
- Microarchitecture: Turing
- CUDA Cores: 896
- GPU Memory: 8GB GDDR6
- FP32 Performance: 2.5 TFLOPS
Basic GPU Dedicated Server - GTX 1650
- 64GB RAM
- GPU: Nvidia GeForce GTX 1650
- Eight-Core Xeon E5-2667v3
- 120GB + 960GB SSD
- 100Mbps-1Gbps
- OS: Windows / Linux
- Single GPU Specifications:
- Microarchitecture: Turing
- CUDA Cores: 896
- GPU Memory: 4GB GDDR5
- FP32 Performance: 3.0 TFLOPS
Basic GPU Dedicated Server - GTX 1660
- 64GB RAM
- GPU: Nvidia GeForce GTX 1660
- Dual 10-Core Xeon E5-2660v2
- 120GB + 960GB SSD
- 100Mbps-1Gbps
- OS: Windows / Linux
- Single GPU Specifications:
- Microarchitecture: Turing
- CUDA Cores: 1408
- GPU Memory: 6GB GDDR6
- FP32 Performance: 5.0 TFLOPS
Advanced GPU Dedicated Server - V100
- 128GB RAM
- GPU: Nvidia V100
- Dual 12-Core E5-2690v3
- 240GB SSD + 2TB SSD
- 100Mbps-1Gbps
- OS: Windows / Linux
- Single GPU Specifications:
- Microarchitecture: Volta
- CUDA Cores: 5,120
- Tensor Cores: 640
- GPU Memory: 16GB HBM2
- FP32 Performance: 14 TFLOPS
Professional GPU Dedicated Server - RTX 2060
- 128GB RAM
- GPU: Nvidia GeForce RTX 2060
- Dual 10-Core E5-2660v2
- 120GB + 960GB SSD
- 100Mbps-1Gbps
- OS: Windows / Linux
- Single GPU Specifications:
- Microarchitecture: Ampere
- CUDA Cores: 1920
- Tensor Cores: 240
- GPU Memory: 6GB GDDR6
- FP32 Performance: 6.5 TFLOPS
Advanced GPU Dedicated Server - RTX 2060
- 128GB RAM
- GPU: Nvidia GeForce RTX 2060
- Dual 20-Core Gold 6148
- 120GB + 960GB SSD
- 100Mbps-1Gbps
- OS: Windows / Linux
- Single GPU Specifications:
- Microarchitecture: Ampere
- CUDA Cores: 1920
- Tensor Cores: 240
- GPU Memory: 6GB GDDR6
- FP32 Performance: 6.5 TFLOPS
Advanced GPU Dedicated Server - RTX 3060 Ti
- 128GB RAM
- GPU: GeForce RTX 3060 Ti
- Dual 12-Core E5-2697v2
- 240GB SSD + 2TB SSD
- 100Mbps-1Gbps
- OS: Windows / Linux
- Single GPU Specifications:
- 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
Advanced GPU Dedicated Server - A4000
- 128GB RAM
- GPU: Nvidia Quadro RTX A4000
- Dual 12-Core E5-2697v2
- 240GB SSD + 2TB SSD
- 100Mbps-1Gbps
- OS: Windows / Linux
- Single GPU Specifications:
- Microarchitecture: Ampere
- CUDA Cores: 6144
- Tensor Cores: 192
- GPU Memory: 16GB GDDR6
- FP32 Performance: 19.2 TFLOPS
Advanced GPU Dedicated Server - A5000
- 128GB RAM
- GPU: Nvidia Quadro RTX A5000
- Dual 12-Core E5-2697v2
- 240GB SSD + 2TB SSD
- 100Mbps-1Gbps
- OS: Windows / Linux
- Single GPU Specifications:
- Microarchitecture: Ampere
- CUDA Cores: 8192
- Tensor Cores: 256
- GPU Memory: 24GB GDDR6
- FP32 Performance: 27.8 TFLOPS
Enterprise GPU Dedicated Server - A40
- 256GB RAM
- GPU: Nvidia A40
- Dual 18-Core E5-2697v4
- 240GB SSD + 2TB NVMe + 8TB SATA
- 100Mbps-1Gbps
- OS: Windows / Linux
- Single GPU Specifications:
- Microarchitecture: Ampere
- CUDA Cores: 10,752
- Tensor Cores: 336
- GPU Memory: 48GB GDDR6
- FP32 Performance: 37.48 TFLOPS
Basic GPU Dedicated Server - RTX 5060
- 64GB RAM
- GPU: Nvidia GeForce RTX 5060
- 24-Core Platinum 8160
- 120GB SSD + 960GB SSD
- 100Mbps-1Gbps
- OS: Windows / Linux
- Single GPU Specifications:
- Microarchitecture: Blackwell 2.0
- CUDA Cores: 4608
- Tensor Cores: 144
- GPU Memory: 8GB GDDR7
- FP32 Performance: 23.22 TFLOPS
Enterprise GPU Dedicated Server - RTX 5090
- 256GB RAM
- GPU: GeForce RTX 5090
- Dual 18-Core E5-2697v4
- 240GB SSD + 2TB NVMe + 8TB SATA
- 100Mbps-1Gbps
- OS: Windows / Linux
- Single GPU Specifications:
- Microarchitecture: Ada Lovelace
- CUDA Cores: 20,480
- Tensor Cores: 680
- GPU Memory: 32 GB GDDR7
- FP32 Performance: 109.7 TFLOPS
Enterprise GPU Dedicated Server - A100
- 256GB RAM
- GPU: Nvidia A100
- Dual 18-Core E5-2697v4
- 240GB SSD + 2TB NVMe + 8TB SATA
- 100Mbps-1Gbps
- OS: Windows / Linux
- Single GPU Specifications:
- Microarchitecture: Ampere
- CUDA Cores: 6912
- Tensor Cores: 432
- GPU Memory: 40GB HBM2
- FP32 Performance: 19.5 TFLOPS
Enterprise GPU Dedicated Server - A100(80GB)
- 256GB RAM
- GPU: Nvidia A100
- Dual 18-Core E5-2697v4
- 240GB SSD + 2TB NVMe + 8TB SATA
- 100Mbps-1Gbps
- OS: Windows / Linux
- Single GPU Specifications:
- Microarchitecture: Ampere
- CUDA Cores: 6912
- Tensor Cores: 432
- GPU Memory: 80GB HBM2e
- FP32 Performance: 19.5 TFLOPS
Enterprise GPU Dedicated Server - H100
- 256GB RAM
- GPU: Nvidia H100
- Dual 18-Core E5-2697v4
- 240GB SSD + 2TB NVMe + 8TB SATA
- 100Mbps-1Gbps
- OS: Windows / Linux
- Single GPU Specifications:
- Microarchitecture: Hopper
- CUDA Cores: 14,592
- Tensor Cores: 456
- GPU Memory: 80GB HBM2e
- FP32 Performance: 183TFLOPS
Multi-GPU Dedicated Server- 2xRTX 4090
- 256GB RAM
- GPU: 2 x GeForce RTX 4090
- Dual 18-Core E5-2697v4
- 240GB SSD + 2TB NVMe + 8TB SATA
- 1Gbps
- OS: Windows / Linux
- Single GPU Specifications:
- Microarchitecture: Ada Lovelace
- CUDA Cores: 16,384
- Tensor Cores: 512
- GPU Memory: 24 GB GDDR6X
- FP32 Performance: 82.6 TFLOPS
Multi-GPU Dedicated Server- 2xRTX 5090
- 256GB RAM
- GPU: 2 x GeForce RTX 5090
- Dual Gold 6148
- 240GB SSD + 2TB NVMe + 8TB SATA
- 1Gbps
- OS: Windows / Linux
- Single GPU Specifications:
- Microarchitecture: Ada Lovelace
- CUDA Cores: 20,480
- Tensor Cores: 680
- GPU Memory: 32 GB GDDR7
- FP32 Performance: 109.7 TFLOPS
Multi-GPU Dedicated Server - 2xA100
- 256GB RAM
- GPU: Nvidia A100
- Dual 18-Core E5-2697v4
- 240GB SSD + 2TB NVMe + 8TB SATA
- 1Gbps
- OS: Windows / Linux
- Single GPU Specifications:
- Microarchitecture: Ampere
- CUDA Cores: 6912
- Tensor Cores: 432
- GPU Memory: 40GB HBM2
- FP32 Performance: 19.5 TFLOPS
- Free NVLink Included
Multi-GPU Dedicated Server - 2xRTX 3060 Ti
- 128GB RAM
- GPU: 2 x GeForce RTX 3060 Ti
- Dual 12-Core E5-2697v2
- 240GB SSD + 2TB SSD
- 1Gbps
- OS: Windows / Linux
- Single GPU Specifications:
- Microarchitecture: Ampere
- CUDA Cores: 4864
- Tensor Cores: 152
- GPU Memory: 8GB GDDR6
- FP32 Performance: 16.2 TFLOPS
Multi-GPU Dedicated Server - 2xRTX 4060
- 64GB RAM
- GPU: 2 x Nvidia GeForce RTX 4060
- Eight-Core E5-2690
- 120GB SSD + 960GB SSD
- 1Gbps
- OS: Windows / Linux
- Single GPU Specifications:
- Microarchitecture: Ada Lovelace
- CUDA Cores: 3072
- Tensor Cores: 96
- GPU Memory: 8GB GDDR6
- FP32 Performance: 15.11 TFLOPS
Multi-GPU Dedicated Server - 2xRTX A5000
- 128GB RAM
- GPU: 2 x Quadro RTX A5000
- Dual 12-Core E5-2697v2
- 240GB SSD + 2TB SSD
- 1Gbps
- OS: Windows / Linux
- Single GPU Specifications:
- Microarchitecture: Ampere
- CUDA Cores: 8192
- Tensor Cores: 256
- GPU Memory: 24GB GDDR6
- FP32 Performance: 27.8 TFLOPS
Multi-GPU Dedicated Server - 2xRTX A4000
- 128GB RAM
- GPU: 2 x Nvidia RTX A4000
- Dual 12-Core E5-2697v2
- 240GB SSD + 2TB SSD
- 1Gbps
- OS: Windows / Linux
- Single GPU Specifications:
- Microarchitecture: Ampere
- CUDA Cores: 6144
- Tensor Cores: 192
- GPU Memory: 16GB GDDR6
- FP32 Performance: 19.2 TFLOPS
Multi-GPU Dedicated Server - 3xRTX 3060 Ti
- 256GB RAM
- GPU: 3 x GeForce RTX 3060 Ti
- Dual 18-Core E5-2697v4
- 240GB SSD + 2TB NVMe + 8TB SATA
- 1Gbps
- OS: Windows / Linux
- Single GPU Specifications:
- Microarchitecture: Ampere
- CUDA Cores: 4864
- Tensor Cores: 152
- GPU Memory: 8GB GDDR6
- FP32 Performance: 16.2 TFLOPS
Multi-GPU Dedicated Server - 3xV100
- 256GB RAM
- GPU: 3 x Nvidia V100
- Dual 18-Core E5-2697v4
- 240GB SSD + 2TB NVMe + 8TB SATA
- 1Gbps
- OS: Windows / Linux
- Single GPU Specifications:
- Microarchitecture: Volta
- CUDA Cores: 5,120
- Tensor Cores: 640
- GPU Memory: 16GB HBM2
- FP32 Performance: 14 TFLOPS
Multi-GPU Dedicated Server - 3xRTX A5000
- 256GB RAM
- GPU: 3 x Quadro RTX A5000
- Dual 18-Core E5-2697v4
- 240GB SSD + 2TB NVMe + 8TB SATA
- 1Gbps
- OS: Windows / Linux
- Single GPU Specifications:
- Microarchitecture: Ampere
- CUDA Cores: 8192
- Tensor Cores: 256
- GPU Memory: 24GB GDDR6
- FP32 Performance: 27.8 TFLOPS
Multi-GPU Dedicated Server - 3xRTX A6000
- 256GB RAM
- GPU: 3 x Quadro RTX A6000
- Dual 18-Core E5-2697v4
- 240GB SSD + 2TB NVMe + 8TB SATA
- 1Gbps
- OS: Windows / Linux
- Single GPU Specifications:
- Microarchitecture: Ampere
- CUDA Cores: 10,752
- Tensor Cores: 336
- GPU Memory: 48GB GDDR6
- FP32 Performance: 38.71 TFLOPS
Multi-GPU Dedicated Server - 4xA100
- 512GB RAM
- GPU: 4 x Nvidia A100
- Dual 22-Core E5-2699v4
- 240GB SSD + 4TB NVMe + 16TB SATA
- 1Gbps
- OS: Windows / Linux
- Single GPU Specifications:
- Microarchitecture: Ampere
- CUDA Cores: 6912
- Tensor Cores: 432
- GPU Memory: 40GB HBM2
- FP32 Performance: 19.5 TFLOPS
Multi-GPU Dedicated Server - 4xRTX A6000
- 512GB RAM
- GPU: 4 x Quadro RTX A6000
- Dual 22-Core E5-2699v4
- 240GB SSD + 4TB NVMe + 16TB SATA
- 1Gbps
- OS: Windows / Linux
- Single GPU Specifications:
- Microarchitecture: Ampere
- CUDA Cores: 10,752
- Tensor Cores: 336
- GPU Memory: 48GB GDDR6
- FP32 Performance: 38.71 TFLOPS
Multi-GPU Dedicated Server - 8xV100
- 512GB RAM
- GPU: 8 x Nvidia Tesla V100
- Dual 22-Core E5-2699v4
- 240GB SSD + 4TB NVMe + 16TB SATA
- 1Gbps
- OS: Windows / Linux
- Single GPU Specifications:
- Microarchitecture: Volta
- CUDA Cores: 5,120
- Tensor Cores: 640
- GPU Memory: 16GB HBM2
- FP32 Performance: 14 TFLOPS
Multi-GPU Dedicated Server - 8xRTX A6000
- 512GB RAM
- GPU: 8 x Quadro RTX A6000
- Dual 22-Core E5-2699v4
- 240GB SSD + 4TB NVMe + 16TB SATA
- 1Gbps
- OS: Windows / Linux
- Single GPU Specifications:
- Microarchitecture: Ampere
- CUDA Cores: 10,752
- Tensor Cores: 336
- GPU Memory: 48GB GDDR6
- FP32 Performance: 38.71 TFLOPS
What is Gemma Hosting?
Gemma Hosting is the deployment and serving of Google’s Gemma language models (like Gemma 2B and Gemma 7B) on dedicated hardware or cloud infrastructure for various applications such as chatbots, APIs, or research environments.
Gemma is a family of open-source, lightweight large language models (LLMs) released by Google, designed for efficient inference on consumer GPUs and enterprise workloads. They are smaller and more efficient than models like GPT or LLaMA, making them ideal for cost-effective hosting.
LLM Benchmark Results for Gemma 1B/2B/4B/9B/12B/27B Hosting
vLLM Benchmark for Gemma
How to Deploy Gemma LLMs with Ollama/vLLM
Install and Run Gemma Locally with Ollama >
Install and Run Gemma Locally with vLLM v1 >
What Does Gemma Hosting Stack Include?
Hardware Stack
✅ GPU: NVIDIA RTX 3060 / T4 / 4060 (8–12 GB VRAM), NVIDIA RTX 4090 / A100 / H100 (24–80 GB VRAM)
✅ CPU: 4+ cores (Intel/AMD)
✅ RAM: 16–32 GB
✅ Storage: SSD, 50–100 GB free (for model files and logs)
✅ Networking: 1 Gbps for API access (if remote)
✅ Power & Cooling: Efficient PSU & cooling system, Required for stable GPU performance
Software Stack
✅ OS: Ubuntu 20.04 / 22.04 LTS(preferred), or other Linux distros
✅ Driver & CUDA: NVIDIA GPU Drivers + CUDA 11.8+ (depends on inference engine)
✅ Model Runtime: Ollama/vLLM/ Hugging Face Transformers/Text Generation Inference (TGI)
✅ Model Format: Gemma FP16 / INT4 / GGUF (depending on use case and platform)
✅ Containerization: Docker + NVIDIA Container Toolkit (optional but recommended for deployment)
✅ API Framework: FastAPI, Flask, or Node.js-based backend for serving LLM endpoints
✅ Monitoring: Prometheus + Grafana, or basic logging tools
✅ Optional Tools: Nginx (reverse proxy), Redis (cache), JWT/Auth layer for production deployment
Why Gemma Hosting Needs a GPU Hardware + Software Stack
Gemma Models Are GPU-Accelerated by Design
Inference Speed and Latency Optimization
High Memory and Efficient Software Stack Required
Scalability and Production-Ready Deployment
Self-hosted Gemma Hosting vs. Gemma as a Service
Feature | Self-hosted Gemma Hosting | Gemma as a Service (aaS) |
---|---|---|
Deployment Control | Full control over model, infra, scaling & updates | Limited — managed by provider |
Customization | High — optimize models, quantization, backends | Low — predefined settings and APIs |
Performance | Tuned for specific workloads (e.g. vLLM, TensorRT-LLM) | General-purpose, may include usage limits |
Initial Cost | High — GPU server or cluster required | Low — pay-as-you-go pricing |
Recurring Cost | Lower long-term for consistent usage | Can get expensive at scale or high usage |
Latency | Lower (models run locally or in private cloud) | Higher due to shared/public infrastructure |
Security & Compliance | Private data stays in your environment | Depends on provider’s data policies |
Scalability | Manual or automated scaling with Kubernetes, etc. | Automatically scalable (but capped by plan) |
DevOps Effort | High — setup, monitoring, updates | None — fully managed |
Best For | Companies needing full control & optimization | Startups, small teams, quick prototyping |